intelligence essay conclusion

Intelligence Analysis for Tomorrow: Advances from the Behavioral and Social Sciences (2011)

Chapter: 7 conclusions and recommendations, 7 conclusions and recommendations.

The behavioral and social sciences provide a foundation for the knowledge and continuous learning that the intelligence community needs to provide the highest level of analysis, with applications that can be implemented now with modest cost and minimal disruption.

As the intelligence community (IC) seeks to reduce uncertainty and provide warning about potential threats to the national security of the United States, it faces increasing demands for analyses that are accurate, actionable, and properly qualified, so that decision makers know how much confidence the analyses warrant. Producing those analyses requires great institutional and intellectual agility as threats emerge from new quarters and require different kinds and combinations of expertise.

Today’s rapidly changing conditions have also created new opportunities for data collection, both classified (e.g., electronic surveillance) and open (e.g., chat rooms, public calls to action). Furthermore, after years of limited hiring following the end of the Cold War, the significant influx of new employees to the IC after 9/11 has created a major workforce transition, with new analysts bringing diverse skills, backgrounds, and experiences. In order to fulfill its mission, the IC leadership must successfully train, motivate, and retain that workforce, as well as continue to recruit and select new analysts with needed skills.

The conditions the IC faces involve issues that have been long studied in the behavioral and social sciences, particularly the behavior of individuals and groups and the working conditions that foster effective analysis. Although that work has yielded significant, usable findings, little of that knowledge has found a place in the IC. As a result, there is a large body of scientific theory, method, and results that could—and should—be applied to IC tasks.

The committee concludes that the IC can derive great benefit, in short time and at relatively low cost, by building on available behavioral and

social science knowledge. As a result, the committee’s recommendations focus on strengthening the scientific foundations of the IC’s analytical methods and the organizational processes needed to support them.

The committee recommends that the IC adopt a two-fold strategy to take full advantage of existing behavioral and social science research. First, it should review its current analytic methods and procedures, in terms of how compatible they are with what is known about how people think and work, as individuals and groups. Second, it should conduct systematic empirical evaluations of current and proposed procedures, assessing their efficacy under normal working conditions as much as possible. Those assessments will allow the IC to know how much confidence to place in these procedures and where to focus its efforts on developing improved ones. These evaluations will not only strengthen the evidentiary base of the IC’s analytical work, but also provide the feedback necessary for continuous learning and improvement.

Over time, this strategy will provide a powerful impetus to basic research critical to the IC’s needs. The former head of a major research unit in the United Kingdom has argued that basic science advances through integrated programs of applied basic and basic applied research (Baddeley, 1979). The former tests how well basic research generalizes to different applied settings. The latter identifies new theoretical questions and then translates them into terms suited to basic research (e.g., experiments, modeling).

Such an integrated research strategy will derive the full benefit of the behavioral and social sciences for the IC’s analytical enterprise. In some cases, the resulting research will be on topics unique to the IC, such as the linguistic conventions of violent extremists. In other cases, it will be on general topics that are central to the IC’s needs, such as electronic collaboration among analysts with heterogeneous information.

The committee’s recommendations are designed to deliver maximum improvement with minimal disruption, helping analysts to do their normal work better. We believe that dramatic improvements in the analytic process are possible within existing organizational constraints. We recognize that many people in the IC feel reorganization fatigue, so we propose ways of working more effectively within whatever structure the IC assumes. We also know that all organizations succeed, in part, by allowing their staff to learn how to work around their inevitable imperfections. Achieving such mastery takes time. If an organization changes too rapidly, its staff cannot function effectively. Thus, we emphasize orderly, measured improvements.

Because they build on existing technologies and organizational structures, our recommendations should not be expensive to implement. They do require both deeply knowledgeable scientists and strong leaders. The scientists will need to know the existing research and ensure its faithful application to the IC’s circumstances. The leaders will need to ensure that

enhancing the IC’s human capital is seen as central to the IC’s success and, therefore, to the nation’s security.

Intelligence analysis is, at its heart, an intensely individual intellectual effort, as analysts synthesize facts of diverse origins. As a result, one focus of our report and recommendations is research regarding how individuals think. However, individual analysts do not operate in a vacuum; they have to collaborate with other analysts. As a result, a second focus of our report and recommendations is the support that they need from their organizations. We thus recommend asking the same questions about collaboration, workforce development, communication, and analytical methods:

What does the science say about current and proposed methods?

How do those methods fare when evaluated under the IC’s conditions?

HISTORIC CONTEXT

The committee’s study has determined that there is knowledge from the behavioral and social sciences that is ready for application within the IC. That claim invites an explanation of why the opportunity exists. We believe that it reflects properties of the behavioral and social sciences and of the IC.

During much of its life, the IC has been intensely concerned with questions of military materiel, standing armies, and large-scale weapons. Its behavioral foci have been fairly narrow, such as the notoriously difficult task of reading leaders’ intentions and the somewhat more tractable tasks of interpreting national and international politics. As a result, the IC has developed little internal expertise on many behavioral and social science issues. Indeed, the IC has so little expertise in some areas that it sometimes struggles to recruit needed scientists, although efforts like its IC Associates Program can provide partial solutions. The computationally intensive demands of many IC analyses have also contributed to its paying relatively little attention to the human side of the analytical enterprise.

For its part, the behavioral and social science community has been a distant, sometimes reluctant partner for the IC. Its science has often involved controlled experiments that foster the discovery of basic behavioral principles, while discouraging study of applications. Social conflicts in the second half of the 20th century have also distanced the academic and intelligence communities from one another in the United States. Fortunately, these barriers have fallen with the rise in national unity following the 9/11 attacks.

A noteworthy exception to this historic pattern has been the landmark work of Richards Heuer, Jr., whose Psychology of Intelligence Analysis

(1999) demonstrates the relevance of behavioral research to the work of the IC. Equally remarkable has been the success of Heuer and his associates in getting structured analytical techniques (SATs), based on behavioral research, accepted in the IC and even having versions of SATs installed on IC computer systems. However, the IC has not pursued this effort through to the point of performing systematic empirical evaluation of SATs. There are theoretical reasons for predicting both that SATs improve analysis and that they interfere with it. Without empirical evaluation, one can only speculate about when improvement or interference will dominate under different conditions and analytic questions.

A BEHAVIORAL AND SOCIAL SCIENCES FOUNDATION

Traditionally, the IC has adopted a practice-based approach to analysis. It has relied primarily on apprenticeship-like processes to train new analysts in methods that have evolved almost exclusively through intensive attempts to learn from experience. We propose a complementary commitment to evidence-based analysis, drawing on the behavioral and social sciences to evaluate current and create new approaches to analysis, collaboration, workforce development, and communication. Such evidence-based analysis should be used both to examine existing and proposed approaches, in order to determine their compatibility with the science, and to study their actual performance empirically, under normal working conditions.

Although this recommendation reflects Enterprise Objectives 2 and 5 of the National Intelligence Strategy (Office of the Director of National Intelligence, 2009; see Box 1-1 in Chapter 1 ), conducting such evaluations is a brave step for any organization, because the evidence needed for internal learning can also be used for external criticism. It is, therefore, critical that the Director of National Intelligence (DNI) be the sponsor for the initiative and the audience for its results, in order to demonstrate commitment, at the highest level, to evidence-based analytical methods.

Evaluation research is methodologically demanding. Poor evaluations can undermine good initiatives (e.g., by failing to recognize that they have been poorly implemented) or promote poor ones (e.g., by subjecting them to soft tests). Poor evaluations can even undermine performance (e.g., if paperwork requirements dominate analytical accuracy). A central task in evaluation research is assessing how well a program has been implemented. Unless a program has been properly implemented, it will not receive a fair test (although if a program cannot be implemented faithfully, it has little value).

In order to meet these commitments, the IC needs staff qualified to identify, implement, and evaluate the best opportunities for improving its analytical processes. To that end, we propose designating a senior officer,

reporting to the DNI and supported by an independent advisory panel of behavioral and social scientists with strong basic and applied credentials. Such individuals are in short supply. The IC’s ability to recruit and retain expert advisors will provide a measure of its success in strengthening the scientific base of its analyses.

Recommendation 1

The Director of National Intelligence should ensure that the intel ligence community applies the principles, evidentiary standards, and findings of the behavioral and social sciences to its analytic methods, workforce development, collaborations, and communications. Success will require strong leadership, active engagement with the academic community, and the creation of a robust reporting mechanism (such as a biennial report from each agency) to identify residual problems and plans to remedy them. The Director of National Intelligence should be supported by a senior officer and an independent advisory committee with appropriate scientific expertise.

Immediate Actions

Use the Intergovernmental Personnel Act to embed independent experts in the IC for limited terms.

Embed IC analysts in academic research environments to participate in research and to network with scientists who can be consulted later.

Develop specialized behavioral and social science expertise cells across the IC, coordinated through the Office of the Director of National Intelligence (ODNI).

Ensure that the IC Associates Program actively uses behavioral and social science expertise.

Create and widely disseminate an Analytical Methods Resource Guide that introduces key methods, shows how to choose methods suited to specific intelligence questions, and identifies experts who can apply each method, from inside and outside the IC.

ANALYTIC METHODS

The conditions that support learning are among the best understood aspects of human behavior. Those conditions include large quantities of unambiguous feedback, with properly aligned incentives. Achieving these conditions is consistent with Enterprise Objectives 5 and 7 of the National Intelligence Strategy (Office of the Director of National Intelligence,

2009; see Box 1-1 in Chapter 1 ), as well as with the IC’s tradition of lessons-learned and after-action reports and with the voluminous literature produced by government commissions, former intelligence officers, and academic researchers (see Chapter 2 ).

Unambiguous feedback requires predictions that can be evaluated in light of subsequent history. A straightforward and necessary step is attaching numeric probabilities to explicitly defined events (e.g., “There is a 75 percent chance that country A has a stockpile of biological weapons”; “There is a 90 percent chance of X being in power at year’s end”). Significant amounts of feedback are needed to provide stable performance measures. In order to create such feedback, we recommend compiling a database of assessments and predictions, indexed by properties that might affect their quality (e.g., the analysts’ background and analytical method), and further annotating the analyses archived in the Library of National Intelligence. Doing so would also facilitate research on confounding factors (such as self-fulfilling and self-defeating prophesies) whereby analyses lead to (political or military) actions that change the world, so that it is no longer possible to evaluate their accuracy.

We recognize that there has historically been resistance to numeric probability estimates from analysts who believe that they imply artificial precision. However, as discussed in Chapter 2 , the scientific evidence, including Canada’s real-world success with numeric probabilities in intelligence analysis (Mandel, 2009), suggest that, with proper training and feedback, such judgments could substantially improve analytic products and customer understanding of them. Proper incentives seek to encourage learning, not to determine culpability. They reward positive performance and cultivate the natural desire to do well, a desire that is especially prevalent in the IC. In addition, numeric probabilities allow feedback that is essential to learning. Proper incentives discourage both overconfidence (intended perhaps to carry an argument) and underconfidence (intended perhaps to avoid responsibility). They encourage good calibration: being as confident as one’s understanding warrants. Thus the DNI must ensure that numeric probabilities are implemented in a constructive way, using them for useful feedback, not destructive criticism.

Recommendation 2

The Director of National Intelligence should ensure that the intel ligence community adopts scientifically validated analytical methods and subjects all of its methods to performance evaluation. To that end, each analytical product should report, in a standardized format, the elements necessary for such evaluation, including its analytical method, domain, conclusions, analysts’ background, and the collaborations that

produced it. Analyses must include quantitative judgments of the prob ability and uncertainty of the events that they forecast. These reports should be archived in a database that is routinely used to promote insti tutional learning and individual training and as input to the Director of National Intelligence’s ongoing review efforts of analytic shortfalls and plans to address them.

Institutionalize an “Analytical Olympics,” with analysts and analytical methods competing to provide the best calibrated probabilities (i.e., showing appropriate levels of confidence) in assessments and predictions made for well-specified outcomes that have occurred or will occur in the near future.

Begin assessing how well-calibrated individual analysts are, using the results as personal feedback that will allow analysts to improve their own performance and the IC to learn how this performance is related to workforce factors, such as personal capabilities, training, and incentives.

Create a research program that reviews current and historic assessments, looking for correlates of accuracy and calibration, considers properties such as the method used, collaboration process, classification level, substantive domain, and team composition.

WORKFORCE DEVELOPMENT

The quality of the human resource pool places greater constraints on an organization’s human capital than any other single factor. It is the focus of Enterprise Objective 6 of the National Intelligence Strategy (Office of the Director of National Intelligence, 2009; see Box 1-1 in Chapter 1 ). Currently, the IC typically recruits analysts on the basis of their substantive expertise and rewards them on the basis of process-based performance (e.g., workflow). Research finds that both practices are inadequate by themselves. We recommend a systematic review of the theoretical soundness of current practices, followed by empirical evaluation of the efficacy of current practices and alternative ones.

Clearly, the IC needs analysts with deep substantive knowledge of countries, cultures, transnational relations, and myriad other issues. However, it also needs analysts capable of integrating knowledge across domains, working with experts from other fields, and coping with shifting assignments. As a result, the IC needs analysts with both the intellectual capacity for synthetic thinking and substantive familiarity with the full range of analytical methods. The former is a stable individual trait, which must

be pursued in the IC’s recruitment and selection processes. The latter is a malleable individual skill that can be acquired through training. The goal of such training is not mastery of alternative methods; rather, the goal is enough familiarity to recognize different kinds of problems and to work with others who have technical mastery of the methods.

Thus, every analyst should have a basic understanding of the fundamental ways of thinking captured by probability theory, game theory, operations research, qualitative analysis, and other analytic methods (see Chapter 3 ). Each method provides a different way to look at the world and organize data. Each has been refined through decades (even centuries) of rigorous peer review and has well-understood strengths and limitations. Making them part of IC analysts’ basic intellectual repertoire will increase analysts’ ability to address their customers’ needs.

Recommendation 3

The Director of National Intelligence should ensure that intelligence community agencies use evidence-based methods to recruit, select, train, motivate, and retain an adaptive workforce able to achieve the performance levels required by intelligence community missions. On the basis of that research:

The intelligence community should recruit and select individuals who have the stable individual attributes (e.g., cognitive ability, personality, values) known through research to be associated with better performance.

The intelligence community’s training, motivation, and per formance feedback should focus on improving malleable indi vidual attributes (e.g., job-specific skills) associated with better performance.

The intelligence community should expand opportunities for continuous learning that will enhance collaboration, innova tion, and growth in the application of analytical skills.

Create a course to provide all IC analysts with basic familiarity with the full range of analytical methods with strong scientific foundations (e.g., probability theory, statistics, game theory, qualitative analysis).

Create an inventory of psychometrically validated measures of intellectual ability that can be administered to current and pro-

spective analysts, in order to study which abilities are related to analytical performance.

Convene an independent working group of human resource scientists to review current recruitment, selection, motivation, and retention practices in light of the relevant behavioral and social science.

Develop on-the-job training programs to cultivate a culture of continuous learning, whereby the entire workforce is actively involved as both teachers and students.

