# What is artificial intelligence?

What is the definition of artificial intelligence?

• We don't know how to define "artificial intelligence" probably because we don't know how to define "intelligence". – k.c. sayz 'k.c sayz' Oct 20 '19 at 3:36
• Perhaps the last sections of this fascinating paper would help: techrxiv.org/articles/… – Julia Jul 30 at 21:21

Over the years, many people attempted to define artificial intelligence. A lot of those definitions are summed up by Stuart Russell and Peter Norvig in their book Artificial Intelligence - A Modern Approach

The definitions of AI can be summarised as falling into the following categories:

1. Those that address thought process and reasoning (how an AI thinks/reasons)
2. Those that address behaviour (how an AI acts given what it knows)

Furthermore, the above 2 categories are further divided into definitions that:

I. assess the success of an AI (to do the above) based on its ability to replicate human performance

II. or an ability to replicate an ideal performance measure called 'rationality' (does it do the 'right' thing based on what it knows?)

I will cite you definitions that fit into each of the above categories:

• 1.I. "The [automation of] activities that we associate with human thinking, activities such as decision making, problem solving, learning.." - Bellman 1978
• 1.II. "The study of the computations that make it possible to perceive, reason, and act." - Winston, 1992
• 2.I. "The study of how to make computers do things at which, at the moment, people do better" - Rich and Knight, 1991
• 2.II. "The study of the design of intelligent agents" - Poole et al., 1998

In summary, AI is devoted to the creation of intelligent and rational machines that can make rational decisions and take rational actions.

I would suggest you read up on the Turing test, which Alan Turing proposed to test if a computer was intelligent. However, the Turing test has a few issues, because it is anthropomorphic.

When Aeronautical engineers created the airplane, they didn't set their goal that planes should fly exactly like birds, but rather, they started learning how lift forces were generated, based on the study of aerodynamics. Using this knowledge, they created planes.

Similarly, people in the AI world shouldn't put, IMHO, human intelligence as the standard to strive for, but, rather, we could use, say, rationality as a standard (amongst others).

• Comments are not for extended discussion; this conversation has been moved to chat. – nbro Mar 12 at 19:50

What is artificial intelligence?

This question is ambiguous. I will address the two less ambiguous but related questions.

1. What is the goal of the AI field?
2. What is an artificial intelligence?

## What is the goal of the AI field?

In the article What is artificial intelligence? (2007), John McCarthy, one of the founders of artificial intelligence and who also coined the expression artificial intelligence, writes

Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs.

It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.

Therefore, the goal of the AI field is to create intelligent programs (or machines). So, he defines the goal of the field based on the concept of intelligence, which he defines as follows.

Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.

So, we could conclude that the goal of the AI field is to create programs (or machines) that achieve goals in the world to different extents.

This definition is of intelligence is reasonable, but maybe not formal and rigorous enough. In this answer, I report a possibly more sound definition of intelligence given by Hutter and Legg.

## What is an artificial intelligence?

Nowadays, most people distinguish two types of artificially intelligent systems:

This distinction started with philosophical arguments, such as the Chinese room argument, where the ability of a computer to understand the actual problem was questioned. Nowadays, there are multiple successful cases of narrow AIs (e.g. AlphaGo), but there isn't yet a truly AGI system. This is mainly due to the fact that more people have been (probably wisely) focusing on solving specific problems rather than solving the "holy grail" problem of the AI field, i.e. create an AGI, which seems to be a lot more difficult than creating narrow AI systems. (Anyway, the creation of an AGI could actually arise from solutions to these specific problems, so maybe we are already creating the tools needed to build an AGI, without realizing it). See What is the difference between strong-AI and weak-AI? for more details about the difference between narrow AI and strong AI.

The shortest answer I could can come up with could be as follow; take it with a grain of salt though since we still do not know a lot about natural intelligence:

What natural intelligence is could be seen as the process of learning abstract concepts from limited observations with the intention to use them for solving a [new] task. This process involves using those concepts to imagine new, hypothetically correct scenarios/theories and combine them in a meaningful way to cut down the enormous hypothesis space of possibilities and enable generalization to new situations without observing any data beforehand. Artificial intelligence is to bring what natural intelligence does into machines.

Artificial intelligence researchers undoubtedly have an interest in defining the term used for their own field of discipline, and there is little ambiguity in the term artificial. The challenge is in that the word intelligence has historically been more of a qualitative description than a real number quantity.

How can the intelligence of one be compared with the intelligence of another? IQ testing, averaging college board testing categories, net worth, Chess and Go tournament victory, wrong decision rates, various intellectual speed races, evaluation boards and panels have been a painfully inadequate set of kludges for those involved in the mathematical theory behind the mental capabilities we call intelligence.

