> The author Pamela McCorduck writes: "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'."
The flip side of that is that every time a new AI approach becomes popular even more people proclaim "this is what thinking is", believing that the new technology reflects the underlying process of human intelligence. This phenomenon goes back further than AI as a formal discipline, to early computers, and even during the age of mechanical computers. There are parallels with robotics, where for centuries anything that could seemingly move like a human was perceived to be imbued with human-like qualities.[1] The human instinct to anthropomorphize is deep-seated and powerful.
I keep returning to this insight by a researcher of the Antikythera Mechanism, which in the context of ML seems even more apropos today than in 1986:
> I would like to conclude by telling a cautionary tale. Let us try and place the Antikythera Mechanism within the global context of ancient Greek thought. Firstly came the astronomers observing the motions of the heavenly bodies and collecting data. Secondly came the mathematicians inventing mathematical notation to describe the motions and fit the data. Thirdly came the technicians making mechanical models to simulate those mathematical constructions, like the Antikythera Mechanism. Fourthly came generations of students who learned their astronomy from these machines. Fifthly came scientists whose imagination had been so blinkered by generations of such learning that they actually believed that this was how the heavens worked. Sixthly came the authorities who insisted upon the received dogma. And so the human race was fooled into accepting the Ptolemaic system for a thousand years.
> Today we are in danger of making the same mistake over computers. Our present generation is able to view them with an appropriate skepticism when necessary. But our children's children may be brought up within a society dominated by computers, that they may actually believe this is how our brains work. We do not want the human race to be fooled again for another thousand years.
I also regularly return to Richard Stallman's admonition regarding the use of the term, intellectual property. He deeply disliked that term and argued it was designed to obfuscate, through self-serving[2] equivocations, the legal principles behind the laws of copyright, patent, trademark, trade secret, etc.
Contemporary machine learning may rightly be called artificial intelligence, but to conflate it with human intelligence is folly. It's clearly not human intelligence. It's something else. The same way dolphin intelligence isn't human intelligence, or a calculator isn't human intelligence. These things may be able to tell us something about the contours and limits of human intelligence, especially in contrast, but equivocations or even simple direct comparisons only serve to obfuscate and constrain how we think of intelligence.
[1] See, e.g., the 1927 film Metropolis, which played off prevailing beliefs and fears about the progress and direction of actuated machines.
[2] Serving the interests of those who profit the most from expanding the scope and duration of these legal regimes by obfuscating the original intent and design behind each regime, replacing them with concepts and processes that favored expansion.
> Contemporary machine learning may rightly be called artificial intelligence, but to conflate it with human intelligence is folly. It's clearly not human intelligence. It's something else. The same way dolphin intelligence isn't human intelligence, or a calculator isn't human intelligence. These things may be able to tell us something about the contours and limits of human intelligence, especially in contrast, but equivocations or even simple direct comparisons only serve to obfuscate and constrain how we think of intelligence.
This is something I mostly agree with. One quibble:
The process in LLMs clearly differs from human intelligence, but lumping it in with a the intelligence of a calculator is, IMO, making a mistake in the opposite direction.
I don't think argument by assertion is appropriate where there's a lot of people who "clearly" believe that it's a good approximation of human intelligence. Given we don't understand how human intelligence works, asserting that one plausible model (a continuous journey through an embedding space held in neurons) that works in machines isn't how humans do it seems too strong.
It is demonstrably true that artificial neurons have nothing to do with cortical neurons in mammals[1] so even if this model of human intelligence is useful, transformers/etc aren't anywhere close to actually implementing the model faithfully. Perhaps by Turing completeness o3 or whatever has stumbled into a good implementation of this model, but that is implausible. o3 still wildly confabulates worse than any dementia patient, still lacks the robust sense of folk physics we see in infants, etc. (This is even more apparent in video generators, Veo2 is SOTA and it still doesn't understand object permanence or gravity.) It does not make sense to say something is a model of human intelligence if it can do PhD-level written tasks but is outsmarted by literal babies (also chimps, dogs, pigeons...)
AI people toss around the term "neuron" way too freely.
> somebody figured out how to make a computer do something
Well, I would argue that in most deterministic AI systems the thinking was all done by the AI researchers and then encoded for the computer. That’s why historically it’s been easy to say, “No, the machine isn’t doing any thinking, but only applying thinking that’s embedded within.” I think that line of argument becomes less obvious when you have learning systems where the behavior is training dependent. It’s still fairly safe to argue that the best LLMs today are not yet thinking, at least not in a way a human does. But in another generation or two? It will become much harder to deny.
In many ways LLMs are a regression compared to what was before. They solve a huge class of problems quickly and cheaply, but they also have severe limitations that older methods didn't have.
So no, it's not a linear progress story like in a sci-fi story.
> It’s still fairly safe to argue that the best LLMs today are not ... thinking
I agree completely.
> But in another generation or two? It will become much harder to deny.
Unless there is something ... categorically different about what an LLM does and in a generation or two we can articulate what that is (30 years of looking at something makes it easier to understand ... sometimes).
> It’s still fairly safe to argue that the best LLMs today are not yet thinking, at least not in a way a human does. But in another generation or two? It will become much harder to deny.
Current LLMs have a hard division between training and inference time; human brains don’t-we train as we infer (although we probably do a mix of online/offline training: you build new connections while awake, but then pruning and consolidation happens while you sleep). I think softening the training-vs-inference division is a necessary (but possibly not sufficient) condition for closing the artificial-vs-human intelligence gap. But that softening is going to require completely different architectures from current LLMs, and I don’t think anyone has much of an idea what those new architectures will look like, or how long it will take for them to arrive
Do LLMS think? Of course they do. But thinking =/= intelligence.
Its pretty easy to define what an AI actually would look like:
A human coder sits down and writes an algorithm. In that algorithm, there is no reference to any specific piece of information on ANYTHING (including human words), whether its manually written in code or derived through training a neural net on that information and the code is just a bunch of matrix multiplies.
The algorithm has 2 interfaces - a terminal for a human to interact with, and an api to a tcp socket over which it can communicate to the world wide web.
A human could give this algorithm an instruction, like for example, "Design and build me a flying car and put it in my driveway and do not spend a single cent of my money, and do everything legally".
Provided there are no limits on communication that would result in the algorithm being perma banned of the internet, the algorithm prior to even tackling the task at hand will have to do the following at the least:
- figure out how to properly structure HTTP communication to be able to talk to servers, and essentially build an internal API.
- figure out what the words you typed mean - i.e map them to information collected from the web and
- start running internal simulations to figure out what the best course of action is
- figure out how to deal with ambiguity and ask you questions (like "how far do you want to fly"), and figure out how to deal with dead ends.
- start executing actions with preplanned risk (figuring out what risk is in the process) and learn from mistakes.
And that's just the short start.
But the key factor is that this same process that it uses to figure basic functionality is the same process (at least on the lowest level) that it would use to start designing a flying car once it has all the information it needs to "understand" the task.
And there isn't anything even remotely close on the horizon with any of the current AI research that indicates that we have any idea what that process looks like. The only claims that we can make is that its definitely recursive, not fully forward like LLMs, and its essentially a search algorithm. But what its searching and what the guidance metric is for search direction is the mystery.
> every time somebody figured out how to make a computer do something
Well, there’s the clue it is not really thinking if somebody told the machine how to do things. My Roomba isn’t intelligent because it’s been programmed to clean the floor, now is it?
Wake me up when machines learn to do something on their own. I know everybody is on the AI hype train, but please show your extraordinary evidence to your extraordinary claims first.
> The author Pamela McCorduck writes: "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'."