COLLABORATION

Recognizing that essential information is often scattered across individuals and units, the IC has made collaboration central to its current efforts. The need for collaboration is recognized in Enterprise Objectives 1, 2, and 4 of the National Intelligence Strategy (Office of the Director of National Intelligence, 2009; see Box 1-1 in Chapter 1 ) and has been the motivation for such innovations as A-Space, Intellipedia, the Analytical Resources Catalogue (ARC), the Library of National Intelligence, and joint IC duty positions (see Intelligence Community Directive 601 [Office of the Director of National Intelligence, 2006]). All of these innovations are intended to familiarize intelligence officers with a wide variety of intelligence requirements, methods, users, and capabilities (see Intelligence Reform and Terrorism Prevention Act of 2004, P.L. 108-458). These innovations seek to create the agility needed to cope with adversaries who have rapidly shifting identities and operations.

Behavioral and social science findings provide reason to believe that these innovations do, in fact, promote the collaboration that the IC seeks. They are flexible, allowing analysts to adapt them to their own purposes. They are open, allowing analysts to create self-organizing groups that are adapted to specific tasks. They are complementary, allowing analysts to choose the methods best suited to their needs. At the same time, however, those behavioral and social science findings also provide reasons to question the efficacy of these innovations. For example, these methods can be time consuming and provide information from unfamiliar sources, with uncertain quality. Given these contradictory possibilities, we recommend that the IC undertake systematic empirical study of these “natural experiments,” assessing the impacts of the various methods for different uses and users. Although not expensive, such evaluations require methodological sophistication in order to create fair tests and to provide useful guidance on how the innovations could be improved.

Recommendation 4

The Director of National Intelligence should require systematic empiri cal evaluation of current and proposed procedures for enhancing the collaboration that is essential to fulfilling the intelligence community’s mission. That evaluation should be based on scientific principles that prescribe the extent and form of collaborative methods for effective performance under the intelligence community’s operating conditions. This approach will require ongoing innovation, evaluation, and learn ing about collaborative methods.

Conduct field evaluations of at least two collaborative methods, assessing their uses, users, and impacts. Create and implement an evaluation methodology that can then be used more broadly.

Collaborative aids like A-Space should be subjected to rigorous evaluation of what they do well and poorly. That evaluation should examine the possibility of enhancing A-Space with programs that prompt collaboration between analysts who are working on related problems, but are unaware of their mutual interests.

Develop a database, or modify the Library of National Intelligence, to characterize analyses in terms of features that might be related to their effectiveness, such as the methods used, the contacts consulted, and the collaborations undertaken.

COMMUNICATION

Effective communication is essential to ensuring that analysts understand their customers’ information needs and that their customers understand the analysts’ conclusions and confidence levels. These needs are recognized in Enterprise Objective 2 of the National Intelligence Strategy (Office of the Director of National Intelligence, 2009; see Box 1-1 in Chapter 1 ). However, there are many potential barriers to effective communication, including the natural tendency to exaggerate how well one communicates, the frequent lack of direct contact between analysts and their customers, and the many steps in the IC review, coordination, and approval process, each capable of improving or degrading how well the resulting report conveys the original analysts’ intent.

The clarity demanded by the evaluation processes that the committee proposes in Recommendation 2 will provide a foundation for better communication, by requiring analysts to be explicit about their terms, predictions, uncertainty, rationale, and conditions of validity. Standardization

will facilitate creating communication protocols that convey the intended meaning of the analyses to customers, as well as permitting the elicitation of customers’ needs in clear terms. These kinds of protocols can build on the science of communication and use its methods to evaluate analysts’ success in establishing the needed situational awareness. Over time, a disciplined approach to communication will make analysts and customers more sophisticated about one another’s worlds, improving the collaboration between them.

Recommendation 5

The Director of National Intelligence should implement scientific, evidence-based protocols for ensuring that analysts and customers understand one another. Achieving this goal will require standard pro tocols for communicating the uncertainties and limitations of analyses, expanded opportunities for analysts to learn about customers’ needs, and feedback evaluating the usefulness and presentation of analyses.

Develop and evaluate standard protocols for communicating the confidence that should be placed in analytic judgments (following Recommendation 2).

Evaluate the efficacy of current methods for requesting analyses in terms of how well they convey customers’ intentions to analysts.

Evaluate the impact of internal review processes on how well the resulting reports convey analysts’ intending meaning.

The IC has recently undergone its most sweeping structural changes since 1947, including the creation of the ODNI. The IC is also undergoing a demographic transition, with new analysts bringing different backgrounds and capabilities into the community. At the same time, new technologies offer new capabilities for data gathering, data sharing, and collaboration which might aid or distract analysts. The IC has received additional resources, along with growing public awareness of its importance.

Taking full advantage of these opportunities will require carefully planned strategies. New analysts must be trained in tradecraft, rewarded for high-quality performance, and provided access to veteran analysts’ wisdom and tacit knowledge. New methods and technologies have to be designed with analysts in mind, subjected to rigorous evaluation, and kept from interfering with normal individual and collective thought processes.

Even if the world were static, complex activities that involve people, like intelligence analysis, will never be perfect. As a result, the IC must continually evaluate its own performance, both to learn from its experience and to provide policy makers with realistic expectations of its capabilities.

The committee’s recommendations are interdependent. Without appropriate human resource policies, analysts cannot create the communication networks needed to share information. Without regularly updating their theoretical knowledge, analysts cannot take advantage of the evidence and information sources available to them. Without sound, informative performance evaluation, no one can know how well any methods are working.

Any change involves a gamble, sacrificing the relative stability of current practices in return for the promise of improved future performance. Change is necessary today, because traditional analytical methods and institutional arrangements are increasingly challenged by the demands on IC analysis. Pursuing such disciplined, evidence-based change will require strong leadership. Analysts need to know that their organization will support them if they innovate and if they rigorously evaluate their own performance.

Strong leadership is needed to acknowledge that intelligence analysis is inherently imperfect, then to create realistic standards of accountability, demanding the best feasible performance. Leadership is needed to recognize that even the best systems lose their efficacy if the world changes faster than they do. That leadership must be manifested externally, by subjecting performance to well-designed tests and rejecting unfair ones; it must be manifested internally, by showing that “evaluation” denotes learning and not fixing blame. The behavioral and social sciences provide a foundation for taking the best possible gambles, regarding analytical and management processes, then objectively evaluating their success.

Baddeley, A.D. (1979). Applied cognitive and cognitive-applied psychology: The case of face recognition. In L.G. Nilsson, ed., Perspectives on Memory Research (pp. 367-388). Hillsdale, NJ: Lawrence Erlbaum.

Fischhoff, B. (2008). Assessing adolescent decision-making competence. Developmental Review, 28 , 12-28.

Heuer, R.J., Jr. (1999). Psychology of Intelligence Analysis . Washington, DC: Center for the Study of Intelligence, Central Intelligence Agency.

Mandel, D. (2009). Canadian Perspectives: Applied Behavioral Science in Support of Intelligence Analysis. Paper presented at the meeting of the Committee on Behavioral and Social Science Research to Improve Intelligence Analyses for National Security, Washington, DC. May 14. Available: http://individual.utoronto.ca/mandel/nas2009.pdf [October 2010].

Office of the Director of National Intelligence. (2006). Human Capital Joint Intelligence Community Duty Assignments. Intelligence Community Directive Number 601 (Effective: May 16, 2006). Available: http://www.dni.gov/electronic_reading_room/ICD_601.pdf [April 2010].

Office of the Director of National Intelligence. (2009). National Intelligence Strategy of the United States of America . Available: http://www.dni.gov/reports/2009_NIS.pdf [June 2010].

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The intelligence community (IC) plays an essential role in the national security of the United States. Decision makers rely on IC analyses and predictions to reduce uncertainty and to provide warnings about everything from international diplomatic relations to overseas conflicts. In today's complex and rapidly changing world, it is more important than ever that analytic products be accurate and timely. Recognizing that need, the IC has been actively seeking ways to improve its performance and expand its capabilities.

In 2008, the Office of the Director of National Intelligence (ODNI) asked the National Research Council (NRC) to establish a committee to synthesize and assess evidence from the behavioral and social sciences relevant to analytic methods and their potential application for the U.S. intelligence community. In Intelligence Analysis for Tomorrow: Advances from the Behavioral and Social Sciences , the NRC offers the Director of National Intelligence (DNI) recommendations to address many of the IC's challenges.

Intelligence Analysis for Tomorrow asserts that one of the most important things that the IC can learn from the behavioral and social sciences is how to characterize and evaluate its analytic assumptions, methods, technologies, and management practices. Behavioral and social scientific knowledge can help the IC to understand and improve all phases of the analytic cycle: how to recruit, select, train, and motivate analysts; how to master and deploy the most suitable analytic methods; how to organize the day-to-day work of analysts, as individuals and teams; and how to communicate with its customers.

The report makes five broad recommendations which offer practical ways to apply the behavioral and social sciences, which will bring the IC substantial immediate and longer-term benefits with modest costs and minimal disruption.

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Intelligence Essay | Importance of Intelligence, Benefits and Strength of Intelligence

October 14, 2021 by Prasanna

Intelligence Essay: Intelligence is perceived as the capacity to obtain information, to think and give reason successfully and to manage the climate. This intellectual ability helps him in the errand of hypothetical just as commonsense control of things, items or occasions present in his current circumstance to adjust or confront new difficulties and issues in life as effectively as could really be expected.

Intelligence gets from the capacity to learn and use what has been realized in acclimating to new circumstances and tackling new issues. The idea of Intelligence owes a lot to early investigations of creature learning. Intelligence is the ability to comprehend the world, think judiciously, and use assets viably when confronted with difficulties.

You can also find more  Essay Writing  articles on events, persons, sports, technology and many more.

Sample Essay on Intelligence 800 Words in English

Introduction

Intelligence is the thing that you use when you don’t have the foggiest idea of what to do. The individual is viewed as the most Intelligenceful creature in this world. He is equipped for controlling any remaining creatures and numerous different things in this world. The word Intelligence has been gotten from a Latin action word ‘intellegere’ signifies to comprehend.

Intelligence plays a vital part in the everyday exercises of the person. Intelligence addresses a point of convergence for therapists, they expect to see how individuals can embrace their conduct to the climate where they reside. It additionally addresses a critical part of how people contrast from each other in the manner by which they find out about and comprehend the world. Mental tests are utilized to quantify individual contrasts that exist among individuals in capacities, aptitudes, interests and part of the character.

“Intelligence is the total or worldwide limit of a person to think objectively, to act intentionally and to manage his current circumstance”.

This definition incorporates three significant cycles, viz., to act deliberately implies, to act not set in stone way with no equivocalness, to think fittingly in a judicious way with practically no biases and to manage the climate or to change in an appropriate manner with the climate.

Importance of Intelligence

Man is particularly not quite the same as the lower types of creatures as a result of his capacity in controlling the climate he lives in. The differentiation among man and different creatures additionally springs from his fruitful transformation to his natural requests. The creatures can, best case scenario, departure to wellbeing, secure their lives, may assemble homes as their asylum, can move to a far off land, yet can’t overcome nature. The creatures barely can brilliantly adjust to any negative turbulent climate.

Despite what might be expected, man can reproduce the world, make solaces for him with the assistance of logical contraptions, climb the high scopes of mountains, attack the profundity of the oceans and air, travel with huge speed, and can broaden the life expectancy of its species by designing solutions for a few deadly infections. His scholarly abilities place him as the most prevalent species in the animals of the world collectively. Subsequently, basic all human capacities lie the fundamental ascribes of Intelligence.

Intelligence is a famous term alluding to all types of man’s mind-boggling mental capacities. Intelligence as a term alludes to the capacity to get, act, decipher, and foresee the future, and to accomplish and deal with connections, data, ideas, and conceptual images. Intelligence is accordingly a normally utilized word to communicate all-inclusive limit needed for endurance and progress past the present.

Intelligence is a course of comprehension. “Cognizance alludes to how we obtain, store, recover, and use information”. Every one of the essential mental cycles like learning, Intelligence, memory, idea development, thinking, thinking, critical thinking, dynamic, and imagination are identified with Intelligence.

Intelligence conduct incorporates all types of intellectual conduct like joining in, seeing, getting the hang of, remembering, thinking and foreseeing. Intelligence is a theoretical idea. It can’t be noticed bearing it very well may be assessed distinctly through person’s presentation on tests and relife circumstances. As of late, the idea of Intelligence has been expanded to incorporate such terms as “enthusiastic Intelligence”, “otherworldly Intelligence “down to earth Intelligence”, “Social Intelligence”, “professional Intelligence” and “melodic Intelligence”.

Short Essay on Intelligence is Strength 400 Words in English

Many would say characterizing Intelligence is handily done, or that Intelligence is unmistakably the capacity to secure and apply information and abilities. Be that as it may, there is something beyond one sort of Intelligence in individuals. Only one out of every odd human has a similar point of view and capacities. Each individual has their own arrangement of remarkable capacities and gifts that can’t be characterized and caught in one sentence. Intelligence is characterized by individuals, and by their activities, speculations, convictions, and advancements. Many have looked to characterize Intelligence, which is the reason we are left with various hypotheses of what Intelligence really is.

Maybe, there are two kinds of Intelligence that sort various gifts and capacities moved by people. Asimov accepts there are two sorts of Intelligence. He starts to clarify how certain individuals have the ability of remembrance and basic reasoning. Others have minds that can envision something and make or fix it to its wonderful design.

Each side of the mind controls two distinct sorts of reasoning. The first is the right side, which is utilized for inventive reasoning. The second is the left this is utilized for coherent reasoning. It has been demonstrated that small kids tackle additional force from the innovative side. Since schools for the most part instruct towards consistent thinking as kids become more seasoned the greater part of the populace utilizes intelligent reasoning.

This immensely affects human Intelligence. This shows that individuals can be similarly Intelligenceful however their Intelligence can lay in various regions alongside the possibility that Intelligence is something not fixed yet can be expanded. Many would contend that abilities and Intelligence are not exactly the same thing. In any case, the ability is only an inclination to be effective in a specific endeavour. This definition additionally clarifies the facilitate some have with math and phonetics; accordingly, it is consistent to say that regions usually considered as abilities have similarly as much to do with Intelligence as regions all the more normally saw as “really Intelligence.”

Frequently individuals fail to remember that Intelligence isn’t restricted to rationale, math, and etymology. Intelligence extends to the makers, trend-setters, and menders just as the legitimate scholars. Each individual understands things in an unexpected way. This doesn’t make one human keener than the other. They basically have dominated diverse savvy abilities. Maybe, the smartest individual would be one who can utilize the left and right half of their cerebrum to every one of their greatest abilities.

FAQ’s on Intelligence Essay

Question 1. What is the true meaning of intelligence?

Answer: Intelligence is the capacity to learn or comprehend or to manage new or attempting circumstances. It is the capacity to apply information to control one’s current circumstances or to think uniquely as estimated by target standards.

Question 2. What is the importance of intelligence?

Answer: Intelligence is the capacity to think, to gain, as a matter of fact, to take care of issues, and to adjust to new circumstances. Intelligence is significant on the grounds that it affects numerous human practices.

Question 3. Write a short paragraph on intelligence.

Answer: Intelligence is perceived as the capacity to gain information, to think and give reason adequately and to manage the climate. Intelligence addresses a point of convergence for therapists, they expect to see how individuals can take on their conduct to the climate in which they live.

Question 4. What is emotional intelligence?

Answer: Emotional Intelligence is the capacity to distinguish and control one’s feelings and comprehend the feelings the others. A high Emotional Intelligence assists you with building connections, decrease group pressure, stop the struggle and further develop work fulfillment. Emotional Intelligence is significant for each and every individual who needs to be vocation prepared.

