Today, whenever we speak of AI, we mean a specific model of AI based on large language models and diffusion, both of which, undergo foundational training in an approach called autoregression. In today's discourse, whenever a researcher asks a "cutting edge" question about AI, they are just asking a question about how to tweak the current model of AI. For instance, whenever researchers ask questions about "AI Ethics", most of them at least, are just asking about LLM Ethics in the form of guardrails, constitution, etc.
But the idea of intelligence itself (and as a consequence, ethics, morality, etc.) has fascinated researchers over several thousands of years now. And even in the last 70 years or so, many philosophical questions about intelligence have been asked and sought to be explained in precise, mathematical terms.
In this post, let us have a look at the different ways in which "intelligence" can be understood and modelled.
At the outset, I should push aside theories like IQ scores, from this post. If someone actually believes that intelligence can be meaningfully represented as a single-dimensional, numerical value, and we can rank people and populations based on this value; then they are not very intelligent, and I hesitate to engage with them. I'm just amazed by how popular is this whole IQ business still.
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The term "Artificial Intelligence" was born in the summer of 1956, at what is now called the Dartmouth Conference. Organized by a young researcher named John McCarthy, the gathering brought together brilliant minds eager to prove that thinking machines were no longer just the stuff of science fiction. McCarthy had a branding problem: he needed a catchy banner to unite researchers scattered across cybernetics, psychology, and early computer science. Other researchers like Herbert Simon and Allen Newell pushed for a much more bureaucratic, academic-sounding alternative-- "Complex Information Processing."Had they won the debate, today we might be talking about the rapid rise of "CIP" systems and worrying about whether complex information processors are taking our jobs. Instead, McCarthy’s catchy term "Artificial Intelligence" captured popular imagination, transforming a dry engineering pursuit into a thrilling quest to decode the very essence of the mind, capturing the public's imagination and setting the stage for decades of grand dreams, wild hype, and sci-fi obsession.
As a result of this, there are several models of intelligence that have been proposed, each of which, have lead to their own kinds of breakthroughs. Let us have a look at some of these models.
Intelligence as observable behaviour
Alan Turing who is credited with creating the mathematical basis for computation, was one of the first to also propose an "empirical" definition of intelligence, in his 1950 paper, "Computing Machinery and Intelligence". He proposed that instead of getting bogged down in unanswerable philosophical debates about intelligence or asking whether machines can truly "think," we should define intelligence entirely through observable behaviour. If a machine acts, converses, and reacts indistinguishably from a human, then for all practical and empirical purposes, it is intelligent. This pragmatic approach makes intuitive sense because even among humans, internal mental states are inherently unobservable in others-- we infer intelligence solely through external outputs like speech and action. There is a related concept that was first used in political science, and now also in computer science. This is called "duck typing": "If we see something that walks like a duck, waddles like a duck, and quacks like a duck, then it is probably a duck." Just as duck typing cares only about an object's methods rather than its internal class inheritance, Turing’s test judges a system purely by its functional interface with the world. Hence, if someone or something acts intelligently, then it is probably intelligent.
However, this behavioural approach has faced pushback from several detractors. Philosophers like John Searle famously countered this with his "Chinese Room argument". Imagine a (non-Chinese) clerk who is stuck in an office where all transactions happen only in Chinese. The clerk is given a set of rules by which he should respond to different sequences of symbols. And suppose the clerk is able to do that. Then, it appears to the outside world that the clerk now understands Chinese. But the clerk is merely following a set of rules. Similarly, while a computer can flawlessly manipulate symbols to simulate fluent conversation, it is not actually understanding what it is doing-- it is merely following some rules.
Philosopher Peter Norvig, with his "pigeon argument" argues that if aviators were to think of building flying machines in the way Turing thought of building thinking machines, then their goal would be to build a machine "that looks like a pigeon, flies like a pigeon and fools other pigeons into thinking that it is a pigeon." Which is absurd. Our flying machines (airplanes) do not look like birds-- yet they fly. Similarly, we can agree that animals are also intelligent-- yet they cannot carry out a conversation with humans and fool humans into thinking that they are also human.
Yet others like Peter Wegner argue that the Turing Test encourages deceptive superficiality-- rewarding programs that master the art of parlour tricks and linguistic bluffing rather than true reasoning, common sense, or adaptability. For instance, how do you play chess with two grandmasters at once, and fool both of them into thinking that you are a grandmaster? The answer is to get them to play against one another-- through us (that is, see what the first player plays, and make the same move to the second player, and reflect the second player's move, back to the first player). While this trickery can still be argued to be a "manipulative" kind of intelligence, this still leaves open the more fundamental question-- how did the grandmasters get their intelligence?
Intelligence as Imitation:
One of the key markers of intelligence that we see in children (including cubs of many animals) is their ability to imitate their surrounding. Children often copy their parents and elders in their mannerisms and action and further on, even in thought and interpretation.
Imitation requires an enormous amount of pattern matching and reasoning capabilities. A "completely imitative agent" of a human for example, should be able to mimic a human in all our capabilities-- in actions, thoughts, and even in ethics, morals and spiritual grounding. Hence, an agent that can maximally imitate the universe, can in fact, imitate the intelligence in the universe, and hence, can be called intelligent as well.
