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.

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.

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.

Most people on Earth try to put what they're seeing into the context of what they understand; mental gymnastics to try and understand what is happening based on their prior experience. They have absolutely 0 understanding of how it works under the hood so, to them, it must be alive.

Given a certain world state (including a tool's internal state), its effects back on the world state (as initiated by me) are at some leve of description understandable, expected and repeatable. Swing hammer, drive nail into wood. Make slicing motion with knife, cut meat. Type ' find /path/to/some/dir -name "keyword"', find files with keyword. Point harness at codebase with prompt 'fix bug X', actually fix bug X.

All these examples are at some level of description incredibly complex (think of all particles interacting at the (sub-)atomic level even when using a hammer to only drive a nail into some wood), and of course all the electrons flowing through the GPUs doing matrix multiplications in order to fix bug X, but at some level of description (the one I just used) they are also incredibly simple and understandable.

Intelligence is rather nebulous (and as used by OpenAI/Anthropic, quite threatening), but I don't think this definition of a tool precludes it to be "intelligent". They feel more orthogonal. The intelligence (or perhaps capability) feels like it is related to the size of the chunk of the world state that it can take into account and affect, while still resulting in understandable, expected and repeatable effects. LLMs, when properly harnessed, are pretty great at this currently and we are still discovering what they are consistently capable of.

Calling harnessed LLMs tools is perhaps also a more grounding frame specifically to counter-act the anthropomorphizing framing that OpenAI and Anthropic consistently go for in their game of AI-doom-chicken talk. The tool framing is in that sense maybe a (self-)jedi-mind-trick.

If you ask a common question to an LLM with unusual qualifiers, it tends to ignore the qualifiers and give you the typical answer. I saw a demonstration of this with the whole "the surgeon is my mother" "puzzle" that people use to expose implicit gender bias (ie where they assume the surgeon is a man). Ask variations of this and it'll keep going back to the standard form.

Another one I saw was multiplying large numbers. The starting and ending digits tended to be correct but the middle digits were wrong. Why? Because it's really not doing multiplication at all. It's looking for statistical answers. It's unlikely to have met the exact pair of very large numbers you're multiplying before.

Now pundits will argue that all of these are solvable problems and individually they are. But my suspicion is that there will be a neverending stream of such edge cases and it'll be impossible to trust an LLM's output unless you are knowledgeable enough to fact check it yourself.

Now if your example of identifying zero days, this comes up with what I can only describe as "light positives", meaning it's technically a bug but essentially impossible to exploit. IIRC this came up with the demonstration where someone pointed Fable at some BSD code. I'm not sure if there have been any true false positives and obviously false negatives are impossible to know.

I guess my point is that I think LLMs are way more limited than a lot of people think.

I think the posts mental model of stastically likely prompt completions is spot on.

This was written in July 2023. ChatGPT was released November 2022. No matter your views on AI, surely you can't blame the OP for writing this after a few months ChatGPT was released.

There is a difference between you not liking something and it being wrong. Maybe think about that with your grey matter.