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Conclusion of Artificial Intelligence | How to Write | With Example

Artificial intelligence has been a topic of fascination and debate for decades, with endless possibilities and potential threats being explored. As we come closer to the peak of AI advancement, the question arises: what is the conclusion of artificial intelligence? Will it bring about a utopian society where machines cater to our every need, or will it lead to our downfall as humans become obsolete in the face of superior intelligence?

The conclusion of artificial intelligence is still uncertain, with experts and researchers divided on the potential outcomes. Some argue that AI has the power to revolutionize industries, improve efficiency, and enhance our quality of life. Others warn of job loss, ethical dilemmas, and the dangers of creating machines that surpass human intelligence.

Why Conclusion is important in Artificial Intelligence Project

When working on an Artificial Intelligence Project, it is crucial to include a well-thought-out Conclusion at the end of the project. The Conclusion serves as a summation of the project’s findings and outcomes, providing a clear and concise overview of the key points discussed. It allows the reader to understand the significance of the research conducted and the implications it may have on future studies or implementations.

The Conclusion can also highlight any limitations or challenges encountered during the project, providing a more comprehensive understanding of the work done. Moreover, a well-crafted Conclusion can also help reinforce the main objectives of the project and tie together various aspects of the research to form a cohesive narrative.

How to Write Conclusion of Artificial Intelligence

Writing a conclusion for an essay or report on Artificial Intelligence (AI) should summarize the main points, reiterate the importance of the topic, and provide a forward-looking statement.

• Briefly summarize the key points you’ve discussed in your essay. This could include the definition and types of AI, its applications, benefits, challenges, or ethical considerations, depending on what you’ve focused on.
• Explain why AI is a significant topic. You could discuss its impact on society, economy, or technology, or its potential for future advancements.
• Make it clear why your readers should care about what you’ve written. This could involve discussing the implications of your findings or arguments, or how they relate to broader issues.
• Conclude by looking to the future. What might happen next in the field of AI? What further research is needed? What challenges need to be overcome?

Conclusion of Artificial Intelligence

In conclusion, Artificial Intelligence, ranging from narrow AI to superintelligent AI, has profoundly impacted various sectors, from healthcare to transportation. Its ability to analyze vast amounts of data and make predictions has led to increased efficiency and new opportunities. However, it also presents ethical challenges that we must navigate carefully. As we move forward, it is crucial to continue researching and developing AI in a responsible manner, ensuring that it serves as a tool for human betterment while mitigating potential risks.

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Conclusion of AI

Artificial intelligence has made huge advances recently due to more computing power, big data, and better algorithms. AI can now match or beat humans at specific tasks like games, image recognition and language processing. However, AI still lacks general intelligence and common sense. More research is needed for AI to achieve complex reasoning, creativity and social skills. As AI advances, policymakers need to address ethical concerns around risks and benefits to ensure these powerful technologies benefit humanity.

Conclusion of Artificial Intelligence Sample

In conclusion, the integration of artificial intelligence technologies into our daily lives has shown great promise in improving efficiency, productivity, and decision-making processes. It is important to address ethical concerns, data privacy issues, and potential job displacement that may arise from the widespread adoption of AI. The future of artificial intelligence appears to be bright, but careful consideration and regulation are necessary to ensure its responsible and beneficial use for society.

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Artificial Intelligence Conclusion

In summary, the evolution of Artificial Intelligence has transformed the way we live and work, with its applications spanning from virtual personal assistants to autonomous vehicles. The benefits of AI, such as improved productivity and decision-making, are undeniable. However, it also raises important questions about job displacement, privacy, and security. As we continue to advance in this field, it is essential to strike a balance between leveraging AI’s potential and addressing its challenges. Future research should focus on creating transparent, explainable, and ethically sound AI systems. In this way, we can ensure that AI is a force for good, enhancing human capabilities and contributing to a better future for all.

Conclusion of Artificial Intelligence Example

Artificial Intelligence is a transformative technology with far-reaching impacts. It offers significant benefits, including improved efficiency and new opportunities, but also presents challenges such as job displacement and privacy concerns. As we look to the future, it is crucial to continue exploring AI’s potential while addressing its ethical implications. By doing so, we can harness AI to enhance human capabilities and contribute to a prosperous, inclusive future.

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Artificial Intelligence and Its Impact on Education Essay

Introduction, ai’s impact on education, the impact of ai on teachers, the impact of ai on students, reference list.

Rooted in computer science, Artificial Intelligence (AI) is defined by the development of digital systems that can perform tasks, which are dependent on human intelligence (Rexford, 2018). Interest in the adoption of AI in the education sector started in the 1980s when researchers were exploring the possibilities of adopting robotic technologies in learning (Mikropoulos, 2018).

Their mission was to help learners to study conveniently and efficiently. Today, some of the events and impact of AI on the education sector are concentrated in the fields of online learning, task automation, and personalization learning (Chen, Chen and Lin, 2020). The COVID-19 pandemic is a recent news event that has drawn attention to AI and its role in facilitating online learning among other virtual educational programs. This paper seeks to find out the possible impact of artificial intelligence on the education sector from the perspectives of teachers and learners.

Technology has transformed the education sector in unique ways and AI is no exception. As highlighted above, AI is a relatively new area of technological development, which has attracted global interest in academic and teaching circles. Increased awareness of the benefits of AI in the education sector and the integration of high-performance computing systems in administrative work have accelerated the pace of transformation in the field (Fengchun et al. , 2021). This change has affected different facets of learning to the extent that government agencies and companies are looking to replicate the same success in their respective fields (IBM, 2020). However, while the advantages of AI are widely reported in the corporate scene, few people understand its impact on the interactions between students and teachers. This research gap can be filled by understanding the impact of AI on the education sector, as a holistic ecosystem of learning.

As these gaps in education are minimized, AI is contributing to the growth of the education sector. Particularly, it has increased the number of online learning platforms using big data intelligence systems (Chen, Chen and Lin, 2020). This outcome has been achieved by exploiting opportunities in big data analysis to enhance educational outcomes (IBM, 2020). Overall, the positive contributions that AI has had to the education sector mean that it has expanded opportunities for growth and development in the education sector (Rexford, 2018). Therefore, teachers are likely to benefit from increased opportunities for learning and growth that would emerge from the adoption of AI in the education system.

The impact of AI on teachers can be estimated by examining its effects on the learning environment. Some of the positive outcomes that teachers have associated with AI adoption include increased work efficiency, expanded opportunities for career growth, and an improved rate of innovation adoption (Chen, Chen and Lin, 2020). These benefits are achievable because AI makes it possible to automate learning activities. This process gives teachers the freedom to complete supplementary tasks that support their core activities. At the same time, the freedom they enjoy may be used to enhance creativity and innovation in their teaching practice. Despite the positive outcomes of AI adoption in learning, it undermines the relevance of teachers as educators (Fengchun et al., 2021). This concern is shared among educators because the increased reliance on robotics and automation through AI adoption has created conditions for learning to occur without human input. Therefore, there is a risk that teacher participation may be replaced by machine input.

Performance Evaluation emerges as a critical area where teachers can benefit from AI adoption. This outcome is feasible because AI empowers teachers to monitor the behaviors of their learners and the differences in their scores over a specific time (Mikropoulos, 2018). This comparative analysis is achievable using advanced data management techniques in AI-backed performance appraisal systems (Fengchun et al., 2021). Researchers have used these systems to enhance adaptive group formation programs where groups of students are formed based on a balance of the strengths and weaknesses of the members (Live Tiles, 2021). The information collected using AI-backed data analysis techniques can be recalibrated to capture different types of data. For example, teachers have used AI to understand students’ learning patterns and the correlation between these configurations with the individual understanding of learning concepts (Rexford, 2018). Furthermore, advanced biometric techniques in AI have made it possible for teachers to assess their student’s learning attentiveness.

Overall, the contributions of AI to the teaching practice empower teachers to redesign their learning programs to fill the gaps identified in the performance assessments. Employing the capabilities of AI in their teaching programs has also made it possible to personalize their curriculums to empower students to learn more effectively (Live Tiles, 2021). Nonetheless, the benefits of AI to teachers could be undermined by the possibility of job losses due to the replacement of human labor with machines and robots (Gulson et al. , 2018). These fears are yet to materialize but indications suggest that AI adoption may elevate the importance of machines above those of human beings in learning.

The benefits of AI to teachers can be replicated in student learning because learners are recipients of the teaching strategies adopted by teachers. In this regard, AI has created unique benefits for different groups of learners based on the supportive role it plays in the education sector (Fengchun et al., 2021). For example, it has created conditions necessary for the use of virtual reality in learning. This development has created an opportunity for students to learn at their pace (Live Tiles, 2021). Allowing students to learn at their pace has enhanced their learning experiences because of varied learning speeds. The creation of virtual reality using AI learning has played a significant role in promoting equality in learning by adapting to different learning needs (Live Tiles, 2021). For example, it has helped students to better track their performances at home and identify areas of improvement in the process. In this regard, the adoption of AI in learning has allowed for the customization of learning styles to improve students’ attention and involvement in learning.

AI also benefits students by personalizing education activities to suit different learning styles and competencies. In this analysis, AI holds the promise to develop personalized learning at scale by customizing tools and features of learning in contemporary education systems (du Boulay, 2016). Personalized learning offers several benefits to students, including a reduction in learning time, increased levels of engagement with teachers, improved knowledge retention, and increased motivation to study (Fengchun et al., 2021). The presence of these benefits means that AI enriches students’ learning experiences. Furthermore, AI shares the promise of expanding educational opportunities for people who would have otherwise been unable to access learning opportunities. For example, disabled people are unable to access the same quality of education as ordinary students do. Today, technology has made it possible for these underserved learners to access education services.

Based on the findings highlighted above, AI has made it possible to customize education services to suit the needs of unique groups of learners. By extension, AI has made it possible for teachers to select the most appropriate teaching methods to use for these student groups (du Boulay, 2016). Teachers have reported positive outcomes of using AI to meet the needs of these underserved learners (Fengchun et al., 2021). For example, through online learning, some of them have learned to be more patient and tolerant when interacting with disabled students (Fengchun et al., 2021). AI has also made it possible to integrate the educational and curriculum development plans of disabled and mainstream students, thereby standardizing the education outcomes across the divide. Broadly, these statements indicate that the expansion of opportunities via AI adoption has increased access to education services for underserved groups of learners.

Overall, AI holds the promise to solve most educational challenges that affect the world today. UNESCO (2021) affirms this statement by saying that AI can address most problems in learning through innovation. Therefore, there is hope that the adoption of new technology would accelerate the process of streamlining the education sector. This outcome could be achieved by improving the design of AI learning programs to make them more effective in meeting student and teachers’ needs. This contribution to learning will help to maximize the positive impact and minimize the negative effects of AI on both parties.

The findings of this study demonstrate that the application of AI in education has a largely positive impact on students and teachers. The positive effects are summarized as follows: improved access to education for underserved populations improved teaching practices/instructional learning, and enhanced enthusiasm for students to stay in school. Despite the existence of these positive views, negative outcomes have also been highlighted in this paper. They include the potential for job losses, an increase in education inequalities, and the high cost of installing AI systems. These concerns are relevant to the adoption of AI in the education sector but the benefits of integration outweigh them. Therefore, there should be more support given to educational institutions that intend to adopt AI. Overall, this study demonstrates that AI is beneficial to the education sector. It will improve the quality of teaching, help students to understand knowledge quickly, and spread knowledge via the expansion of educational opportunities.

Chen, L., Chen, P. and Lin, Z. (2020) ‘Artificial intelligence in education: a review’, Institute of Electrical and Electronics Engineers Access , 8(1), pp. 75264-75278.

du Boulay, B. (2016) Artificial intelligence as an effective classroom assistant. Institute of Electrical and Electronics Engineers Intelligent Systems , 31(6), pp.76–81.

Fengchun, M. et al. (2021) AI and education: a guide for policymakers . Paris: UNESCO Publishing.

Gulson, K . et al. (2018) Education, work and Australian society in an AI world . Web.

IBM. (2020) Artificial intelligence . Web.

Live Tiles. (2021) 15 pros and 6 cons of artificial intelligence in the classroom . Web.

Mikropoulos, T. A. (2018) Research on e-Learning and ICT in education: technological, pedagogical and instructional perspectives . New York, NY: Springer.

Rexford, J. (2018) The role of education in AI (and vice versa). Web.

Seo, K. et al. (2021) The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education , 18(54), pp. 1-12.

UNESCO. (2021) Artificial intelligence in education . Web.

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Home — Essay Samples — Information Science and Technology — Modern Technology — Artificial Intelligence

Essays on Artificial Intelligence

Artificial intelligence essay topics for college students.

Welcome, college students! Writing an essay on artificial intelligence can be an exciting and challenging task. The key to a successful essay lies in selecting the right topic that sparks your interest and allows you to showcase your creativity. In this resource page, we will provide you with a variety of essay types and topics to help you get started on your AI essay journey.

Argumentative Essay Topic for Artificial Intelligence Essays

• The ethical implications of AI technology
• The impact of AI on job automation
• Regulating AI development for societal benefits

Introduction Paragraph Example: Artificial intelligence has revolutionized the way we interact with technology, raising important ethical questions about its implications on society. In this essay, we will explore the ethical challenges of AI technology and discuss the need for regulations to ensure its responsible development.

Conclusion Paragraph Example: In conclusion, it is evident that the ethical implications of AI technology are multifaceted and require careful consideration. By implementing regulations and ethical guidelines, we can harness the benefits of AI while minimizing its potential risks.

Compare and Contrast Essay Topics for Artificial Intelligence

• The differences between narrow AI and general AI
• Comparing AI in science fiction to real-world applications
• The impact of AI on different industries
• AI vs. human intelligence: Strengths and weaknesses
• Machine learning vs. deep learning
• AI in healthcare vs. AI in finance
• AI-driven automation vs. traditional automation
• Cloud-based AI vs. edge AI
• The role of AI in developed vs. developing countries
• AI in education vs. AI in entertainment

Introduction Paragraph Example: The field of artificial intelligence encompasses a wide range of technologies, from narrow AI systems designed for specific tasks to the hypothetical concept of general AI capable of human-like intelligence. In this essay, we will compare and contrast the characteristics of narrow and general AI to understand their implications on society.

Conclusion Paragraph Example: Through this comparison, we have gained insights into the diverse applications of AI technology and the potential challenges it poses to various industries. By understanding the differences between narrow and general AI, we can better prepare for the future of artificial intelligence.

Descriptive Essay Essay Topics for Artificial Intelligence

• The role of AI in healthcare advancements
• The development of AI algorithms for autonomous vehicles
• The applications of AI in natural language processing
• The architecture of neural networks
• The evolution of AI from the 20th century to today
• The ethical implications of AI decision-making
• The process of training an AI model
• The impact of AI on the job market
• The future potential of quantum AI
• The role of AI in personalized marketing

Introduction Paragraph Example: AI technology has transformed the healthcare industry, enabling innovative solutions that improve patient care and diagnosis accuracy. In this essay, we will explore the role of AI in healthcare advancements and its impact on the future of medicine.

Conclusion Paragraph Example: In conclusion, the integration of AI technology in healthcare has the potential to revolutionize the way we approach patient care and medical research. By leveraging AI algorithms and machine learning capabilities, we can achieve significant advancements in the field of medicine.