Only a century ago, intelligence was a qualitative term people associated with the ability to find solutions to problems in academia, business, and personal life. As culture began to seek quantitative treatment of things once solely qualitative, the dependency of mental capability on a person's age and and their environmental opportunities introduced a challenge. The idea of intelligence quotient (IQ) grew out of the desire to quantify mental potential independent of age and opportunity.

Some have attempted to minimize environmental factors by producing standardized testing of fundamental cognitive skills as they apply to mathematics and language.

Production system and fuzzy logic containers (rules based), deep learning (artificial network based), genetic algorithms, and other forms of AI research have not produced machines that can score well in standardized tests designed for humans. Yet the natural language capabilities, mechanical coordination, planning excellence, and the drawing of conclusions based on clear and verifiable reasoning continue to be sought in machines.

The below are categories of mental capabilities, distinguished by their methods of measurement, architecture of use, and the kinds of research that have produced early promising results and continuous improvement.

• Dialog — measured both by Alan Turing's proposed imitation game and through the viability of answering system automation, personal assistants, and mobile chat-bots
• Mechanical control — measured both by system stability criteria and by rate of incident cost and reduction in loss of life in the case of transportation sector use of intelligent automation
• Business intelligence — measured primarily by increase or decrease in profitability in relation to trends prior to or concurrent with manual planning and operational control

The discovery of the most likely and optimal parameterization for a complex function based on some mathematical expression of what optimal means is deliberately not listed above. What is the central activity for machine learning devices does not fit well into the categories of what has historically been called intelligence, nor should it be. Statistical treatment of data sets for predictive purposes is not learning in the intellectual sense. It is surface fitting. Machine learning is currently a tool to be used by human intelligence, to extend its power, like other computational tools.

This constraint on machine learning may, in the future, be transcended. It is not known if and when artificial networks will demonstrate cognition, logic, the ability to recognize significance, and effective capability in the categories listed above.

One of the difficulties in defining intelligence is the lack of unanimity regarding its dimensionality. If intelligence is to be quantified, the value of $$n$$ where the measure of intelligence $$\mathcal{I} \in \mathbb{R}^n$$ is significant. The concepts of g-factor and IQ imply that $$n = 1$$, but several critics of this ideology, such as Howard Earl Gardner, Ph.D. and Thomas Armstrong, Ph.D. have proposed that there are multiple dimensions to intelligence.

• Linguistic intelligence (“word smart”)
• Logical-mathematical intelligence (“number/reasoning smart”)
• Spatial intelligence (“picture smart”)
• Bodily-Kinesthetic intelligence (“body smart”)
• Musical intelligence (“music smart”)
• Interpersonal intelligence (“people smart”)
• Intrapersonal intelligence (“self smart”)

The argument that these are all manifestations of a single intelligence capability expressed in varying effectiveness due to education or other training has been systematically weakened by evidence-based discovery in the fields of cognitive science, genetics, and bioinformatics.

In genetics, at least twenty-two independent genetic components to intelligence have been identified, and that number is likely to grow. These independent switches in human DNA do not all impact the same neural controls in the brain, indicating the evidence-based weakness of the g-factor ideology.

It is likely that some of the forms of human intelligence and DNA expression map in complex ways that will be discovered over time and that this mapping will entirely replacing the g-factor simplification over time.

The term Artificial Intelligence may be better expressed as Simulations of the Forms and Expressions of Human Intelligence and merely abbreviated as AI. However that is not a definition. It is a rough description. There may never be a single precise definition for all the dimensions we loosely group under the single term. If that is the case for human intelligence, then it may also remain true for artificial intelligence.

There are some common characteristics one can list about all intelligent responses.

• Intelligence can only be measured and obtain usefulness within the context of a particular environmental condition and some objective or set of objectives. Examples of objectives include staying alive, obtaining a degree, negotiating a truce amidst a conflict, or growing assets or a business.
• Intelligence involves adaptation to unexpected conditions based on what is learned through experience, therefore learning without the ability to apply what is learned is not intelligence and applying a process that was learned and simply transferred to some one or some thing controlling the a process is also not considered intelligence.

Human intelligence can learn and apply in what seems to be a concurrent fashion. Beyond that, it would be amiss to discuss a working definition to intelligence without mentioning some of the key human mental abilities that have been proposed as recursion on lesser forms, but proof that recursion or composition will produce these mental features does not exist.

• Concurrent learning and use of what has been learned
• The ability to invent new mechanisms of incremental improvement
• The ability to invent structure outside currently learned domains

Future requirements for intelligent machines may include these, and there may be some wisdom to include them now.

