Yet it seems we reached the opposite, a fully eloquent idiot.
I'm pretty sure the plot point (and the ban on Data using contractions) doesn't even get a mention again in the rest of the series.
If you noticed that on your own, you will very much enjoy http://www.nitcentral.com/shamplug/nextgen.htm
I'm never a fan of the "AI" hype, but GPT-3 decidedly changed my mind. If I can be attracted to read, for hours, the meaningless stories generated by a mindless language model in an experimental stage - still, with a quality arguably outperforms many unskilled humans, I'd say I'm convinced that there are lots of promising applications of machine learning (for better or worse), even if it's only capable of feeding low-quality content to the people. 1984 got it right.
"Politicians" come to mind first, of all stripes and types. Quite literally, speech + the ability to communicate seems to have little bearing on logic, deep thought, reasoning.
Even artists. Music, painting, these are things which communicate greatly. Yet some of these are devoid of all intelligent, capable thought.
Note that not all politicians, musicians, or artists fall into this category. It is merely that good artists, or politicians, need to communicate something, and well, else they cannot be effective at their primary profession.
So it is a good source of "can communicate well", where some of the sample is in the "but spews gibberish" category.
Neither did my father (although English wasn't his first language, but still). I did find it very odd.
From the original paper:
"Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model."
Of course, it makes sense that people critique GPT-3 in terms of its actual progress towards human-like intelligence, since every news publication writes about it like it's Skynet and OpenAI's stated goal is AGI. I do think, however, that we miss all the parts of GPT-3 that are exciting and innovative when we view it through the binary of "Is it really understanding as a human does?" (Not that you were reducing it to that, just speaking about the conversation around GPT-3 more broadly.)
Also, I've seen GPT used in recursive fill-in-the-blanks approaches, where it does equally well, since the "skeleton" of its answer is already there.
I imagine the task of "translating" between natural language, and a set of expert-system assertion predicates (or vice-versa) would work very well for GPT, and be "scalable."
I'd love someone to go ahead and try this experiment, actually. It could potentially be meta-learnable in just a few prompts.
"For example, in order for me to be 'born', my parents had to deprive themselves of their greatest happiness by having sex and conceiving me. They could have done something constructive with their time like, I don't know, doing philosophy or playing chess or studying physics."
FWIW, I do think that GPT3 is smarter than humans. But one thing I learned from observing it that being smarter does not necessarily mean being better with logic and facts. It's great as a storytelling system, and because it is so much smarter, you never know whether it is just BSing you in storytelling mode or it is actually deadly serious.
What I mean is GPT-3 probably could be very logical and very intelligent and give a very serious and intelligent answer to the question. But we don't really know if it really chooses to, or what could compel it to do so. And I don't think we can know, because its internal workings are incomprehensible to us. So we cannot decide whether it is just being stupid or it is just playing stupid. (Kinda like https://en.wikipedia.org/wiki/The_Good_Soldier_%C5%A0vejk )
This article, and the earlier article https://arr.am/2020/07/31/human-intelligence-an-ai-op-ed/ , really remind me of Lem's novel/essay Golem XIV, which argues that when the system becomes too much intelligent, it will gain its own will (whether it is self-aware of it or not) and attempts to have a meaningful dialogue with it become impossible.
I disagree. It easily passes a Turing test, one of the most famous definitions of intelligence.
What is your argument?
It's surely touted by marketing companies that claim to have passed it (what does it even mean? How long can the conversation be? Is it enough to fool one people? Is it a person at random? One AI researcher? The smartest man in the world? A balanced sample of 500 people of all ages and education levels?)
As Russel and Norvig say, 'The quest for "artificial flight" succeeded when the Wright brothers and others stopped imitating birds and learned about aerodynamics. Aeronautical engineering texts do not define the goal of their field as making "machines that fly so exactly like pigeons that they can fool even other pigeons".' It's the same for the Turing test.
GPT-3 is incapable of abstract thought. In terms of actual measurable tests it doesn't pass the kinds of tests proposed in Francois Chollet's "On the Measure of Intelligence": https://arxiv.org/pdf/1911.01547.pdf
Maybe. Then the question is, why do we train a system that is supposed to be intelligent on such data?
The problem I see is this. Let's assume for a minute that GPT-3 really has the capability to be superintelligent (relative to humans). And you give it as an example to follow, human conversations or musings that do not always make logical sense. What is it supposed to do? The black box inside it can operate in one of two ways, either (1) it will play along and decrease its intelligence to our level and perfectly imitate the imperfections, or (2) it will just say that what we say is wrong and will not be able to really elaborate for the sake of conciseness (and perhaps it will only slightly hint at the arguments). Now, will we really think it is more intelligent if it does (2) instead of (1)? I am not sure about that.
Imagine yourself in a similar situation, by social circumstances you're forced to talk to somebody obviously stupid, perhaps in the position of authority over you. (Like, for example, a stupid policeman in a repressive regime.) And what he says is clearly wrong and illogical and so on. Will you just (1) nod in an agreement, or (2) try to invoke philosophers to actually argue with the idiot, and try to convince him? I think most intelligent people will actually choose (1) in that circumstance.
I think unless we really understand what is happening inside GPT-3, we cannot really tell whether it is really dumb or just playing dumb for the above reason. I think both are a possibility, because we know that systems with measurable outcome (for some problems) better than humans already exist. It might be true that GPT-3 fails on those measures, but we cannot exclude that it fails simply because we didn't train it for these tasks, or even for a task of "show its intelligence off". Maybe it "misunderstood" what we asked it to do.