Persuasive Essay Essay Topics for Artificial Intelligence

• Promoting diversity and inclusion in AI development
• The importance of ethical AI education in schools
• Advocating for AI transparency and accountability
• The necessity of regulating AI technology
• Why AI should be used to combat climate change
• The benefits of AI in improving public safety
• Encouraging responsible AI usage in social media
• The potential of AI to revolutionize education
• Why businesses should invest in AI technology
• The role of AI in enhancing cybersecurity

Introduction Paragraph Example: As artificial intelligence continues to permeate various aspects of our lives, it is essential to prioritize diversity and inclusion in AI development to ensure equitable outcomes for all individuals. In this essay, we will discuss the importance of promoting diversity and inclusion in AI initiatives and the benefits it brings to society.

Conclusion Paragraph Example: By advocating for diversity and inclusion in AI development, we can create a more equitable and socially responsible future for artificial intelligence. Through ethical education and transparent practices, we can build a foundation of trust and accountability in AI technology.

Narrative Essay Essay Topics for Artificial Intelligence

• A day in the life of an AI researcher
• The journey of building your first AI project
• An imaginary conversation with a sentient AI being
• The story of a world transformed by AI
• How AI solved a major global problem
• A personal encounter with AI technology
• The evolution of AI in your lifetime
• The challenges faced while developing an AI startup
• A future where AI coexists with humans
• Your experience learning about AI for the first time

Introduction Paragraph Example: Imagine a world where artificial intelligence blurs the lines between human and machine, offering new possibilities and ethical dilemmas. In this narrative essay, we will embark on a journey through the eyes of an AI researcher, exploring the challenges and discoveries that come with pushing the boundaries of technology.

Conclusion Paragraph Example: Through this narrative journey, we have delved into the complexities of artificial intelligence and the ethical considerations that accompany its development. By embracing the possibilities of AI technology while acknowledging its limitations, we can shape a future that balances innovation with ethical responsibility.

Hooks for Artificial Intelligence Essay

• "Imagine a world where machines not only perform tasks but also think, learn, and make decisions just like humans. Welcome to the era of Artificial Intelligence (AI), a revolutionary force reshaping our future."
• "From self-driving cars to smart personal assistants, AI is seamlessly integrating into our daily lives. But what lies beneath this cutting-edge technology, and how will it transform the way we live and work?"
• "As AI continues to advance at an unprecedented pace, questions about its ethical implications and impact on society become more urgent. Can we control the intelligence we create, or will it control us?"
• "AI is not just a futuristic concept confined to science fiction. It’s here, and it’s real, influencing industries, healthcare, education, and even our personal lives. How prepared are we for this technological revolution?"
• "The debate over AI is heating up: Will it lead to a utopian society with endless possibilities, or is it a Pandora's box with risks we have yet to fully understand? The answers may surprise you."

Artificial Intelligence: a Threat Or a Potential Boon

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Artificial Intelligence: Good and Bad Effects for Humanity

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Artificial Intelligence: Applications, Advantages and Disanvantages

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Artificial intelligence (AI) refers to the intellectual capabilities exhibited by machines, contrasting with the innate intelligence observed in living beings, such as animals and humans.

The inception of artificial intelligence research as an academic field can be traced back to its establishment in 1956. It was during the renowned Dartmouth conference of the same year that artificial intelligence acquired its distinctive name, definitive purpose, initial accomplishments, and notable pioneers, thereby earning its reputation as the birthplace of AI. The esteemed figures of Marvin Minsky and John McCarthy are widely recognized as the founding fathers of this discipline.

• The term "artificial intelligence" was coined in 1956 by computer scientist John McCarthy.
• McKinsey Global Institute estimates that by 2030, automation and AI technologies could contribute to a global economic impact of $13 trillion.
• AI is used in various industries, including healthcare, finance, and transportation.
• The healthcare industry is leveraging AI for improved patient care. A study published in the journal Nature Medicine reported that an AI model was able to detect breast cancer with an accuracy of 94.5%, outperforming human radiologists.
• Ethical concerns surrounding AI include privacy issues, bias in algorithms, and the potential for job displacement.

Artificial Intelligence is an important topic because it has the potential to revolutionize industries, improve efficiency, and enhance decision-making processes. As AI technology continues to advance, it is crucial for society to understand its implications, both positive and negative, in order to harness its benefits while mitigating its risks.

1. Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: A Modern Approach (3rd ed.). Prentice Hall. 2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. 3. Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. Viking. 4. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford University Press. 5. Chollet, F. (2017). Deep Learning with Python. Manning Publications. 6. Domingos, P. (2018). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. 7. Ng, A. (2017). Machine Learning Yearning. deeplearning.ai. 8. Marcus, G. (2018). Rebooting AI: Building Artificial Intelligence We Can Trust. Vintage. 9. Winfield, A. (2018). Robotics: A Very Short Introduction. Oxford University Press. 10. Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.

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Psychology Discussion

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Here is an essay on ‘Intelligence’ for class 11 and 12. Find paragraphs, long and short essays on ‘Intelligence’ especially written for school and college students.

Essay # 1. Intelligence- Contrasting Views of Its Nature:

Intelligence, like love, is one of those concepts that are easier to recognize than to define. We often refer to others’ intelligence, describing people as bright, sharp, or quick on the one hand, or as slow, dull, or even stupid on the other. And slurs on one’s intelligence are often fighting words where children and even adults are concerned.

But again, what, precisely, is intelligence? Psychologists don’t entirely agree, but as a working definition we can adopt the wording offered by a distinguished panel of experts. The term intelligence refers to individuals’ abilities to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by careful thought.

Why do we place so much importance on evaluating others’ (and our thought, own) intelligence? Partly because we believe that intelligence is related to many important outcomes; how quickly individuals can master new tasks and adapt to new situations, how successful they will be in school and in various kinds of jobs, and even how well they can get along with others.

To some extent, our commonsense ideas in this respect are correct. But although intelligence is related to important life outcomes, this relationship is far from perfect. Many other factors, too, play a role, so predictions based on intelligence alone can be wrong.

Intelligence: Unitary or Multifaceted?

Is intelligence a single characteristic, or does it involve several different components? In the past, psychologists who studied intelligence often disagreed sharply on this issue. In one camp were scientists who viewed intel­ligence as a single characteristic or dimension along which people vary. One early supporter of this view was Spearman (1927), who believed that performance on any cognitive task depended on a primary general factor (which he termed g) and one or more specific factors relating to particular tasks.

He found that although tests of intelligence often contain different kinds of items designed to measure different aspects of intelligence, scores on these items often correlate highly with one another. This fact suggested to him that no matter how intel­ligence was measured, it was related to a single, primary factor.

In contrast, other researchers believed that intelligence is composed of many separate abilities that operate more or less independently. According to this multifactor view, a given person can be high on some components of intelligence but low on others and vice versa.

One early supporter of this position was Thurstone (1938), who suggested that intelligence is composed of seven distinct primary mental abilities. Included in his list were verbal meaning understanding of ideas and word meanings; number speed and accuracy in dealing with numbers; and space the ability to visualize objects in three dimensions.

Most modern theories of intelligence recognize that intelligence may involve a general ability to handle a wide range of cognitive tasks and problems, as Spearman suggested, but also that intelligence is expressed in many different ways, and that persons can be high on some aspects of intelligence but low on others. As examples of this modern approach, let’s briefly consider three influential views of intelligence.

Culture and Intelligence:

A major characteristic of intelligence is that it helps individuals to adapt to their environment. The cultural environment provides a context for intelligence to develop. Vygotsky has argued that culture provides a social context in which people live, grow, and understand the world around them.

For example, in less technologically developed societies, social and emotional skills in relating to people are valued, while in technologically advanced societies, personal achievement founded on abilities of reasoning and judgment is considered to represent intelligence.

A person’s intelligence is likely to be tuned by these cultural parameters. Many theorists have regarded intelligence in terms of attributes specific to the person without regard to their cultural background. The unique features of culture now find some representation in theories of intelligence.

Vygotsky also believed that cultures, like individuals, have a life of their own; they grow and change, and in the process specify what will be the end-product of successful intellectual development. Thus, while ele­mentary mental functions (e.g., crying, attending to mother’s voice, sensi­tivity to smells, walking, and running) are universal, the manner in which higher mental functions such as problem solving and thinking operate are largely culture-produced.

Technologically advanced societies; adopt child rearing practices that foster skills of generalization and abstraction, speed, minimal moves, and mental manipulation among children. This is a type of intelligence, which can be called technological intelligence. In these societies, persons are well-versed in skills of attention, observation, analysis, performance, speed, and achievement orientation. Intelligence tests devel­oped in Western cultures look precisely for these skills in an individual.

Technological intelligence is not so valued in many Asian and African societies. The qualities and skills regarded as intelligent actions in non-western cultures are sharply different, though the boundaries are gradu­ally vanishing with the processes of acculturation and globalization.

In addition to cognitive competence that is very specific to the individual, the non-western cultures look for skills to relate to others in the society. Some non-western societies value self-reflection and collectivistic orientation as opposed to personal achievement and individualistic orientation.

Intelligence in the Indian Tradition:

Contrary to technological intelligence, intelligence in the Indian tradition can be termed as integral intelligence, which gives emphasis on connectivity with the social and world environment.

Indian thinkers view intelligence from a holistic perspective where equal attention is paid to cognitive and non-cognitive processes as well as their integration. Intelligence in the Indian thought systems is treated as a state, a process, and an entity, the realiza­tion of which depends upon one’s own effort, persistence, and motivation.

The Sanskrit word buddhi which is often used to represent intelligence is far more pervasive in scope than the western concept of intelligence. Buddhi, according to J. P. Das (1994), includes skills such as mental effort, determined action, feelings, and opinions along with cognitive competence such as knowledge, discrimination, and understanding.

Among other things, buddhi is the knowledge of one’s own self based on conscience, will, and desire. Thus, the notion of buddhi has affective and motivational components besides a strong cognitive component. Unlike the Western views, which primarily focus on cognitive functions, the following compe­tencies are identified as facets of intelligence in the Indian tradition.

i. Cognitive competence (sensitivity to context, understanding, discrimination, problem solving, and effective communication).

ii. Social competence (respect for social order, commitment to elders, the young and the needy, concern about others, recognizing others’ perspectives).

iii. Emotional competence (self-regulation and self-monitoring of emotions, honesty, politeness, good conduct, and self-evaluation).

iv. Entrepreneurial competence (commitment, persistence, patience, hard work, vigilance, and goal-directed behaviors).

The role of genetic and environmental factors in intellectual growth was also recognized in Indian thought. Intelligence is seen as the result of one’s own karma and inheritance. However, the expression of this genetic endowment is believed to depend upon the child’s own efforts and endeavor.

Baral and Das (2004) have reviewed the developments in intelligence in Indian context including contribu­tions of Sri Aurobindo and Krishnamurti. According to Sri Aurobindo, ultimate aim of intelligence is direct cognizance without the mediation of senses and hence without the distortions brought by the ego. Human intellectual pursuit occurs at two levels.

At the lower level, the obvious functions of the mind are reasoning and inference based on sense experience. But the higher function of intelligence is self-awareness, using the mind to know about oneself. Krishnamurti contends that intelligence is truth, beauty, completeness, and love itself. To understand the environment, whatever it maybe is intelligence.

Essay # 2. Human Intelligence- The Role of Heredity and the Role of Environment :

Human intelligence is clearly the result of the complex interplay between genetic factors and a wide range of environmental conditions. Here we’ll consider some of the evidence pointing to this conclusion.

i. Evidence for the Influence of Heredity :

Several lines of research offer support for the view that heredity plays an important role in human intelligence. First, consider findings with respect to family relationship and measured IQ. If intelligence is indeed deter­mined by heredity, we would expect that the more closely two persons are related, the more similar their IQs will be. This prediction has generally been confirmed. For example, the IQs of iden­tical twins raised together correlate almost +.90, those of brothers and sisters about +.50, and those of cousins about +.15.

Additional support for the impact of heredity on intelligence is provided by studies involving adopted children. If intelligence is strongly affected by genetic factors, the IQs of adopted children should resemble those of their biological parents more closely than those of their adoptive parents.

In short, the children should be more similar in IQ to the persons from whom they received their genes than to the persons who raised them. This prediction, too, has been confirmed. For example, consider a long-term study conducted by Plomin and his colleagues.

In this investigation (the Colorado Adoption Project), the researchers studied 245 children who were placed for adoption by their mothers shortly after birth (on average, when they were twenty-nine days old) until they were teenagers. Measures of the children’s intelligence were obtained when they were one, two, three, four, seven, twelve, and sixteen years old. In addition, measures were obtained of their biological mothers’ intelligence and of their adoptive parents’ intelligence.

A comparison group of children who were living with their biological parents was tested in the same manner. The results showed a clear pattern; the correlation between the adopted children’s intelligence and that of their biological parents increased over time, as did the correlation between the intelligence of the control group (children living with their biological parents) and that of their parents.

In contrast, the correlation between the intelligence of the adopted children and that of their adoptive parents decreased over time. Similar patterns were found for specific components of intelligence, as well as for general cognitive. These findings suggest that genetic factors play an important role in intelligence and may indeed outweigh environmental factors in this respect.

However, the authors are also quick to add that the children studied were placed in homes above average in socioeconomic status; thus, they were not ex­posed to environmental extremes of poverty, disadvantage, or malnutrition. Such extreme conditions can strongly affect children’s intelligence. In addition, somewhat different measures of intelligence were employed at different ages, especially for the youngest children; this too may have played some role in the pattern of findings obtained.

Additional evidence for the role of genetic factors in intelligence is provided by recent studies focused on the task of identifying the specific genes that influence intelligence. These studies have adopted as a working hypothesis the view that many genes, each exerting relatively small effects, probably play a role in general intelligence—that is, in what many aspects of mental abilities (e.g., verbal, spa­tial, speed-of-processing, and memory abilities) have in common.

In other words, such research has not attempted to identify the gene that influences intelligence, but rather has sought quantitative trait loci (QTLs): genes that have relatively small effects and that influence the likelihood of some characteristic in a population. Chorney and his colleagues (1998) compared individuals with IQ scores greater than 160 and a control group of persons average in intelligence (with mean IQ scores of about 100).

They found that persons in the very high-IQ group were more likely to possess a specific gene (actually, a particular form of this gene) than were persons in the average, however, that the effects of this gene were small; the researchers estimated that it accounted for only 2 percent of the variance in general intelligence.

Finally, evidence for the role of genetic factors in intelligence has been provided by research on identical twins separated as infants (usually, within the first few weeks of life) who were then raised in different homes. Because such persons have identical genetic inheritance but have been exposed to different environmental conditions in some cases, sharply contrasting conditions studying their IQs provides a powerful means for comparing the roles of genetic and environmental factors in human intel­ligence.

The results of such research are clear; the IQs of identical twins reared apart (often from the time they were only a few days old) correlate almost as highly as those of identical twins reared together. Moreover, such individuals are also amazingly simi­lar in many other characteristics, such as physical appearance, preferences in dress, mannerisms, and even personality. Clearly, these findings point to an important role for heredity in intelligence and in many other aspects of psychological functioning.

On the basis of these and other findings, some researchers have estimated that the heritability of intelligence, the proportion of the variance in intelligence within a given population that is attributable to genetic factors ranges from about 35 percent in childhood to as much as 75 percent in adulthood, and may be about 50 percent overall.

