What is the definition of artificial intelligence?
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:
- Those that address thought process and reasoning (how an AI thinks/reasons)
- 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).
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.
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.
- Any decision-making agent that is skillfully (intentionally) constructed.
APPENDIX: The meaning of "intelligence"
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.
What is artificial intelligence?
This question is ambiguous. I will address the two less ambiguous but related questions.
- What is the goal of the AI field?
- 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 of intelligence is reasonable and consistent with reinforcement learning (which could be the path to AGI), but maybe not formal and rigorous enough. In this answer, I report a possibly more sound definition of intelligence given by Hutter and Legg, so I suggest that you read it, but the definitions are roughly consistent with each other (because the concepts of "goal" and "goal-seeking behavior" are present in both definitions), although they emphasize different aspects (e.g. computation or generality).
What is an artificial intelligence?
Nowadays, most people distinguish two types of artificially intelligent systems:
- Narrow AI (aka weak AI, although this term may not exactly be a synonym for narrow AI, but it's just the opposite of strong AI: see the Chinese-Room argument): a system that solves a very specific problem (e.g. playing go)
- Artificial general intelligence (aka strong AI, although this term may not always be used as a synonym for AGI): a system that can solve multiple problems
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.
According to the book Artificial Intelligence: A Modern Approach (section 1.1), artificial intelligence (AI) has been defined in multiple ways, which can be organized into 4 categories.
- Thinking Humanly
- Thinking Rationally
- Acting Humanly
- Acting Rationally
The following picture (from the same book) provides 8 definitions of AI, where each box contains 2 definitions that fall into the same category. For example, the definitions in the top-left corner fall into the category thinking humanly.
There is also the AI effect, which Pamela McCorduck describes (in her book Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, p. 204) as follows
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, but that's not thinking
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.
The facts and anomalies paper (mine) has attempted narrowing down on what intelligence is. Only once you identify it, you'll be on the right path to replicating it. For now, nobody knows exactly what constitutes intelligence. From the perspective of just what intelligence is: The ability to search for, parse and bring in the right information into a context in order to deduce past, present and future (temporal) occurrences that enable moving toward favourable outcomes. So a program or machine that operates based on some if-then-style conditions would be considered as having a basic level of intelligence.
However, when we say "artificial intelligence", we usually mean an intelligence that's approximately as good or better than living creatures. From the facts and anomalies paper, the decision-making process was observed to be similar across almost all living creatures. What differs, is the amount of memory stored, the length of the attention-span, questioning capability and to what level of complexity the creature can mix and match memories in the world model it creates in its mind.
The paper also brings forth an important point that an intelligence created via any tech can be specific to that tech. So an AI created via microprocessors will inherently be different from an organic intelligence (and there is nothing wrong in building it as such), but we will recognize it as being intelligent in the same way as an artificial sweetener is accepted as a replacement for sugar.
To think of it another way: If you had to say which one of two people are intelligent, how would you evaluate them? The person we'd consider more intelligent, would be the one who understands and analyzes situations better to take decisions that have a better outcome than others. Even a person lacking vast knowledge will be considered intelligent if their creativity and depth of thought is higher than others.
A crucial factor that enables this is the attention span of the mind. A machine that is programmed to access vast stores of memories for even trivial decision making tasks (which helps it evaluate consequences of various actions: commonsense) and is capable of asking questions and can simulate situations in memory by loading and modifying stored memories in the simulation (imagination) will be a lot more "intelligent" than us.
There is a second paper (cognitive memory constructs) that describes a bit about the theory of implementation.
All this being said, this is just our perspective on intelligence. The fact that the universe exists in such a complex form, is probably evidence of much higher forms of intelligence (like how we are much more intelligent than the Age of Empires AI characters we created). Intelligence may exist in far more dimensions than we are currently capable of imagining.
Along the observation in the comment, the artificial part can in principle be assigned to artifacts (or at least combination of natural beings and artifacts) and focus on the intelligence knot.
Assuming the shortest definition cannot be less than three words long, intelligence looks like a measure of an operation onto something.
Here are some tentative points around a sensible concise definition, depending on the concrete word we choose for each piece of the abstract "measure of operation on something".
Data organization simplicity.
Information processing efficiency.
Model adaptation adequacy.
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.