What is AI?
Artificial intelligence, or short AI – we all have heard this term and probably we also use it now and then. It is one of the current buzz words. But what do we actually mean when we speak of AI?
There are many definitions of artificial intelligence floating around. Some define it through the machine aspect and (human) intelligence:
“Artificial Intelligence (AI) may be defined as the branch of computer science that is concerned with the automation of intelligent behaviour.” (Luger, 2009, p.1).
“Artificial intelligence (AI) represents a highly capable and complex technology that aims to simulate human intelligence.” (Glikson & Woolley, 2020, p.627)
Others through the machine aspect and its abilities:
“An AI system is a machine-based system that is capable of influencing the environment by producing recommendations, predictions or other outcomes for a given set of objectives.” (OECD, 2022, p.23).
Some avoid using difficult terms and define it reversely:
“[Artificial Intelligence is] the collection of problems and methodologies studied by artificial intelligence researchers.” (Luger, 2009, p.2).
And again, others as a process:
“[…] we conceive of AI as a process, rather than a phenomenon in itself. We define AI as the frontier of computational advancements that references human intelligence in addressing ever more complex decision-making problems. In short, AI is whatever we are doing next in computing.” (Berente et al., 2021, p.1435).
The tricky word
Why are there so many different definitions for AI? Well, artificial intelligence is a tricky word. Maybe most of us can agree on the definition of artificial: something that does not occur naturally, something that is human made. But what about the term intelligence? When is somebody, or something intelligent? Is it the IQ, how good they are at calculating, how well they can judge other people’s emotional states? What are the criteria for intelligence?
And then: is artificial intelligence in its core the same as human intelligence? (If you thought: well, of course! Then just another aspect: what about creativity? Or self-awareness? Is this part of intelligence?) Can we use the same criteria to judge AI and human intelligence?
A famous test for judging the intelligence of AI is the Turing Test, originally called the Imitation Game. As the name hints, it was invented by famous Alan Turing and is a (theoretical) test in which a machine needs to perform a task (e.g., answer questions). The original name also hints at the idea behind the test: Based on the output of the machine (e.g., an answer to a question) a human needs to judge whether they are interacting with a machine or another human being. If the machine manages to pass as a human, the AI has passed the Turing Test and is considered intelligent (Luger, 2009).
You can see that the Turing Test has a quite specific definition of intelligence – if a machine appears to be human.
Narrow vs. general AI
But is this really what we mean with intelligence? What about a machine that is able to compute extremely fast and to give you an answer to a problem in less than a second, while a human needs days, if not years to solve it – is this machine not intelligent? Most would say it is very intelligent, as it outperforms a human! On the other hand – ask this same machine to perform the task of distinguishing a person crying from laughter and another person crying from sadness. It will fail in giving you the correct answer. Now we would say it is not intelligent at all. And this is because machines are only intelligent in a narrow field.
One speaks of narrow/weak vs. general/strong AI (Glikson & Woolley, 2020). Narrow/weak AI can perform specific tasks, for example, face recognition. General AI refers to a machine that is super-intelligent in “all” aspects. Strong AI is usually what is envisioned in dystopian futuristic books and movies (e.g., 2001 Space Odyssey or the robots in the Alien movies).
However, it is important to note down that we are far away from general AI.
Some think there are aspects of human intelligence that we will never be able to implement or that we do not need/want general AI, others believe that it is just a question of time until we reach strong AI.
So, what are examples for AI?
AI encompasses a variety of intelligent systems with many different algorithms. Some follow rational programmed rules, some are agent-based and situated (Luger, 2009). Some of them come with a body, like robots. Boston Dynamics are an impressive example of smart robots (see the picture of their dog Spot) already in use or co-bots on shop floors or mobile telepresence robots in hospitals (this is what Alejandra is looking at!). On the other side are bodyless intelligent systems. AI encompasses a variety of these, a subpart being machine learning (ML). Note that often Machine Learning is what people refer to when they talk about AI.
We speak of machine learning if the machine learns by itself without somebody explicitly programming a set of rules it needs to follow. The system manages to learn from the given inputs. Machine learning itself is again a huge field and can be divided into different approaches (e.g., unsupervised learning, supervised learning, reinforcement learning – I can recommend Daugherty & Wilson’s book: Human + Machine for more details (Daugherty & Wilson, 2018)).
Some concrete examples of AI that you have probably encountered already: Apple’s Siri, Amazon’s Alexa, GoogleMaps, Instagram/Facebook sorting your feed and giving you suggestions, Amazon suggesting related products, or Netflix recommending what to watch next.
So you see – already knowing what AI is can be difficult! Maybe next time you read or hear the word AI you will take a minute to think about the use of the word in the context and figure out what kind of AI they mean. 🙂
P.S.: Credits for the featured image to Alina Constantin / Better Images of AI / Handmade A.I / CC-BY 4.0.
Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing Artificial Intelligence. MIS Quarterly, 45(3), 1433–1450. https://doi.org/10.25300/MISQ/2021/16274
Daugherty, P. R., & Wilson, H. J. (2018). Human + Machine: Reimagining Work in the Age of AI (1st ed.). Harvard Business Review Press.
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057
Luger, G. F. (2009). Artificial intelligence: structures and strategies for complex problem solving (6th ed.). Pearson Education.
OECD. (2022). OECD Framework for the Classification of AI Systems. OECD Digital Economy Papers, 323(February). https://doi.org/https://doi.org/10.1787/cb6d9eca-en