Why does the contribution of genetic factors to intelligence increase with age? Perhaps because as individuals grow older, their interactions with their environment are shaped less and less by restraints imposed on them by their families or by their social origins and are shaped more and more by the characteristics they bring with them to these environments. In other words, as they grow older, individuals are increasingly able to choose or change their environments so that these permit expression of their genetically determined tendencies and preferences.

ii. Evidence for the Influence of Environmental Factors :

Genetic factors are definitely not the entire picture where human intelligence is concerned, however. Other findings point to the conclusion that environmental variables, too, are important. One such finding is that performance on IQ tests has risen substantially around the world at all age levels in recent decades. This phe­nomenon is known as the Flynn effect after the psychologist who first reported it.

Such increases have averaged about 3 IQ points per decade worldwide; but in some countries they have been even larger. As a result of these gains in performance, it has been necessary to re-standardize widely used tests so that they con­tinue to yield an average IQ of 100; what is termed “average” today is actually a higher level of performance than was true in the past.

What accounts for these increases? It seems unlikely that massive shifts in human heredity occur from one generation to the next. A more reasonable explanation, therefore, focuses on changes in environmental factors. What factors have changed in recent decades? The following variables have been suggested as possible contribu­tors to the continuing rise in IQ: better nutrition, increased urbanization, the advent of television, more and better education, more cognitively demanding jobs, and even exposure to computer games!

Many of these changes are real and seem plausible as explanations for the rise in IQ; but, as noted recently by Flynn (1999), there is as yet not sufficient evidence to conclude that any or all of these factors have played a role. In any case, whatever the specific causes involved the steady rise in performance on IQ tests points to the importance of environmental factors in human intelligence.

Additional evidence provided by the findings of studies of environmental deprivation and environmental enrichment. With respect to deprivation, some findings suggest that intelligence can be reduced by the absence of key forms of environmental stimulation early in life. In terms of enrichment, removing children from sterile, restricted environments and placing them in more favorable settings seems to enhance their intellectual growth.

For example, in one of the first demonstrations of the beneficial impact on IQ of an enriched environment, Skeels (1938, 1966) removed thirteen children, all about two years old, from an orphan­age in which they received virtually no intellectual stimulation and virtually no contact with adults and placed them in the care of a group of retarded women living in an institution.

After a few years, Skeels noted that the children’s IQs had risen dramatically 29 points on average. Interestingly, Skeels also obtained IQ measures of children who had remained in the orphanage and found that these had actually dropped by 26 points on average presumably as a result of continued exposure to the impoverished environment at the orphanage.

Twenty-five years later, the thirteen children who had experienced the enriched environment were all doing well; most had gradu­ated from high school, found a job, and married. In contrast, those in the original control group either remained institutionalized or were functioning poorly in society.

In the Indian context the studies do indicate negative effects of poverty and deprivation on measures of intellective performance. These effects become pronounced with advancing age indicating cumulative deficit.

While more recent—and more carefully controlled—efforts to increase intelligence through environmental interventions have not yielded gains as dramatic as those reported by Skeels (1966), some of these programs have produced beneficial results.

However, as noted by Ramey and Ramey (1998), such changes are most likely to occur when the following conditions are met:

(1) The interventions begin early and continue for a long time;

(2) The programs are intense, involving home visits several times per week;

(3) The children receive new learning experiences delivered directly to them by experts rather than indirectly though their parents;

(4) The interventions are broad in scope, using many different procedures to enhance children’s development;

(5) The interventions are matched to the needs of individual children; and

(6) Environmental supports (e.g., excellent schools) are put in place to support and maintain the positive attitudes toward learning the children gain. Needless to say, programs that meet these criteria tend to be expensive.

Additional support for the role of environmental factors in intelligence is provided by the finding that many biological factors that children encounter while growing up can affect their intelligence. Prolonged malnutri­tion can adversely affect IQ, as can exposure to lead either in the air or in lead-based paint, which young children often eat because it tastes sweet.

Exposure to such factors as alcohol and drugs; indicate that these factors can also adversely affect intelligence. In sum, therefore, many forms of evidence support the view that intelligence is determined, at least in part, by environmental factors. Especially when these are extreme, they may slow or accelerate children’s intellectual growth; and this effect, in turn, can have important implications for the societies in which those children will become adults.

Essay # 3. Group Differences in Intelligence Test Scores- Why They Occur:

There are sizable differences among the average IQ scores of various ethnic groups. The members of some minority groups score lower, on average, than members of the majority group. Why do such differences occur? This has been a topic of considerable controversy in psychology for many years, and currently there is still no final, universally accepted conclusion.

However, it seems fair to say that at present, most psychologists attribute such group differences in performance on standard intelligence tests largely to environmental variables. 

Group Differences in IQ Scores: Evidence for the Role of Environmental Factors :

Group differences in performance on intelligence tests stem primarily from environmental factors: the fact that the tests themselves may be biased against test takers from some minority groups. Why? In part because the tests were standardized largely on middle-class white persons; thus, interpreting the test scores of persons from minority groups in terms of these norms is not appropriate.

Even worse, some critics have suggested that the tests themselves suffer from cultural bias: Items on the tests are ones that are familiar to middle-class white children and so give them an important edge in terms of test performance. Are such concerns valid? Careful examination of the items used on intelligence tests suggests that they may indeed be culturally biased, at least to a degree. Some items do seem to be ones that are less familiar and therefore more difficult to answer for minority test takers. To the extent that such cultural bias exists, it is indeed a serious flaw in IQ tests.

On the other hand, though, its important to note that the tests are gen­erally about as successful in predicting future school performance by children from all groups. So while the tests may be biased in terms of content, this in itself does not make them useless from the point of view of predicting future performance.

However, as noted by Steele and Aronson (1996), because minority children find at least some of the items on these tests unfamiliar, they may feel threatened by the tests; and this, in turn, may reduce their scores.

In an effort to eliminate cultural bias, psychologists have attempted to design culture-fair tests. Such tests attempt to include only items to which all groups, regardless of ethnic or racial background, have been exposed. Because many minority children are exposed to languages other than standard English, these tests tend to be nonverbal in nature. One of these, the Raven Progressive Matrices.

This test consists of sixty matrices of varying difficulty, each containing a logical pattern or design with a missing part. Individuals select the item that completes the pattern from several different choices.

Because the Raven test and ones like it focus primarily on fluid intelligence our basic abilities to form concepts, reason, and identify similarities these tests seem less likely to be subject to cultural bias than other kinds of intelligence tests. However, it is not clear that these tests, or any others, totally eliminate the problem of subtle built-in bias.

Additional evidence for the role of environmental factors in group differences in test performance has been divided by Flynn (1999), one expert on this issue, into two categories- indirect and direct. Indirect evidence is evidence from research in which efforts are made to equate environmental factors for all test takers, for instance, by eliminating the effects of socioeconomic status through statistical techniques.

The results of such studies are mixed; some suggest that the gap between minority groups and whites is reduced by such procedures, but other studies indicate that between-group differences still remain. These findings suggest that while socioeconomic factors contribute to group differences in IQ scores, other factors, as yet unknown, may also play some role.

Direct evidence for environmental factors, in contrast, involves actual life changes that take many minor­ity persons out of the disadvantaged environment they often face and provide them with an environment equivalent to that of other groups. According to Flynn (1999), one compelling piece of direct evidence for the role of environmental factors in group differences does exist.

During World War II, African American soldiers fathered thousands of children in Germany (much of which was occupied by U.S. troops after the war). These children have been raised by white mothers in what is essentially a white environment. The result? Their IQs are virtually identical to those of white children matched to them in socioeconomic status.

Given that the fathers of these children scored very similarly to other African American soldiers, these findings suggest that environmental factors are in fact the key to group differences in IQ: When such factors are largely eliminated, differences between the groups, too, disappear.

Group Differences in IQ Scores: Is There Any Evidence for the Role of Genetic Factors?

Now for the other side of the story the suggestion that group differences in intelligence stem largely from genetic factors. In 1994 this issue was brought into sharp focus by the publication of a highly controversial book entitled The Bell Curve.

Herrnstein and Murray (1994) voiced strong support for the genetic hypothesis. They noted, for instance, that there are several converging sources of evidence for “a genetic factor in cognitive ethnic differences” between African, American and other ethnic groups in the United States. They suggested that intelligence may not be readily modi­fiable through changes in environmental conditions. They proposed, therefore, that special programs aimed at raising the IQ scores of disadvantaged minorities were probably a waste of effort.

As you can imagine, these suggestions were challenged vigorously by many psychologists. These critics argued that much of the reasoning in The Bell Curve was flawed and that the book over­looked many important findings. Perhaps the harshest criticism of the book centered on its contention that because individual differences in intelligence are strongly influenced by genetic factors, group differences are, too. Several researchers took strong exception to this logic.

They contended that this reasoning would be accurate only if the environments of the various groups being compared were identical. Under those conditions, it could be argued that differences between the groups stemmed, at least in part, from genetic factors. In reality, however, the environments in which the members of various ethnic groups exist are not identical.

As a result, it is false to assume that group differences with respect to IQ scores stem from genetic factors, even if we know that individual differences in such scores are strongly influenced by these factors. (When environmental differences are removed or minimized, group differences in intelligence, too, disappear.) Perhaps this point is best illustrated by a simple analogy.

Imagine that a farmer plants a batch of seeds that are known to be genetically identical. The farmer plants the seeds in two different fields; one is known to contain all the nutrients needed for good plant growth, but the other lacks these nutrients. Several months later, there are large differences between the plants growing in the two fields, despite the fact that their genetic makeup is identical.

Why? Probably because of the contrasting soil fertility. So differ­ences between the two fields are due to this environmental factor, whereas within each field, any differences among the plants are due to genetic factors. In a similar manner, it is entirely possible that differences in the IQ scores of various groups occur because of contrasting life environment and that genetic factors play little if any role in such differences.

In fact, the worldwide gains in IQ are directly analogous to this example. Here we have a case in which variation in intelligence within each generation is strongly influenced by genetic factors we know that this is so. Yet differences between the generations must be due to environmental factors- no one would argue that one generation is genetically different from the next.

Such reasoning argues powerfully against a genetic basis for group differences in performance on tests of intelligence. While some researchers continue to insist that sufficient evidence exists to conclude that genetic factors play a role, most take strong exception to this view and contend that the evidence for this view is relatively weak.

Gender Differences in Intelligence :

Do males and females differ in intelligence? Overall, they score virtually identically on standard tests of this characteristic. However, a few subtle differences do seem to exist with respect to certain components of intelligence. First, females tend to score higher than males with respect to verbal abilities such tasks as naming synonyms (words with the same meaning) and verbal fluency (e.g., naming words that start with a given letter).

Females also score higher than males on college achievement tests in literature, spelling, and writing. Such differences are relatively small and seem to be decreasing, but they do appear, even in very careful meta-analyses performed on the results of many different studies.

In contrast, males tend to score somewhat higher than females on visual-spatial tasks such as mental rota­tion or tracking a moving object through space. Ask several male and female friends to try their hand at the task it involves. You may discover that the males find this slightly easier (and perhaps more enjoyable) than the females. However, gender differences in performing visual-spatial tasks, like almost all gender differences, are far smaller than gender stereotypes suggest; so if you do observe any differ­ence, it is likely to be a small one.

Additional findings suggest that other subtle differences may exist between males and females with respect to various aspects of intelligence. For instance, consider the following study by Silverman and Eals (1992). These researchers asked female and male participants to perform several tasks in a small office.

In one condition participants were told to try to remember the location of various objects in the room; in another no mention was made of this task. When later asked to name the objects and indicate their locations, women outperformed men in both conditions. However, the difference was larger in the condition in which participants were not told to remember the information.

Other studies, in contrast, indicate that men are better at finding their way back to some physical location after taking a complex route away from it. What accounts for these observations? Silverman and Eals suggest that such gender differences may reflect different kinds of tasks performed by females and males during the evolution of our species. Before the development of civilization, humans lived by hunting and gathering.

Men hunted and women foraged for edible plants. Silverman and Eals suggest that these tasks required different spa­tial abilities. Gatherers (primarily females) needed to be able to notice edible plants and to pinpoint their loca­tion so that they could find them again in the future. In contrast, hunters (mainly males) needed to be able to find their way back home after crossing large distances.

The result, the two psychologists suggest, is that men are better at tasks such as rotating objects in their minds, while women are better at noticing and remembering spe­cific objects and their locations.

We can’t do experiments on the evolution of our species, however, so we can’t know for certain. In any case, it is clear that a few differences do exist between males and females where certain components of intelligence are concerned, but that these differences are small in size and subtle in nature.

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Artificial Intelligence Essay for Students and Children

500+ words essay on artificial intelligence.

Artificial Intelligence refers to the intelligence of machines. This is in contrast to the natural intelligence of humans and animals. With Artificial Intelligence, machines perform functions such as learning, planning, reasoning and problem-solving. Most noteworthy, Artificial Intelligence is the simulation of human intelligence by machines. It is probably the fastest-growing development in the World of technology and innovation . Furthermore, many experts believe AI could solve major challenges and crisis situations.

Types of Artificial Intelligence

First of all, the categorization of Artificial Intelligence is into four types. Arend Hintze came up with this categorization. The categories are as follows:

Type 1: Reactive machines – These machines can react to situations. A famous example can be Deep Blue, the IBM chess program. Most noteworthy, the chess program won against Garry Kasparov , the popular chess legend. Furthermore, such machines lack memory. These machines certainly cannot use past experiences to inform future ones. It analyses all possible alternatives and chooses the best one.

Type 2: Limited memory – These AI systems are capable of using past experiences to inform future ones. A good example can be self-driving cars. Such cars have decision making systems . The car makes actions like changing lanes. Most noteworthy, these actions come from observations. There is no permanent storage of these observations.

Type 3: Theory of mind – This refers to understand others. Above all, this means to understand that others have their beliefs, intentions, desires, and opinions. However, this type of AI does not exist yet.

Type 4: Self-awareness – This is the highest and most sophisticated level of Artificial Intelligence. Such systems have a sense of self. Furthermore, they have awareness, consciousness, and emotions. Obviously, such type of technology does not yet exist. This technology would certainly be a revolution .

Get the huge list of more than 500 Essay Topics and Ideas

Applications of Artificial Intelligence

First of all, AI has significant use in healthcare. Companies are trying to develop technologies for quick diagnosis. Artificial Intelligence would efficiently operate on patients without human supervision. Such technological surgeries are already taking place. Another excellent healthcare technology is IBM Watson.

Artificial Intelligence in business would significantly save time and effort. There is an application of robotic automation to human business tasks. Furthermore, Machine learning algorithms help in better serving customers. Chatbots provide immediate response and service to customers.

AI can greatly increase the rate of work in manufacturing. Manufacture of a huge number of products can take place with AI. Furthermore, the entire production process can take place without human intervention. Hence, a lot of time and effort is saved.

Artificial Intelligence has applications in various other fields. These fields can be military , law , video games , government, finance, automotive, audit, art, etc. Hence, it’s clear that AI has a massive amount of different applications.

To sum it up, Artificial Intelligence looks all set to be the future of the World. Experts believe AI would certainly become a part and parcel of human life soon. AI would completely change the way we view our World. With Artificial Intelligence, the future seems intriguing and exciting.

