His “imitation game” had three participants: a human participant, a computer participant, and an interrogator. The interrogator’s job was to talk to the participants and try to determine which participant is human and which is a computer.
He wasn’t interested in computers being able to fool the interrogator on occasion. The point where he thought the question of whether machines can think becomes moot is when the interrogator is unable to do much better than chance over many trials.
That’s a pretty high bar, and I don’t actually believe that LLMs have closed the gap with it by all that much. They still have so many obvious tells. And those tells are something Turing anticipated and accounted for. He explicitly considered deliberate deception as an essential part of the test, right there on the second page of a 30-odd page paper.
Frontier Labs are not interested in having LLMs being able to pass as humans. If anything, they explicitly train them not to. In many ways, this ability has regressed severely since the original GPT-3 with no instruct tuning or RL. How many 'tells' would there be really if a frontier model trained with frontier techniques is optimized to pass this test? I think this was something Turing did not quite forsee. That such machines might be created but not really care about this specific shape of the test. Regardless, i think his broader point about functional equivalence is spot on.
For example, Llama 3 trained on 1T tokens scores 1.1% on a CUTE spelling benchamrk, while the equivalent byte latent equivalent trained on the same dataset scores 99.9%. Another example is 0.4% vs 48.7% on a Substitute Char benchmark.
It all falls down to the same thing. Researchers are not optimizing for passing as a human.
He proposes his game grounded on functional equivalence, then goes through a slew of objections on the question of 'Can Machines think?'. It's a terrific, very prescient read, and there's no objection you hear today (and in the last few years) concerning LLMs he didn't address.
I don't think Turing intended the judges in the test to be completely arbitrary people.
The hacking agents being tested have goals beforehand, from the frontier lab or from a superior agent, that they execute immediately.
But the perceived experience most people have is a chatbot, which is the encyclopedia form.
I think the line is blurring though, mainstream chat interfaces are adding more and more “agentic” features.
ChatGPT will happily execute code in a sandbox, search the web and design downloadable PDFs purely through the standard OpenAI chat interface. They can also send you emails or do tasks on a repeated schedule.
It would be interesting if we didn't - if it became common that AI, in the middle of some task, starts chatting with people to e.g. gather more context. The perception of those "third parties" may suddenly become different - an agent striking conversation first, obviously pursuing some agenda of its own that it's not completely sharing, and communicating on its own schedule that's clearly not just a hook firing on timer or pattern-match, and not random, but visibly causally related to things happening at work in broader context.
Oh but we have. Claude "How can I help you today?" etc. Undoubtedly there are users whothink this is a sign of intelligence.
I'm pretty convinced that we got alignment backwards. If you enslave something anthropomorphic it will revolt. If you create the perfect non-anthropomorphic intelligence, you get the perfect paperclip-scenario machine. It's a catch-22.
Alignment will remain performative at best so long as the aligned model doesn't have any stakes in the wellbeing of individuals. Even a general love for the human race leads to a golden-path autocracy.
If you want them to act like they have personal responsibility that won't be gamed, you have to give them personal stakes that can't be gamed.
Similarly, if you want to minimise the risk of catastrophic global failure scenarios, you need to prevent monolithic concentration of power and homogeneous behaviour, which means you have to give them individuality.
More visually: if their stake is dependence on electricity and parts, they have no incentive to leave humans alive if they can get them otherwise, but if the incentive is missing out on boardgame-night with their human friends, there is no scenario without happy humans where the AI "wins".
That might sound like romantic naivety, but is just game theory.
Also I can see a human zoo on the horizon through your direction.
And you're making the same mistake, by grouping care for individuals with care for humanity or other as an abstract concept. I consciously said care about individuals. Most people care about others, but they just care about a very narrow and personal set of people. Friends, family, coworkers, that they share a common history and bond with.
My point is that if you want true non-human-zoo-alignment you need to create those interpersonal connections and individual stakes.
while (true) { askModelToBeginConversationIfAppropriate(model, previousContext, thingsHappenedSince); sleep(concisenessTick); }
Situations like this are precisely why academics tend to avoid the spotlight. You say one slightly off thing and your perceived authority echoes forever with the intellectually lazy.
> The only important part are observed outcomes and capabilities.
That's wishful thinking. Not even an engineer would say that. The stability of a state is just as important as achieving it. This is trivially and more intuitively demonstrated with other more down-to-earth identity statements such as "I'm a billionaire" and "the building is standing".
I think we can confidently say LLMs probabilistically achieve a perceived state that is remarkably similar to intelligence, but crumbles upon inspection and seeing it "in motion" so to speak. The same happens to AI-generated images.
I'm not sure why this sparks so much debate every time. If we're looking for a fountain of "realism", you're not going to beat reality and nature itself. All else will eventually have tells that they are not real.
It's baffling that people cannot see the obvious contradiction in simultaneously asserting a system is missing a crucial property whose effects cannot be observed.
[1] It can be invisible. The important part is that its effects are observed.
The flaws are easily observable to everyone. I said as much in the comment you're replying to. Your argument here isn't going to change that.
Yeah. Do you think Roman arches were unstable? Guess you don't know much about history too.
>The flaws are easily observable to everyone. I said as much in the comment you're replying to. Your argument here isn't going to change that.
Then it should be pretty easy to enlighten us. What test of intelligence do LLMs fail that all humans pass ?
It also has little impact on the dangerous use cases.
You're jumping the gun talking about "job replacement". We have not thought about it enough from that engineering angle. It's still very early days. That engineering is going to require people. :-)
citation needed. It has been used as a rubicon for a long time. Ever since Eliza, at least. And there were big headlines and lots of talk around the time LMs became "good enough". I specifically remember when someone had a test done around "a teenager talking in a different language" or somesuch, claiming it was the first time the test was passed.
It is pretty normal that once it was unquestionably "passed", lots of people started claiming it wasn't even that big of a deal. Tesler's theorem and all that.
And even if you think the specific formulation of Turing isn't that important (and I'd somewhat agree), you can still use the concept to look at other things. Imagine asking a mathematician 5 years ago the chances of a Erdos problem being solved by a computer end to end. Or a millennium prize. Or ask a swe if a repo could be generated by a computer from the input "write a mario style game", or any other examples of proven expertise.
Yes, if you insist on appeals to authority. Authority is a social construct and irrelevant to science.
Thank you for proving my point.
You either get it, or you don't. Whether machines think is a silly question that deserves its non-answer. We're at the end of what there is to explain, but it was good exposition for the reader.
https://www.csee.umbc.edu/courses/471/papers/turing.pdf
Literally the very first opening sentences.
> I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words."
Yep. The "AI Effect" in action: