What is artificial intelligence (AI)? We take the term for granted, but how might we phrase a formal definition? And are the technologies that we have today really reflective of all that this term implies?
Traditionally a branch of computer science, AI as a holistic concept has pulled from many areas of academic arenas, from philosophy to physics. Many are aware of the recognized origins of the term – famed computer scientist John McCarthy in 1956 at the “Dartmouth summer research project on Artificial Intelligence.” Since that time and as research and technology in this area has evolved, definitions of AI have shifted across a wide spectrum, and academics, businesspeople, and laypersons have a range of definitions (some better informed and reasoned than others – though again, the utility of such a term can depend on background and objectives).
One of the reasons AI is so difficult to define is because we still don’t have a set definition or one solid concept for intelligence in general. Intelligence is often dependent on context. A traditionalist might define intelligence as level of reasoning power, and this seems one of the reasons why a popular determiner of AI has often been games – man and machine try to ‘outthink’ the other, or in the case of the machine at least match the human, so that it becomes difficult to tell where man begins and machine ends.
But, in the end, mastering a game (like Go) is very different from sealing a successful business deal in the real world, then driving home to have a meal with your family and reading and reflecting on a bit of Plutarch before bed. In any case, researchers Shane Legg and Marcus Hutter have made the case that intelligence includes the following features:
- Intelligence is a property of some entity or agent that interacts with some form of environment
- Intelligence is generally indicative of an entity’s ability to succeed (by a given set of criteria) at a particular task or achieving a stated goal
- When speaking of an “authentic” intelligence, there is an emphasis on learning, adaptation, and flexibility within a wide range of environments and scenarios
Emerj’s Definition of Artificial Intelligence:
*NOTE: Artificial intelligence (AI) can be separated into two branches of entities – that of ‘smart’ computers or systems (such as today’s deep learning), and a still unrealized ‘artificial general intelligence’ or AGI. We include this as a preface for helping to distinguish between the two in our present state of technological development. Our definition attempts to define an entity rather than a field of study, and also utilizes broad or somewhat open terminology to allow room for evolution and growth of the AI field as we know it. Our attempt at an informed, “living” definition of AI is below:
“Artificial intelligence is an entity (or collective set of cooperative entities), able to receive inputs from the environment, interpret and learn from such inputs, and exhibit related and flexible behaviors and actions that help the entity achieve a particular goal or objective over a period of time.”
* How We Arrived at Our Definition:
As with any concept, artificial intelligence may have a slightly different definition, depending on whom you ask. We combed the Internet to find five practical definitions from reputable sources:
1. “It 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.” – Stanford
2. “Artificial Intelligence is the study of man-made computational devices and systems which can be made to act in a manner which we would be inclined to call intelligent.” – The University of Louisiana at Lafayette
3. “Defining artificial intelligence isn’t just difficult; it’s impossible, not the least because we don’t really understand human intelligence. Paradoxically, advances in AI will help more to define what human intelligence isn’t than what artificial intelligence is.” – OReilly
4. “The ability of a machine communicating using natural language over a teletype to fool a person into believing it was a human. “AGI” or “artificial general intelligence” extends this idea to require machines to do everything that humans can do, such as understand images, navigate a robot, recognize and respond appropriately to facial expressions, distinguish music genres, and so on.” Matt Mahoney, PhD, Data Compression Expert
5. “The scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines.” AITopics.org
We sent these definitions to experts whom we’ve interviewed and/or included in one of our past research consensuses, and asked them to respond with their favorite definition or to provide their own. Our introductory definition is meant to reflect the varied responses. readers should note, Machine learning and artificial intelligence share the same definition in the minds of many, however, there are some distinct differences readers should recognize as well. Below are some of their responses:
Dr. Andras Kornai, Budapest Institute of Technology
I like them all, except for #3, in that no exact definition is needed to work on a problem. We still don’t fully understand gravity, and can measure it only to one part in ten thousand (most other physical phenomena we can now measure to one part in a billion or even better), yet it would be silly to say “it’s impossible to define gravity”.
Some clarity may be required to distinguish “good old-fashioned” AI from modern AGI. GOFAI was centered on conceptual modeling by symbol manipulation, e.g. the planning required to win a chess game, and took the Turing Test as its central goal. AGI demands more, reaching comparable levels in all forms of human intelligence are seen as goals. This development is well emphasized in #4.
#1, #2, and #5 are right in emphasizing that A(G)I is primarily about creating algorithms that show intelligent behavior, and is not to be confused with cognitive science or brain modeling which aim at explaining how a particular hardware, the human brain, gets there. We may, or may not, be able to steal ideas from nature, this remains to be seen.
Dr. Ashok Goel, Georgia Institute of Technology
Artificial Intelligence is the science of building artificial minds by understanding how natural minds work and understanding how natural minds work by building artificial minds.
Dr. Pei Wang, Temple University
This is a complicated problem. I have a paper on it: What Do You Mean by “AI”?, in which “intelligence” is defined as “adaptation with insufficient knowledge and resources.”
(In response to definitions 1 and 2) – Defining AI using “intelligent” or “intelligence” is a circular definition. The statement is agreeable, but does not provide clear guidance to the research.
(In response to definition 3) – Our understanding of human intelligence is a matter of degree. This opinion is encouraging blind trial-and-error, which is not good advice for any scientific research.
(In response to definition 4) – Too anthropocentric. AGI can be easily distinguishable from human beings, while still being considered as highly intelligent.
(In response to definition 5) – Better than the others, though still uses “intelligent”.