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How artificial intelligence is transforming the world

Subscribe to the center for technology innovation newsletter, darrell m. west and darrell m. west senior fellow - center for technology innovation , douglas dillon chair in governmental studies john r. allen john r. allen.

April 24, 2018

Artificial intelligence (AI) is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making—and already it is transforming every walk of life. In this report, Darrell West and John Allen discuss AI’s application across a variety of sectors, address issues in its development, and offer recommendations for getting the most out of AI while still protecting important human values.

Table of Contents I. Qualities of artificial intelligence II. Applications in diverse sectors III. Policy, regulatory, and ethical issues IV. Recommendations V. Conclusion

• 49 min read

Most people are not very familiar with the concept of artificial intelligence (AI). As an illustration, when 1,500 senior business leaders in the United States in 2017 were asked about AI, only 17 percent said they were familiar with it. 1 A number of them were not sure what it was or how it would affect their particular companies. They understood there was considerable potential for altering business processes, but were not clear how AI could be deployed within their own organizations.

Despite its widespread lack of familiarity, AI is a technology that is transforming every walk of life. It is a wide-ranging tool that enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decisionmaking. Our hope through this comprehensive overview is to explain AI to an audience of policymakers, opinion leaders, and interested observers, and demonstrate how AI already is altering the world and raising important questions for society, the economy, and governance.

In this paper, we discuss novel applications in finance, national security, health care, criminal justice, transportation, and smart cities, and address issues such as data access problems, algorithmic bias, AI ethics and transparency, and legal liability for AI decisions. We contrast the regulatory approaches of the U.S. and European Union, and close by making a number of recommendations for getting the most out of AI while still protecting important human values. 2

In order to maximize AI benefits, we recommend nine steps for going forward:

• Encourage greater data access for researchers without compromising users’ personal privacy,
• invest more government funding in unclassified AI research,
• promote new models of digital education and AI workforce development so employees have the skills needed in the 21 st -century economy,
• create a federal AI advisory committee to make policy recommendations,
• engage with state and local officials so they enact effective policies,
• regulate broad AI principles rather than specific algorithms,
• take bias complaints seriously so AI does not replicate historic injustice, unfairness, or discrimination in data or algorithms,
• maintain mechanisms for human oversight and control, and
• penalize malicious AI behavior and promote cybersecurity.

Qualities of artificial intelligence

Although there is no uniformly agreed upon definition, AI generally is thought to refer to “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention.” 3  According to researchers Shubhendu and Vijay, these software systems “make decisions which normally require [a] human level of expertise” and help people anticipate problems or deal with issues as they come up. 4 As such, they operate in an intentional, intelligent, and adaptive manner.

Intentionality

Artificial intelligence algorithms are designed to make decisions, often using real-time data. They are unlike passive machines that are capable only of mechanical or predetermined responses. Using sensors, digital data, or remote inputs, they combine information from a variety of different sources, analyze the material instantly, and act on the insights derived from those data. With massive improvements in storage systems, processing speeds, and analytic techniques, they are capable of tremendous sophistication in analysis and decisionmaking.

Artificial intelligence is already altering the world and raising important questions for society, the economy, and governance.

Intelligence

AI generally is undertaken in conjunction with machine learning and data analytics. 5 Machine learning takes data and looks for underlying trends. If it spots something that is relevant for a practical problem, software designers can take that knowledge and use it to analyze specific issues. All that is required are data that are sufficiently robust that algorithms can discern useful patterns. Data can come in the form of digital information, satellite imagery, visual information, text, or unstructured data.

Adaptability

AI systems have the ability to learn and adapt as they make decisions. In the transportation area, for example, semi-autonomous vehicles have tools that let drivers and vehicles know about upcoming congestion, potholes, highway construction, or other possible traffic impediments. Vehicles can take advantage of the experience of other vehicles on the road, without human involvement, and the entire corpus of their achieved “experience” is immediately and fully transferable to other similarly configured vehicles. Their advanced algorithms, sensors, and cameras incorporate experience in current operations, and use dashboards and visual displays to present information in real time so human drivers are able to make sense of ongoing traffic and vehicular conditions. And in the case of fully autonomous vehicles, advanced systems can completely control the car or truck, and make all the navigational decisions.

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Applications in diverse sectors

AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities. There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways. 6

One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by PriceWaterhouseCoopers estimated that “artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030.” 7 That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Meanwhile, a McKinsey Global Institute study of China found that “AI-led automation can give the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth annually, depending on the speed of adoption.” 8 Although its authors found that China currently lags the United States and the United Kingdom in AI deployment, the sheer size of its AI market gives that country tremendous opportunities for pilot testing and future development.

Investments in financial AI in the United States tripled between 2013 and 2014 to a total of $12.2 billion. 9 According to observers in that sector, “Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check.” 10 In addition, there are so-called robo-advisers that “create personalized investment portfolios, obviating the need for stockbrokers and financial advisers.” 11 These advances are designed to take the emotion out of investing and undertake decisions based on analytical considerations, and make these choices in a matter of minutes.

A prominent example of this is taking place in stock exchanges, where high-frequency trading by machines has replaced much of human decisionmaking. People submit buy and sell orders, and computers match them in the blink of an eye without human intervention. Machines can spot trading inefficiencies or market differentials on a very small scale and execute trades that make money according to investor instructions. 12 Powered in some places by advanced computing, these tools have much greater capacities for storing information because of their emphasis not on a zero or a one, but on “quantum bits” that can store multiple values in each location. 13 That dramatically increases storage capacity and decreases processing times.

Fraud detection represents another way AI is helpful in financial systems. It sometimes is difficult to discern fraudulent activities in large organizations, but AI can identify abnormalities, outliers, or deviant cases requiring additional investigation. That helps managers find problems early in the cycle, before they reach dangerous levels. 14

National security

AI plays a substantial role in national defense. Through its Project Maven, the American military is deploying AI “to sift through the massive troves of data and video captured by surveillance and then alert human analysts of patterns or when there is abnormal or suspicious activity.” 15 According to Deputy Secretary of Defense Patrick Shanahan, the goal of emerging technologies in this area is “to meet our warfighters’ needs and to increase [the] speed and agility [of] technology development and procurement.” 16

Artificial intelligence will accelerate the traditional process of warfare so rapidly that a new term has been coined: hyperwar.

The big data analytics associated with AI will profoundly affect intelligence analysis, as massive amounts of data are sifted in near real time—if not eventually in real time—thereby providing commanders and their staffs a level of intelligence analysis and productivity heretofore unseen. Command and control will similarly be affected as human commanders delegate certain routine, and in special circumstances, key decisions to AI platforms, reducing dramatically the time associated with the decision and subsequent action. In the end, warfare is a time competitive process, where the side able to decide the fastest and move most quickly to execution will generally prevail. Indeed, artificially intelligent intelligence systems, tied to AI-assisted command and control systems, can move decision support and decisionmaking to a speed vastly superior to the speeds of the traditional means of waging war. So fast will be this process, especially if coupled to automatic decisions to launch artificially intelligent autonomous weapons systems capable of lethal outcomes, that a new term has been coined specifically to embrace the speed at which war will be waged: hyperwar.

While the ethical and legal debate is raging over whether America will ever wage war with artificially intelligent autonomous lethal systems, the Chinese and Russians are not nearly so mired in this debate, and we should anticipate our need to defend against these systems operating at hyperwar speeds. The challenge in the West of where to position “humans in the loop” in a hyperwar scenario will ultimately dictate the West’s capacity to be competitive in this new form of conflict. 17

Just as AI will profoundly affect the speed of warfare, the proliferation of zero day or zero second cyber threats as well as polymorphic malware will challenge even the most sophisticated signature-based cyber protection. This forces significant improvement to existing cyber defenses. Increasingly, vulnerable systems are migrating, and will need to shift to a layered approach to cybersecurity with cloud-based, cognitive AI platforms. This approach moves the community toward a “thinking” defensive capability that can defend networks through constant training on known threats. This capability includes DNA-level analysis of heretofore unknown code, with the possibility of recognizing and stopping inbound malicious code by recognizing a string component of the file. This is how certain key U.S.-based systems stopped the debilitating “WannaCry” and “Petya” viruses.

Preparing for hyperwar and defending critical cyber networks must become a high priority because China, Russia, North Korea, and other countries are putting substantial resources into AI. In 2017, China’s State Council issued a plan for the country to “build a domestic industry worth almost $150 billion” by 2030. 18 As an example of the possibilities, the Chinese search firm Baidu has pioneered a facial recognition application that finds missing people. In addition, cities such as Shenzhen are providing up to $1 million to support AI labs. That country hopes AI will provide security, combat terrorism, and improve speech recognition programs. 19 The dual-use nature of many AI algorithms will mean AI research focused on one sector of society can be rapidly modified for use in the security sector as well. 20

Health care

AI tools are helping designers improve computational sophistication in health care. For example, Merantix is a German company that applies deep learning to medical issues. It has an application in medical imaging that “detects lymph nodes in the human body in Computer Tomography (CT) images.” 21 According to its developers, the key is labeling the nodes and identifying small lesions or growths that could be problematic. Humans can do this, but radiologists charge $100 per hour and may be able to carefully read only four images an hour. If there were 10,000 images, the cost of this process would be $250,000, which is prohibitively expensive if done by humans.

What deep learning can do in this situation is train computers on data sets to learn what a normal-looking versus an irregular-appearing lymph node is. After doing that through imaging exercises and honing the accuracy of the labeling, radiological imaging specialists can apply this knowledge to actual patients and determine the extent to which someone is at risk of cancerous lymph nodes. Since only a few are likely to test positive, it is a matter of identifying the unhealthy versus healthy node.

AI has been applied to congestive heart failure as well, an illness that afflicts 10 percent of senior citizens and costs $35 billion each year in the United States. AI tools are helpful because they “predict in advance potential challenges ahead and allocate resources to patient education, sensing, and proactive interventions that keep patients out of the hospital.” 22

Criminal justice

AI is being deployed in the criminal justice area. The city of Chicago has developed an AI-driven “Strategic Subject List” that analyzes people who have been arrested for their risk of becoming future perpetrators. It ranks 400,000 people on a scale of 0 to 500, using items such as age, criminal activity, victimization, drug arrest records, and gang affiliation. In looking at the data, analysts found that youth is a strong predictor of violence, being a shooting victim is associated with becoming a future perpetrator, gang affiliation has little predictive value, and drug arrests are not significantly associated with future criminal activity. 23

Judicial experts claim AI programs reduce human bias in law enforcement and leads to a fairer sentencing system. R Street Institute Associate Caleb Watney writes:

Empirically grounded questions of predictive risk analysis play to the strengths of machine learning, automated reasoning and other forms of AI. One machine-learning policy simulation concluded that such programs could be used to cut crime up to 24.8 percent with no change in jailing rates, or reduce jail populations by up to 42 percent with no increase in crime rates. 24

However, critics worry that AI algorithms represent “a secret system to punish citizens for crimes they haven’t yet committed. The risk scores have been used numerous times to guide large-scale roundups.” 25 The fear is that such tools target people of color unfairly and have not helped Chicago reduce the murder wave that has plagued it in recent years.

Despite these concerns, other countries are moving ahead with rapid deployment in this area. In China, for example, companies already have “considerable resources and access to voices, faces and other biometric data in vast quantities, which would help them develop their technologies.” 26 New technologies make it possible to match images and voices with other types of information, and to use AI on these combined data sets to improve law enforcement and national security. Through its “Sharp Eyes” program, Chinese law enforcement is matching video images, social media activity, online purchases, travel records, and personal identity into a “police cloud.” This integrated database enables authorities to keep track of criminals, potential law-breakers, and terrorists. 27 Put differently, China has become the world’s leading AI-powered surveillance state.

Transportation

Transportation represents an area where AI and machine learning are producing major innovations. Research by Cameron Kerry and Jack Karsten of the Brookings Institution has found that over $80 billion was invested in autonomous vehicle technology between August 2014 and June 2017. Those investments include applications both for autonomous driving and the core technologies vital to that sector. 28

Autonomous vehicles—cars, trucks, buses, and drone delivery systems—use advanced technological capabilities. Those features include automated vehicle guidance and braking, lane-changing systems, the use of cameras and sensors for collision avoidance, the use of AI to analyze information in real time, and the use of high-performance computing and deep learning systems to adapt to new circumstances through detailed maps. 29

Light detection and ranging systems (LIDARs) and AI are key to navigation and collision avoidance. LIDAR systems combine light and radar instruments. They are mounted on the top of vehicles that use imaging in a 360-degree environment from a radar and light beams to measure the speed and distance of surrounding objects. Along with sensors placed on the front, sides, and back of the vehicle, these instruments provide information that keeps fast-moving cars and trucks in their own lane, helps them avoid other vehicles, applies brakes and steering when needed, and does so instantly so as to avoid accidents.

Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. This means that software is the key—not the physical car or truck itself.

Since these cameras and sensors compile a huge amount of information and need to process it instantly to avoid the car in the next lane, autonomous vehicles require high-performance computing, advanced algorithms, and deep learning systems to adapt to new scenarios. This means that software is the key, not the physical car or truck itself. 30 Advanced software enables cars to learn from the experiences of other vehicles on the road and adjust their guidance systems as weather, driving, or road conditions change. 31

Ride-sharing companies are very interested in autonomous vehicles. They see advantages in terms of customer service and labor productivity. All of the major ride-sharing companies are exploring driverless cars. The surge of car-sharing and taxi services—such as Uber and Lyft in the United States, Daimler’s Mytaxi and Hailo service in Great Britain, and Didi Chuxing in China—demonstrate the opportunities of this transportation option. Uber recently signed an agreement to purchase 24,000 autonomous cars from Volvo for its ride-sharing service. 32

However, the ride-sharing firm suffered a setback in March 2018 when one of its autonomous vehicles in Arizona hit and killed a pedestrian. Uber and several auto manufacturers immediately suspended testing and launched investigations into what went wrong and how the fatality could have occurred. 33 Both industry and consumers want reassurance that the technology is safe and able to deliver on its stated promises. Unless there are persuasive answers, this accident could slow AI advancements in the transportation sector.

Smart cities

Metropolitan governments are using AI to improve urban service delivery. For example, according to Kevin Desouza, Rashmi Krishnamurthy, and Gregory Dawson:

The Cincinnati Fire Department is using data analytics to optimize medical emergency responses. The new analytics system recommends to the dispatcher an appropriate response to a medical emergency call—whether a patient can be treated on-site or needs to be taken to the hospital—by taking into account several factors, such as the type of call, location, weather, and similar calls. 34

Since it fields 80,000 requests each year, Cincinnati officials are deploying this technology to prioritize responses and determine the best ways to handle emergencies. They see AI as a way to deal with large volumes of data and figure out efficient ways of responding to public requests. Rather than address service issues in an ad hoc manner, authorities are trying to be proactive in how they provide urban services.

Cincinnati is not alone. A number of metropolitan areas are adopting smart city applications that use AI to improve service delivery, environmental planning, resource management, energy utilization, and crime prevention, among other things. For its smart cities index, the magazine Fast Company ranked American locales and found Seattle, Boston, San Francisco, Washington, D.C., and New York City as the top adopters. Seattle, for example, has embraced sustainability and is using AI to manage energy usage and resource management. Boston has launched a “City Hall To Go” that makes sure underserved communities receive needed public services. It also has deployed “cameras and inductive loops to manage traffic and acoustic sensors to identify gun shots.” San Francisco has certified 203 buildings as meeting LEED sustainability standards. 35

Through these and other means, metropolitan areas are leading the country in the deployment of AI solutions. Indeed, according to a National League of Cities report, 66 percent of American cities are investing in smart city technology. Among the top applications noted in the report are “smart meters for utilities, intelligent traffic signals, e-governance applications, Wi-Fi kiosks, and radio frequency identification sensors in pavement.” 36

Policy, regulatory, and ethical issues

These examples from a variety of sectors demonstrate how AI is transforming many walks of human existence. The increasing penetration of AI and autonomous devices into many aspects of life is altering basic operations and decisionmaking within organizations, and improving efficiency and response times.