References

Scripts, plans, goals, and understanding: An inquiry into human knowledge structures, Schank, Abelson, 2013, cited by 16,689 articles, T&F excerpt: In the summer of 1971, there was a workshop in an ill-defined field at the intersection of psychology, artificial intelligence, and linguistics. The fifteen participants were in various ways interested in the representation of large systems of knowledge or beliefs.

Understanding Our Craft — Wanted: A Definition of Intelligence, Michael Warner, 2002

The Concept of Intelligence and Its Role in Lifelong Learning and Success, Robert J. Sternberg, Yale University, 1997

Some Philosophical Problems From the Standpoint of AI, John McCarthy and Patrick J. Hayes, Stanford University, 1981

Understanding and Developing Emotional Intelligence, Olivier Serrat, Knowledge Solutions, pp 329-339, 2017

Frames of Mind: The Theory of Multiple Intelligences, 2011, Howard Gardner

7 (Seven) Kinds of Smart: Identifying and Developing Your Multiple Intelligences, 1999, Thomas Armstrong

Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence, Suzanne Sniekers et. al., 2017

• This definition of Intelligence focus strongly on a scientific background which is grounded in the human DNA. What is missing is the social component of inventing magic tricks. The first example of robotics were so called Automaton made by the wrongplayer Wolfgang von Kempelen. The idea was to deceive the people. Betting on horse races (Ada Lovelace), crap games and fast calculation in the human brain can all be seen as cheating done by illusionist. – Manuel Rodriguez Nov 4 '18 at 8:31

Intelligence

A measure of the strength of a decision-making agent relative to other decision-making agents, in regard to a given task or set of tasks. The medium is irrelevant—intelligence is exhibited by both organic and intentionally created mechanisms. May also be the capability to solve a problem, as in the case of a solved game.

Artificial

Relates to the term artifact, a thing which is intentionally created. Typically this term has been used to connote physical objects, but algorithms created by humans are also regarded as artifacts.

The etymology is derived from the Latin words ars and faciō: "To skillfully construct", or, "the art of making".

Artificial Intelligence

• Any decision-making agent that is skillfully (intentionally) constructed.

APPENDIX: The meaning of "intelligence"

The original meaning of "intelligence" seems to be "to acquire", back to the Indo-European. See: intelligence (etymology); *leg/*leh₂w-

The OED 1st definition of intelligence is not incorrect, extending the meaning to acquisition of capability (demonstrable utility), just that the second definition is the older and fundamental: "The collection of information of [strategic] value; 2.3 (archaic) Information in general; news."

You can regard the universe as being comprised of information, whatever form that information takes (matter, energy, states, relative positions, etc.) From the standpoint of an algorithm, this makes sense since the only means they have to gauge the universe are percepts.

Take a flat text file. It may just be data, but you could try and execute. If it actually runs, it might demonstrate utility at some task. (For instance, if it is a minimax algorithm.)

"Intelligence as a measure of utility" is itself "intelligence" in the sense of information, specifically that information by which we measure intelligence, as a degree, relative to a task or to other intelligences.

• Note, this also hews to the Russell&Norvig basic definition of intelligence, rooted in utility. Sans utility, there is no meaningful definition of Intelligence, at least not in the sense of being concrete or practical. – DukeZhou Mar 22 '19 at 20:20

There is no formal definition that most people agree on. Hence here is what I, as a data science / machine learning consultant, think:

Artificial intelligence as a research field is the study of agents which sense and act autonomously in an environment and improve their situation according to some metric with their actions.

I don't like the term, because it is too broad / vague. Instead, look at the definition of machine learning by Tom Mitchell:

A computer program is said to learn from experience 'E', with respect to some class of tasks 'T' and performance measure 'P' if its performance at tasks in 'T' as measured by 'P' improves with experience E

Machine learning is an important part of AI, but not the only one. Search algorithms, SLAM, constrained optimization, knowledge bases and automatic inference are also certainly part of AI.

Definitions of Artificial Intelligence can be categorized into **four categories

• Thinking Humanly,
• Thinking Rationally,
• Acting Humanly and
• Acting Rationally.

The following picture (from Artificial Intelligence: A Modern Approach) will shed light on over these definitions:

But what is more interesting is the AI effect. John McCarthy once stated that the part of the problem is that

As soon as it works, no one calls it AI any more.

Similarly, Pamela McCorduck wrote:

It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, 'that's not thinking'.

AI researcher Rodney Brooks complains:

Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a computation.'

This is something that makes the definition of AI flexible.

• Can you please next time format your question more properly? Don't use <br/>. There's no need for that. Just use a white line (i.e. press enter). Also, quote using our tool (i.e. precede the quoted text with >)! See my edit to understand what you need to do! – nbro May 18 at 10:10
• The image should really be entered as block-quoted text. As it is now, no one can copy'n'paste from it, and search engines can't index its content. – Ray Butterworth May 18 at 13:35