Dr. Vincent Müller, Anatolia College
Definition 1 is okay. Of course it leaves the minor question, ‘what ‘intelligence’ means’, open. I think it’s important to see that AI is about making, and that it is distinct from cognitive science – even though traditionally this was seen otherwise. That’s not a definition, however.
(Dr. Müller provides a link to his related work: New developments in the philosophy of AI)
Dr. Dan Roth, University of Illinois at Urbana-Champaign
Any definition of Artificial Intelligence will have to be vague enough due to our inability to define Human Intelligence. But I would say that this is the scientific field that attempts to understand the foundations of intelligent behavior from a computational perspective. It focuses on developing theories and systems pertaining to intelligent behavior, at the heart of which is the idea that learning, abstraction and inference have a central role in intelligence.
We also found and chose to include a more recent and commonly-accepted textbook definition to build on our perspective. In “Artificial Intelligence: A Modern Approach”, Stuart Russell and Peter Norvig defined AI as “the designing and building of intelligent agents that receive percepts from the environment and take actions that affect that environment.” This definition by its nature unites many different splintered fields – speech recognition, machine vision, learning approaches, etc. – and filters them through a machine that is then able to achieve a given goal.
Strong AI versus Weak AI
Strong AI – Also known as deep AI or what some might deep AGI; the idea that a computer can be made or raised to intelligence levels that match human beings’.
Weak AI – Otherwise known as narrow AI; the idea that computers can be endowed with features that mirror or mimic thought or thinking processes, making them useful tools for figuring out how our own mind works. Narrow AI systems also enhance or augments human “intelligence” by delivering calculations, patterns and analyses more efficiently than can be done by a human brain.
The field of artificial life branches out further from traditional AI to include the study and mimicry of various biological forms and organisms that exhibit a range of “intelligent” behaviors.
One way to categorize AI solutions for commercial and scientific needs is by level of complexity of the application: simple, complex, or very complex (though these are, clearly, also open to interpretation). This is an idea borrowed from the Schloer Consulting Group:
Simple – Solutions and platforms for narrow commercial needs, such as eCommerce, network integration or resource management.
- Examples: Customer Relationship Management (CRM) software, Content Management System (CMS) software, automated agent technology
Complex – Involves the management and analysis of specific functions of a system (domain of predictive analytics); could include optimization of work systems, predictions of events or scenarios based on historical data; security monitoring and management; etc.
- Examples: Financial services, risk management, intelligent traffic management in telecommunication and energy
Very Complex – Working through the entire information collection, analysis, and management processes; the system needs to know where to look for data, how to collect, and how to analyze, and then propose suggested solutions for near and mid-term futures.
- Examples: Global climate analysis, military simulations, coordination and control of multi-agent systems
In similar fashion to types of AI solutions organized by capability, there exists a continuum of AI in regards to level of autonomy:
Assisted Intelligence – Involves the taking over of monotonous, mundane tasks that machines can do more efficiently.
Augmented Intelligence – A step up in a more authentic collaboration of “intelligence”, in which machines and humans learn from the other and in turn refine parallel processes.
- Example: Editor from NyTimes
Autonomous Intelligence – System that can both adapt over time (learn on its own) and take over whole processes within a particular system or entity.
- Example: NASA’s Mars Curiosity Rover
Approaches to Achieving AI:
It seems important that what any definition does not do (as noted by Legg and Hutter in their paper A Formal Definition of Intelligence for Artificial Systems), is limit or restrict the inner workings of AI and/or the approaches used to creating an AI entity.
The methods taken toward achieving a “true AI” or AGI are wide and varied, but some are closer in line achieving an adaptive, flexible and autonomous intelligence that is more characteristic of human beings (and likely intelligences that do/will exist beyond our own).
Approaches that have evolved and continue to receive wide recognition in the media include (though are not isolated in approach or limited to) the following:
- Artificial neural networks
- Reinforcement learning
- Self-supervised learning
- Multi-agent learning
- Machine learning
Limitations in Defining AI:
No matter what we do, we can’t (at present) escape our biological and social trapping as human beings, which means that any definition put forth and/or any test that we conceive to test for a “true” artificial intelligence is at risk from anthropocentrism and subjectivity.
What we can do is cultivate an awareness of our biases and opinions and strive to seek out a broader or more “universal” notion of intelligence that encompasses a range therein; achieving the creation of an artificial intelligence in our likeness may be one of the ultimate challenges of our times, but there are likely thousands or even millions types of artificial intelligences which we could aim to (or may not be capable) of conceiving and creating.
While it’s science’s aim to discover truth and knowledge at the heart of every system and process in the universe, we should recognize that we may not be able to understand the workings of an artificial intelligence that we one day create – in fact, that’s already the case with deep learning and neural networks, similar to our current complete lack in understanding how the human brain works. At some point, we may not be able to keep up with an AI’s processing powers and ways of literally seeing and conceiving of reality as we know it.
NOTE: It’s beyond the scope of this article to give a cohesive, historical overview of AI, or of today’s landscape. Instead, our intent is to provide a jumping off point for understanding and further exploring the history, workings, and consequences of AI in today’s increasingly automated and augmented landscape.
Related Interviews and Articles on Emerj:
If you’re interesting in getting a lay-of-the-land perspective on the implications and applications of artificial intelligence, you might enjoy this curated selection of some of our more popular AI overview topics, listed below:
- Popular Interview: Dr. Nando de Freitas – Deep Learning is Like Building with Lego
Image credit: Nomura Connects