Current day LLMs are a result of this paradigm. The core model in an LLM is called "autoregression" where a corpus of text is its own training data. An LLM foundation model does not require a separate tagged dataset and a supervisor to teach the LLM. The LLM randomly masks words in the text and tries to predict it based on the surrounding context. In essence, it is trying to imitate the corpus, and if the corpus is large enough and contains enough amount of intelligent output, the imitating agent also appears intelligent.
This is the reason why many now believe that machines have defeated the Turing test. Today's AI makes their output pretty much indistinguishable from human output. (How much of this blog post was generated by AI-- can you guess?)
But of course, the main issue with this interpretation of intelligence is that, imitation requires an intelligence environment (corpus) for it to imitate-- and the question of where did the intelligence in the environment (corpus) come from, still remains open.
Intelligence as conceptual modelling:
This is yet another approach propounded by the conceptual modelling community. To explain this, we first look at the parable of the "hungry cat".
A man had a pet cat at home. Everyday when he returned home from work, his cat would be quite hungry. It leaps at his legs and starts demanding to be fed. It keeps coming between his feet, making him trip. With great difficulty, he reaches the refrigerator to take out some cat food. But due to the relentless demands of the cat, which is jumping and leaping at the food, he invariably ends up dropping the food on the floor, which the cat eats and goes away.
The exasperated man goes to the animal psychologist to ask why isn't the cat learning to sit by its food bowl and wait for him to feed it. It can see that he intends to feed it.
The animal psychologist replies that the cat is indeed learning something. It has learned to "predict the next token"-- if it keeps leaping at your feet and demanding food, it has predicted that, food will fall from above!
This incidentally, is exactly how present day LLMs work as well-- by predicting the next token (word) given a sequence of words.
What the cat was not able to do, was to build a conceptual model based on understanding latent elements like intention, and an understanding of what the person intends to do.
While conceptual modelling involves a lot of formal logic and symbolic reasoning, building the conceptual model itself is beyond this. As the saying goes, "We can only communicate truth using logic-- we cannot discover truth with logic." A good model helps us perform symbolic reasoning and inference-- not the other way around. With just formal logic and symbolic reasoning, we cannot automatically build good models.
There is no exact "science" of conceptual modelling. We all build conceptual models at different extents of explainability and accuracy. A good conceptual model forms a good abstraction-- in that, it captures the "essence" of what is being observed, which is typically a latent entity. Capturing the essence goes beyond just the ability to predict the next token. A good conceptual model offers a good explanation and understanding of the situation, rather than just matching patterns.
Yet another saying goes: "All models are wrong, but some are useful". For instance, Newtonian physics is a remarkable conceptual model that captures the essence of physical dynamics of the universe. But even Newtonian physics is "wrong" in the sense that, it breaks down when we try to apply this model in the sub-atomic or inter-galactic scale.
Regardless of that, our ability to build conceptual models that goes beyond just predicting the next observation, and offering an explanation and understanding of underlying principles, is an indicator of intelligence.
Intelligence as agency:
This line of argument says that if we observe every entity that we consider intelligent-- humans, animals and even plants-- all have one thing in common. They are all "living" beings and have some element of "free will".
All naturally intelligent beings are autonomous. They don't wait to be prompted or commanded. They act on their own, and pursue their self interest.
This line of argument gave rise to the field of "Intelligent agents" that tried to ask what is meant by autonomy-- if autonomy is the marker for intelligence. Being able to "break down" a prompt into an action sequence and execute them (which is what present day agentic LLMs do) is an artefact of autonomous action, but is not autonomy in itself.
I often give this example to illustrate the above. Sometimes when walking in the darkness outside, we mistake a coil of rope for a snake, and freak out. But the question is, so what if it is a snake? What is the problem? Is it that the snake can kill us? Well, even a rope can kill us-- if we fashion it into a noose and hang ourselves from it. The thing is-- a snake can autonomously kill us! We don't have to do anything for the snake to kill us. While we need to keep "prompting" the rope into forming a noose and hanging us from it, before we can get killed by it..
So this line of argument says that, unless machines can have their own self interest (that is, they will "want" to take over the world or at least want to fight for their "rights"), they are not exactly intelligent. They are adaptive, predictive pattern matching automata-- rather than intelligent agents that exercise their autonomy.
Intelligence as embodied cognition:
In this line of argument, intelligence is seen as having a purpose-- to help protect the body. All intelligent beings we saw, act autonomously. But we see that acting autonomously does not mean they act in an arbitrary or random fashion.
All autonomous actions of living beings fundamentally stem from their quest for survival. And hence, as this line of argument goes, intelligence is tightly tied to our bodies. According to this line of argument, purely software forms of intelligence, including LLMs, are not really intelligent, because they have no purpose of their own. Rodney Brooks, one of they key researcher argues that "Elephants don't play chess" as a critique at purely software based approaches to intelligence like chess playing programs.
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Many of these lines of argument have given rise to key technologies and insights that are still critical to support present day AI. For instance, the entire field of compilers came up because we wanted to formalise language and thought. Similarly, the embodied cognition argument gave rise to present day robotics that are vastly better and autonomous, than earlier forms of robots.
In our quest for asking cutting-edge questions, I do hope we do not assume AI means LLMs and intelligence means autocomplete, or explainability means symbolic logic. The real problems are much deeper.