At the same time, though, these developments raise important policy, regulatory, and ethical issues. For example, how should we promote data access? How do we guard against biased or unfair data used in algorithms? What types of ethical principles are introduced through software programming, and how transparent should designers be about their choices? What about questions of legal liability in cases where algorithms cause harm? 37

The increasing penetration of AI into many aspects of life is altering decisionmaking within organizations and improving efficiency. At the same time, though, these developments raise important policy, regulatory, and ethical issues.

Data access problems

The key to getting the most out of AI is having a “data-friendly ecosystem with unified standards and cross-platform sharing.” AI depends on data that can be analyzed in real time and brought to bear on concrete problems. Having data that are “accessible for exploration” in the research community is a prerequisite for successful AI development. 38

According to a McKinsey Global Institute study, nations that promote open data sources and data sharing are the ones most likely to see AI advances. In this regard, the United States has a substantial advantage over China. Global ratings on data openness show that U.S. ranks eighth overall in the world, compared to 93 for China. 39

But right now, the United States does not have a coherent national data strategy. There are few protocols for promoting research access or platforms that make it possible to gain new insights from proprietary data. It is not always clear who owns data or how much belongs in the public sphere. These uncertainties limit the innovation economy and act as a drag on academic research. In the following section, we outline ways to improve data access for researchers.

Biases in data and algorithms

In some instances, certain AI systems are thought to have enabled discriminatory or biased practices. 40 For example, Airbnb has been accused of having homeowners on its platform who discriminate against racial minorities. A research project undertaken by the Harvard Business School found that “Airbnb users with distinctly African American names were roughly 16 percent less likely to be accepted as guests than those with distinctly white names.” 41

Racial issues also come up with facial recognition software. Most such systems operate by comparing a person’s face to a range of faces in a large database. As pointed out by Joy Buolamwini of the Algorithmic Justice League, “If your facial recognition data contains mostly Caucasian faces, that’s what your program will learn to recognize.” 42 Unless the databases have access to diverse data, these programs perform poorly when attempting to recognize African-American or Asian-American features.

Many historical data sets reflect traditional values, which may or may not represent the preferences wanted in a current system. As Buolamwini notes, such an approach risks repeating inequities of the past:

The rise of automation and the increased reliance on algorithms for high-stakes decisions such as whether someone get insurance or not, your likelihood to default on a loan or somebody’s risk of recidivism means this is something that needs to be addressed. Even admissions decisions are increasingly automated—what school our children go to and what opportunities they have. We don’t have to bring the structural inequalities of the past into the future we create. 43

AI ethics and transparency

Algorithms embed ethical considerations and value choices into program decisions. As such, these systems raise questions concerning the criteria used in automated decisionmaking. Some people want to have a better understanding of how algorithms function and what choices are being made. 44

In the United States, many urban schools use algorithms for enrollment decisions based on a variety of considerations, such as parent preferences, neighborhood qualities, income level, and demographic background. According to Brookings researcher Jon Valant, the New Orleans–based Bricolage Academy “gives priority to economically disadvantaged applicants for up to 33 percent of available seats. In practice, though, most cities have opted for categories that prioritize siblings of current students, children of school employees, and families that live in school’s broad geographic area.” 45 Enrollment choices can be expected to be very different when considerations of this sort come into play.

Depending on how AI systems are set up, they can facilitate the redlining of mortgage applications, help people discriminate against individuals they don’t like, or help screen or build rosters of individuals based on unfair criteria. The types of considerations that go into programming decisions matter a lot in terms of how the systems operate and how they affect customers. 46

For these reasons, the EU is implementing the General Data Protection Regulation (GDPR) in May 2018. The rules specify that people have “the right to opt out of personally tailored ads” and “can contest ‘legal or similarly significant’ decisions made by algorithms and appeal for human intervention” in the form of an explanation of how the algorithm generated a particular outcome. Each guideline is designed to ensure the protection of personal data and provide individuals with information on how the “black box” operates. 47

Legal liability

There are questions concerning the legal liability of AI systems. If there are harms or infractions (or fatalities in the case of driverless cars), the operators of the algorithm likely will fall under product liability rules. A body of case law has shown that the situation’s facts and circumstances determine liability and influence the kind of penalties that are imposed. Those can range from civil fines to imprisonment for major harms. 48 The Uber-related fatality in Arizona will be an important test case for legal liability. The state actively recruited Uber to test its autonomous vehicles and gave the company considerable latitude in terms of road testing. It remains to be seen if there will be lawsuits in this case and who is sued: the human backup driver, the state of Arizona, the Phoenix suburb where the accident took place, Uber, software developers, or the auto manufacturer. Given the multiple people and organizations involved in the road testing, there are many legal questions to be resolved.

In non-transportation areas, digital platforms often have limited liability for what happens on their sites. For example, in the case of Airbnb, the firm “requires that people agree to waive their right to sue, or to join in any class-action lawsuit or class-action arbitration, to use the service.” By demanding that its users sacrifice basic rights, the company limits consumer protections and therefore curtails the ability of people to fight discrimination arising from unfair algorithms. 49 But whether the principle of neutral networks holds up in many sectors is yet to be determined on a widespread basis.

Recommendations

In order to balance innovation with basic human values, we propose a number of recommendations for moving forward with AI. This includes improving data access, increasing government investment in AI, promoting AI workforce development, creating a federal advisory committee, engaging with state and local officials to ensure they enact effective policies, regulating broad objectives as opposed to specific algorithms, taking bias seriously as an AI issue, maintaining mechanisms for human control and oversight, and penalizing malicious behavior and promoting cybersecurity.

Improving data access

The United States should develop a data strategy that promotes innovation and consumer protection. Right now, there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design. AI requires data to test and improve its learning capacity. 50 Without structured and unstructured data sets, it will be nearly impossible to gain the full benefits of artificial intelligence.

In general, the research community needs better access to government and business data, although with appropriate safeguards to make sure researchers do not misuse data in the way Cambridge Analytica did with Facebook information. There is a variety of ways researchers could gain data access. One is through voluntary agreements with companies holding proprietary data. Facebook, for example, recently announced a partnership with Stanford economist Raj Chetty to use its social media data to explore inequality. 51 As part of the arrangement, researchers were required to undergo background checks and could only access data from secured sites in order to protect user privacy and security.

In the U.S., there are no uniform standards in terms of data access, data sharing, or data protection. Almost all the data are proprietary in nature and not shared very broadly with the research community, and this limits innovation and system design.

Google long has made available search results in aggregated form for researchers and the general public. Through its “Trends” site, scholars can analyze topics such as interest in Trump, views about democracy, and perspectives on the overall economy. 52 That helps people track movements in public interest and identify topics that galvanize the general public.

Twitter makes much of its tweets available to researchers through application programming interfaces, commonly referred to as APIs. These tools help people outside the company build application software and make use of data from its social media platform. They can study patterns of social media communications and see how people are commenting on or reacting to current events.

In some sectors where there is a discernible public benefit, governments can facilitate collaboration by building infrastructure that shares data. For example, the National Cancer Institute has pioneered a data-sharing protocol where certified researchers can query health data it has using de-identified information drawn from clinical data, claims information, and drug therapies. That enables researchers to evaluate efficacy and effectiveness, and make recommendations regarding the best medical approaches, without compromising the privacy of individual patients.

There could be public-private data partnerships that combine government and business data sets to improve system performance. For example, cities could integrate information from ride-sharing services with its own material on social service locations, bus lines, mass transit, and highway congestion to improve transportation. That would help metropolitan areas deal with traffic tie-ups and assist in highway and mass transit planning.

Some combination of these approaches would improve data access for researchers, the government, and the business community, without impinging on personal privacy. As noted by Ian Buck, the vice president of NVIDIA, “Data is the fuel that drives the AI engine. The federal government has access to vast sources of information. Opening access to that data will help us get insights that will transform the U.S. economy.” 53 Through its Data.gov portal, the federal government already has put over 230,000 data sets into the public domain, and this has propelled innovation and aided improvements in AI and data analytic technologies. 54 The private sector also needs to facilitate research data access so that society can achieve the full benefits of artificial intelligence.

Increase government investment in AI

According to Greg Brockman, the co-founder of OpenAI, the U.S. federal government invests only $1.1 billion in non-classified AI technology. 55 That is far lower than the amount being spent by China or other leading nations in this area of research. That shortfall is noteworthy because the economic payoffs of AI are substantial. In order to boost economic development and social innovation, federal officials need to increase investment in artificial intelligence and data analytics. Higher investment is likely to pay for itself many times over in economic and social benefits. 56

Promote digital education and workforce development

As AI applications accelerate across many sectors, it is vital that we reimagine our educational institutions for a world where AI will be ubiquitous and students need a different kind of training than they currently receive. Right now, many students do not receive instruction in the kinds of skills that will be needed in an AI-dominated landscape. For example, there currently are shortages of data scientists, computer scientists, engineers, coders, and platform developers. These are skills that are in short supply; unless our educational system generates more people with these capabilities, it will limit AI development.

For these reasons, both state and federal governments have been investing in AI human capital. For example, in 2017, the National Science Foundation funded over 6,500 graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education. 57 The goal is to build a larger pipeline of AI and data analytic personnel so that the United States can reap the full advantages of the knowledge revolution.

But there also needs to be substantial changes in the process of learning itself. It is not just technical skills that are needed in an AI world but skills of critical reasoning, collaboration, design, visual display of information, and independent thinking, among others. AI will reconfigure how society and the economy operate, and there needs to be “big picture” thinking on what this will mean for ethics, governance, and societal impact. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas.

One example of new ways to prepare students for a digital future is IBM’s Teacher Advisor program, utilizing Watson’s free online tools to help teachers bring the latest knowledge into the classroom. They enable instructors to develop new lesson plans in STEM and non-STEM fields, find relevant instructional videos, and help students get the most out of the classroom. 58 As such, they are precursors of new educational environments that need to be created.

Create a federal AI advisory committee

Federal officials need to think about how they deal with artificial intelligence. As noted previously, there are many issues ranging from the need for improved data access to addressing issues of bias and discrimination. It is vital that these and other concerns be considered so we gain the full benefits of this emerging technology.

In order to move forward in this area, several members of Congress have introduced the “Future of Artificial Intelligence Act,” a bill designed to establish broad policy and legal principles for AI. It proposes the secretary of commerce create a federal advisory committee on the development and implementation of artificial intelligence. The legislation provides a mechanism for the federal government to get advice on ways to promote a “climate of investment and innovation to ensure the global competitiveness of the United States,” “optimize the development of artificial intelligence to address the potential growth, restructuring, or other changes in the United States workforce,” “support the unbiased development and application of artificial intelligence,” and “protect the privacy rights of individuals.” 59

Among the specific questions the committee is asked to address include the following: competitiveness, workforce impact, education, ethics training, data sharing, international cooperation, accountability, machine learning bias, rural impact, government efficiency, investment climate, job impact, bias, and consumer impact. The committee is directed to submit a report to Congress and the administration 540 days after enactment regarding any legislative or administrative action needed on AI.

This legislation is a step in the right direction, although the field is moving so rapidly that we would recommend shortening the reporting timeline from 540 days to 180 days. Waiting nearly two years for a committee report will certainly result in missed opportunities and a lack of action on important issues. Given rapid advances in the field, having a much quicker turnaround time on the committee analysis would be quite beneficial.

Engage with state and local officials

States and localities also are taking action on AI. For example, the New York City Council unanimously passed a bill that directed the mayor to form a taskforce that would “monitor the fairness and validity of algorithms used by municipal agencies.” 60 The city employs algorithms to “determine if a lower bail will be assigned to an indigent defendant, where firehouses are established, student placement for public schools, assessing teacher performance, identifying Medicaid fraud and determine where crime will happen next.” 61

According to the legislation’s developers, city officials want to know how these algorithms work and make sure there is sufficient AI transparency and accountability. In addition, there is concern regarding the fairness and biases of AI algorithms, so the taskforce has been directed to analyze these issues and make recommendations regarding future usage. It is scheduled to report back to the mayor on a range of AI policy, legal, and regulatory issues by late 2019.

Some observers already are worrying that the taskforce won’t go far enough in holding algorithms accountable. For example, Julia Powles of Cornell Tech and New York University argues that the bill originally required companies to make the AI source code available to the public for inspection, and that there be simulations of its decisionmaking using actual data. After criticism of those provisions, however, former Councilman James Vacca dropped the requirements in favor of a task force studying these issues. He and other city officials were concerned that publication of proprietary information on algorithms would slow innovation and make it difficult to find AI vendors who would work with the city. 62 It remains to be seen how this local task force will balance issues of innovation, privacy, and transparency.

Regulate broad objectives more than specific algorithms

The European Union has taken a restrictive stance on these issues of data collection and analysis. 63 It has rules limiting the ability of companies from collecting data on road conditions and mapping street views. Because many of these countries worry that people’s personal information in unencrypted Wi-Fi networks are swept up in overall data collection, the EU has fined technology firms, demanded copies of data, and placed limits on the material collected. 64 This has made it more difficult for technology companies operating there to develop the high-definition maps required for autonomous vehicles.

The GDPR being implemented in Europe place severe restrictions on the use of artificial intelligence and machine learning. According to published guidelines, “Regulations prohibit any automated decision that ‘significantly affects’ EU citizens. This includes techniques that evaluates a person’s ‘performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location, or movements.’” 65 In addition, these new rules give citizens the right to review how digital services made specific algorithmic choices affecting people.

By taking a restrictive stance on issues of data collection and analysis, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

If interpreted stringently, these rules will make it difficult for European software designers (and American designers who work with European counterparts) to incorporate artificial intelligence and high-definition mapping in autonomous vehicles. Central to navigation in these cars and trucks is tracking location and movements. Without high-definition maps containing geo-coded data and the deep learning that makes use of this information, fully autonomous driving will stagnate in Europe. Through this and other data protection actions, the European Union is putting its manufacturers and software designers at a significant disadvantage to the rest of the world.

It makes more sense to think about the broad objectives desired in AI and enact policies that advance them, as opposed to governments trying to crack open the “black boxes” and see exactly how specific algorithms operate. Regulating individual algorithms will limit innovation and make it difficult for companies to make use of artificial intelligence.

Take biases seriously

Bias and discrimination are serious issues for AI. There already have been a number of cases of unfair treatment linked to historic data, and steps need to be undertaken to make sure that does not become prevalent in artificial intelligence. Existing statutes governing discrimination in the physical economy need to be extended to digital platforms. That will help protect consumers and build confidence in these systems as a whole.