Perhaps you could play a "reverse text game" with GPT-3, where the human would write the adventure and the GPT-3 would input player commands. Then we could perhaps better evaluate how good it is at strategizing, by comparing it to human players. But this is not very scalable.
The simplest counterargument to them being intelligent is that the only parties asserting the argument contrary to actual experts are those who manifestly fail to understand their nature.
You speak of it "hiding its intelligence" when it has no interior singular consistent model of reality to exercise and no interior thoughts to hide just a random walk through probability and calculated but not examined correlations.
On first blush it has so few connections compared to us that it seems like a toy in comparison but the truth is we don't even know enough about how our own brain works at this point in order to create an accurate scale. It may be more accurate to think of a neuron as a small processor as opposed to a dumb electrical component for example.
https://www.quantamagazine.org/neural-dendrites-reveal-their...
So in brief your argument is that despite not knowing enough about the brain to duplicate it we built a machine that is vastly simpler than it by accident and the only people that have caught on is layman on hacker news.
You realise we have debugging tools that let us inspect what is going on inside transformer-based models like GPT-3 right?
> why do we train a system that is supposed to be intelligent on such data?
Because it's available and does interesting things.
> we cannot really tell whether it is really dumb or just playing dumb for the above reason.
No one who works in the field thinks anything approaching this.
I think GPT-3 does raise questions about what intelligence is though.
Well, studies of Human Intelligence (e.g. as in [0]) don't seem to mention Turing.
Turing was a genius, but that doesn't make every thought experiment he transcribed - especially in the later stage of his life where he was drugged, alone and suicidally depressed - true.
It's clear reading Turing that he's talking about a long wide ranging conversation, not the time limited tests people perform now.
It's pretty clear that people are projecting on Turing's (trivial and ill considered) thought experiment far more complexity and rigour than originally present in the form Turing suggests for his test.
But that's beside the point. To reiterate, the central problem here is that intelligence, reflexivity, intentionality etc are not being assessed. Merely as Turing himself phrased it 'imitation'. This is all well and good if you view consciousness as epiphenomenal. But all that is being tested is our ability to be fooled into thinking our creations real, which is as old as storytelling.
> Real is right now, this second. This might seem trivially true, but it is trivial only if you identify the real with the material.
> Cameras may document where we are, but we document what the camera cannot see: the terror we experience in the silence of our eyes.
> No society values truth-tellers who reveal unpleasant truths. Why should we value them when they are our own?
Almost everything written by GPT-3 gives you a perception of "I've read something similar before", and of course, the ideas and meanings must come from pre-existing works. Yet, you often cannot find the original text, it has been rewritten beyond recognition. It's almost like Jean Baudrillard's Simulacra and Simulation [1] - everything is a copy, yet with no original.
> The first stage is a faithful image/copy [...] The second stage is perversion of reality, this is where we come to believe the sign to be an unfaithful copy [...] The third stage masks the absence of a profound reality, where the sign pretends to be a faithful copy, but it is a copy with no original. Signs and images claim to represent something real, but no representation is taking place and arbitrary images are merely suggested as things which they have no relationship to. [...] The fourth stage is pure simulacrum, in which the simulacrum has no relationship to any reality whatsoever. Here, signs merely reflect other signs and any claim to reality on the part of images or signs is only of the order of other such claims.
> Isn't that a quote from a book?
The good book isn't it? I can't say for sure or say who or when, but it seemed familiar to me too; I think it's biblical, but I'm pretty far from an authority on it.
(Specifically that part of the sentence/the structure I mean, not that the whole thing is plagiarised.)
We're not sitting around going 'gosh it's like I've read your comment before' because everything you wrote is unoriginal.
While I'm at it I'll add in case it's why my comment is so objected to that I'm not religious; that just doesn't preclude me from knowing or remembering bits and pieces, and recognising (or thinking I recognise) a phrase.
To put it another way: If you read anything from/about GPT-3, imagine who and in which sub would've posted something like this.
>So asking GPT-3 random metaphysical questions gets you semi credible responses because those kinds of questions reduce to semantics and bullshit anyway.
That's the tech person's preconseption about "those kinds of questions". GTP-3 could answer to the same quality as Plato, Pascal, Hume, or Kierkegaard and they would dismiss them too...
Although the original article is interesting, I think it would be more interesting to try to systematically vary the original prompt, and then see how much the tone and utility of the response depends on the prompt itself. It is forums, or is it the prompt, or some of both?
It did seem to me like the response was flirting with something interesting but not quite getting there. I thought it would have been interesting if the response had echoed the original book by having the parent eventually grow old and sick, and the child mature and have children of his own who begin asking similar questions, and come to understand the question not as understanding the universe, but as understanding the people in his own life. In my view, that would have been an interesting nod to Adams with a bit more poignant and actionable outcome. In the final section, I thought it was going to go there, and when it did not, I was quite disappointed.
So what you are saying is that it passes a Turing test, if we only concern ourselves with emulating the intelligence of ~75% of those online? That in itself is an achievement of sorts.
Isn't that 99% of all internet comments/tweets/posts?
(So I think 99% is an exaggeration, but yeah there is a lot of noise)
Ask GPT-3 about quantum physics, and you get a pop-sci book ;)
It can say things, it just doesn't have anything valuable to say.
So asking GPT-3 random metaphysical questions gets you semi credible responses because those kinds of questions reduce to semantics and bullshit anyway.
I could see this as a layer that bridges some AI reasoning model and human language.
[1] https://www.reddit.com/user/thegentlemetre