For these advances to be widely adopted, more transparency is needed in how AI systems operate. Andrew Burt of Immuta argues, “The key problem confronting predictive analytics is really transparency. We’re in a world where data science operations are taking on increasingly important tasks, and the only thing holding them back is going to be how well the data scientists who train the models can explain what it is their models are doing.” 66

Maintaining mechanisms for human oversight and control

Some individuals have argued that there needs to be avenues for humans to exercise oversight and control of AI systems. For example, Allen Institute for Artificial Intelligence CEO Oren Etzioni argues there should be rules for regulating these systems. First, he says, AI must be governed by all the laws that already have been developed for human behavior, including regulations concerning “cyberbullying, stock manipulation or terrorist threats,” as well as “entrap[ping] people into committing crimes.” Second, he believes that these systems should disclose they are automated systems and not human beings. Third, he states, “An A.I. system cannot retain or disclose confidential information without explicit approval from the source of that information.” 67 His rationale is that these tools store so much data that people have to be cognizant of the privacy risks posed by AI.

In the same vein, the IEEE Global Initiative has ethical guidelines for AI and autonomous systems. Its experts suggest that these models be programmed with consideration for widely accepted human norms and rules for behavior. AI algorithms need to take into effect the importance of these norms, how norm conflict can be resolved, and ways these systems can be transparent about norm resolution. Software designs should be programmed for “nondeception” and “honesty,” according to ethics experts. When failures occur, there must be mitigation mechanisms to deal with the consequences. In particular, AI must be sensitive to problems such as bias, discrimination, and fairness. 68

A group of machine learning experts claim it is possible to automate ethical decisionmaking. Using the trolley problem as a moral dilemma, they ask the following question: If an autonomous car goes out of control, should it be programmed to kill its own passengers or the pedestrians who are crossing the street? They devised a “voting-based system” that asked 1.3 million people to assess alternative scenarios, summarized the overall choices, and applied the overall perspective of these individuals to a range of vehicular possibilities. That allowed them to automate ethical decisionmaking in AI algorithms, taking public preferences into account. 69 This procedure, of course, does not reduce the tragedy involved in any kind of fatality, such as seen in the Uber case, but it provides a mechanism to help AI developers incorporate ethical considerations in their planning.

Penalize malicious behavior and promote cybersecurity

As with any emerging technology, it is important to discourage malicious treatment designed to trick software or use it for undesirable ends. 70 This is especially important given the dual-use aspects of AI, where the same tool can be used for beneficial or malicious purposes. The malevolent use of AI exposes individuals and organizations to unnecessary risks and undermines the virtues of the emerging technology. This includes behaviors such as hacking, manipulating algorithms, compromising privacy and confidentiality, or stealing identities. Efforts to hijack AI in order to solicit confidential information should be seriously penalized as a way to deter such actions. 71

In a rapidly changing world with many entities having advanced computing capabilities, there needs to be serious attention devoted to cybersecurity. Countries have to be careful to safeguard their own systems and keep other nations from damaging their security. 72 According to the U.S. Department of Homeland Security, a major American bank receives around 11 million calls a week at its service center. In order to protect its telephony from denial of service attacks, it uses a “machine learning-based policy engine [that] blocks more than 120,000 calls per month based on voice firewall policies including harassing callers, robocalls and potential fraudulent calls.” 73 This represents a way in which machine learning can help defend technology systems from malevolent attacks.

To summarize, the world is on the cusp of revolutionizing many sectors through artificial intelligence and data analytics. There already are significant deployments in finance, national security, health care, criminal justice, transportation, and smart cities that have altered decisionmaking, business models, risk mitigation, and system performance. These developments are generating substantial economic and social benefits.

The world is on the cusp of revolutionizing many sectors through artificial intelligence, but the way AI systems are developed need to be better understood due to the major implications these technologies will have for society as a whole.

Yet the manner in which AI systems unfold has major implications for society as a whole. It matters how policy issues are addressed, ethical conflicts are reconciled, legal realities are resolved, and how much transparency is required in AI and data analytic solutions. 74 Human choices about software development affect the way in which decisions are made and the manner in which they are integrated into organizational routines. Exactly how these processes are executed need to be better understood because they will have substantial impact on the general public soon, and for the foreseeable future. AI may well be a revolution in human affairs, and become the single most influential human innovation in history.

Note: We appreciate the research assistance of Grace Gilberg, Jack Karsten, Hillary Schaub, and Kristjan Tomasson on this project.

The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.

Support for this publication was generously provided by Amazon. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment. 

John R. Allen is a member of the Board of Advisors of Amida Technology and on the Board of Directors of Spark Cognition. Both companies work in fields discussed in this piece.

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• Paul Mozur and John Markoff, “Is China Outsmarting American Artificial Intelligence?” New York Times , May 28, 2017.
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• Portions of this section are drawn from Darrell M. West, “Driverless Cars in China, Europe, Japan, Korea, and the United States,” Brookings Institution, September 2016.
• Yuming Ge, Xiaoman Liu, Libo Tang, and Darrell M. West, “Smart Transportation in China and the United States,” Center for Technology Innovation, Brookings Institution, December 2017.
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• Ibid., p. 7.
• Dominic Barton, Jonathan Woetzel, Jeongmin Seong, and Qinzheng Tian, “Artificial Intelligence: Implications for China” (New York: McKinsey Global Institute, April 2017), p. 7.
• Executive Office of the President, “Preparing for the Future of Artificial Intelligence,” October 2016, pp. 30-31.
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• Jon Valant, “Integrating Charter Schools and Choice-Based Education Systems,” Brown Center Chalkboard blog, Brookings Institution, June 23, 2017.
• Tucker, “‘A White Mask Worked Better.’”
• Cliff Kuang, “Can A.I. Be Taught to Explain Itself?” New York Times Magazine , November 21, 2017.
• Yale Law School Information Society Project, “Governing Machine Learning,” September 2017.
• Katie Benner, “Airbnb Vows to Fight Racism, But Its Users Can’t Sue to Prompt Fairness,” New York Times , June 19, 2016.
• Executive Office of the President, “Artificial Intelligence, Automation, and the Economy” and “Preparing for the Future of Artificial Intelligence.”
• Nancy Scolar, “Facebook’s Next Project: American Inequality,” Politico , February 19, 2018.
• Darrell M. West, “What Internet Search Data Reveals about Donald Trump’s First Year in Office,” Brookings Institution policy report, January 17, 2018.
• Ian Buck, “Testimony before the House Committee on Oversight and Government Reform Subcommittee on Information Technology,” February 14, 2018.
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• Greg Brockman, “The Dawn of Artificial Intelligence,” Testimony before U.S. Senate Subcommittee on Space, Science, and Competitiveness, November 30, 2016.
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• Eric Siegel, “Predictive Analytics Interview Series: Andrew Burt,” Predictive Analytics Times , June 14, 2017.
• Oren Etzioni, “How to Regulate Artificial Intelligence,” New York Times , September 1, 2017.
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• Miles Brundage, et al., “The Malicious Use of Artificial Intelligence,” University of Oxford unpublished paper, February 2018.
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• Artificial Intelligence Essay

Essay on Artificial Intelligence

Artificial Intelligence is the intelligence possessed by the machines under which they can perform various functions with human help. With the help of A.I, machines will be able to learn, solve problems, plan things, think, etc. Artificial Intelligence, for example, is the simulation of human intelligence by machines. In the field of technology, Artificial Intelligence is evolving rapidly day by day and it is believed that in the near future, artificial intelligence is going to change human life very drastically and will most probably end all the crises of the world by sorting out the major problems. 

Our life in this modern age depends largely on computers. It is almost impossible to think about life without computers. We need computers in everything that we use in our daily lives. So it becomes very important to make computers intelligent so that our lives become easy. Artificial Intelligence is the theory and development of computers, which imitates the human intelligence and senses, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has brought a revolution in the world of technology. 

Artificial Intelligence Applications

AI is widely used in the field of healthcare. Companies are attempting to develop technologies that will allow for rapid diagnosis. Artificial Intelligence would be able to operate on patients without the need for human oversight. Surgical procedures based on technology are already being performed.

Artificial Intelligence would save a lot of our time. The use of robots would decrease human labour. For example, in industries robots are used which have saved a lot of human effort and time. 

In the field of education, AI has the potential to be very effective. It can bring innovative ways of teaching students with the help of which students will be able to learn the concepts better. 

Artificial intelligence is the future of innovative technology as we can use it in many fields. For example, it can be used in the Military sector, Industrial sector, Automobiles, etc. In the coming years, we will be able to see more applications of AI as this technology is evolving day by day. 

Marketing: Artificial Intelligence provides a deep knowledge of consumers and potential clients to the marketers by enabling them to deliver information at the right time. Through AI solutions, the marketers can refine their campaigns and strategies.

Agriculture: AI technology can be used to detect diseases in plants, pests, and poor plant nutrition. With the help of AI, farmers can analyze the weather conditions, temperature, water usage, and condition of the soil.

Banking: Fraudulent activities can be detected through AI solutions. AI bots, digital payment advisers can create a high quality of service.

Health Care: Artificial Intelligence can surpass human cognition in the analysis, diagnosis, and complication of complicated medical data.

History of Artificial Intelligence

Artificial Intelligence may seem to be a new technology but if we do a bit of research, we will find that it has roots deep in the past. In Greek Mythology, it is said that the concepts of AI were used. 

The model of Artificial neurons was first brought forward in 1943 by Warren McCulloch and Walter Pits. After seven years, in 1950, a research paper related to AI was published by Alan Turing which was titled 'Computer Machinery and Intelligence. The term Artificial Intelligence was first coined in 1956 by John McCarthy, who is known as the father of Artificial Intelligence. 

To conclude, we can say that Artificial Intelligence will be the future of the world. As per the experts, we won't be able to separate ourselves from this technology as it would become an integral part of our lives shortly. AI would change the way we live in this world. This technology would prove to be revolutionary because it will change our lives for good. 

Branches of Artificial Intelligence:

Knowledge Engineering

Machines Learning

Natural Language Processing

Types of Artificial Intelligence

Artificial Intelligence is categorized in two types based on capabilities and functionalities. 

Artificial Intelligence Type-1

Artificial intelligence type-2.

Narrow AI (weak AI): This is designed to perform a specific task with intelligence. It is termed as weak AI because it cannot perform beyond its limitations. It is trained to do a specific task. Some examples of Narrow AI are facial recognition (Siri in Apple phones), speech, and image recognition. IBM’s Watson supercomputer, self-driving cars, playing chess, and solving equations are also some of the examples of weak AI.

General AI (AGI or strong AI): This system can perform nearly every cognitive task as efficiently as humans can do. The main characteristic of general AI is to make a system that can think like a human on its own. This is a long-term goal of many researchers to create such machines.

Super AI: Super AI is a type of intelligence of systems in which machines can surpass human intelligence and can perform any cognitive task better than humans. The main features of strong AI would be the ability to think, reason, solve puzzles, make judgments, plan and communicate on its own. The creation of strong AI might be the biggest revolution in human history.

Reactive Machines: These machines are the basic types of AI. Such AI systems focus only on current situations and react as per the best possible action. They do not store memories for future actions. IBM’s deep blue system and Google’s Alpha go are the examples of reactive machines.

Limited Memory: These machines can store data or past memories for a short period of time. Examples are self-driving cars. They can store information to navigate the road, speed, and distance of nearby cars.

Theory of Mind: These systems understand emotions, beliefs, and requirements like humans. These kinds of machines are still not invented and it’s a long-term goal for the researchers to create one. 

Self-Awareness: Self-awareness AI is the future of artificial intelligence. These machines can outsmart the humans. If these machines are invented then it can bring a revolution in human society. 

Artificial Intelligence will bring a huge revolution in the history of mankind. Human civilization will flourish by amplifying human intelligence with artificial intelligence, as long as we manage to keep the technology beneficial.

FAQs on Artificial Intelligence Essay

1. What is Artificial Intelligence?

Artificial Intelligence is a branch of computer science that emphasizes the development of intelligent machines that would think and work like humans.

2. How is Artificial Intelligence Categorised?

Artificial Intelligence is categorized in two types based on capabilities and functionalities. Based on capabilities, AI includes Narrow AI (weak AI), General AI, and super AI. Based on functionalities, AI includes Relative Machines, limited memory, theory of mind, self-awareness.

3. How Does AI Help in Marketing?

AI helps marketers to strategize their marketing campaigns and keep data of their prospective clients and consumers.

4. Give an Example of a Relative Machine?

IBM’s deep blue system and Google’s Alpha go are examples of reactive machines.

5. How can Artificial Intelligence help us?

Artificial Intelligence can help us in many ways. It is already helping us in some cases. For example, if we think about the robots used in a factory, they all run on the principle of Artificial Intelligence. In the automobile sector, some vehicles have been invented that don't need any humans to drive them, they are self-driving. The search engines these days are also AI-powered. There are many other uses of Artificial Intelligence as well.

Artificial Intelligence Essay

500+ words essay on artificial intelligence.

Artificial intelligence (AI) has come into our daily lives through mobile devices and the Internet. Governments and businesses are increasingly making use of AI tools and techniques to solve business problems and improve many business processes, especially online ones. Such developments bring about new realities to social life that may not have been experienced before. This essay on Artificial Intelligence will help students to know the various advantages of using AI and how it has made our lives easier and simpler. Also, in the end, we have described the future scope of AI and the harmful effects of using it. To get a good command of essay writing, students must practise CBSE Essays on different topics.

Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is concerned with getting computers to do tasks that would normally require human intelligence. AI systems are basically software systems (or controllers for robots) that use techniques such as machine learning and deep learning to solve problems in particular domains without hard coding all possibilities (i.e. algorithmic steps) in software. Due to this, AI started showing promising solutions for industry and businesses as well as our daily lives.

Importance and Advantages of Artificial Intelligence

Advances in computing and digital technologies have a direct influence on our lives, businesses and social life. This has influenced our daily routines, such as using mobile devices and active involvement on social media. AI systems are the most influential digital technologies. With AI systems, businesses are able to handle large data sets and provide speedy essential input to operations. Moreover, businesses are able to adapt to constant changes and are becoming more flexible.

By introducing Artificial Intelligence systems into devices, new business processes are opting for the automated process. A new paradigm emerges as a result of such intelligent automation, which now dictates not only how businesses operate but also who does the job. Many manufacturing sites can now operate fully automated with robots and without any human workers. Artificial Intelligence now brings unheard and unexpected innovations to the business world that many organizations will need to integrate to remain competitive and move further to lead the competitors.

Artificial Intelligence shapes our lives and social interactions through technological advancement. There are many AI applications which are specifically developed for providing better services to individuals, such as mobile phones, electronic gadgets, social media platforms etc. We are delegating our activities through intelligent applications, such as personal assistants, intelligent wearable devices and other applications. AI systems that operate household apparatus help us at home with cooking or cleaning.

Future Scope of Artificial Intelligence

In the future, intelligent machines will replace or enhance human capabilities in many areas. Artificial intelligence is becoming a popular field in computer science as it has enhanced humans. Application areas of artificial intelligence are having a huge impact on various fields of life to solve complex problems in various areas such as education, engineering, business, medicine, weather forecasting etc. Many labourers’ work can be done by a single machine. But Artificial Intelligence has another aspect: it can be dangerous for us. If we become completely dependent on machines, then it can ruin our life. We will not be able to do any work by ourselves and get lazy. Another disadvantage is that it cannot give a human-like feeling. So machines should be used only where they are actually required.

Students must have found this essay on “Artificial Intelligence” useful for improving their essay writing skills. They can get the study material and the latest updates on CBSE/ICSE/State Board/Competitive Exams, at BYJU’S.

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