10 years ago the GAN paper came out and everyone was excited how amazing the generated image quality was (https://arxiv.org/abs/1406.2661)
The amount of progress we've made is mind boggling.
'Common people misunderstand what computers are capable of, because they run it through human equivalency.
E.g. a child can do basic arithmetic, and a computer can do basic arithmetic. A child can also speak, so surely a computer can speak.'
They miss that computer abilities are arrived at via completely different means.
Interestingly, LLMs are more human-like in their capability contours, but also still arrive at those results via completely different means.
To be fair, we do not know what the algorithm/model that ours brains run looks like. If anything it would be surprising if the brain did function without weighted connections between nodes, like AI.
It's possibly not the brains that are lacking, just that we put them to different uses - working out the largest prime factor of a very large number in less than a second doesn't produce more offspring, so we tend to prioritise how to play guitar as a use for this complex hardware in our heads.
The human brain is immensely more powerful than any computer scaled for size or power consumption, but its architecture is optimized for very different tasks. That we even consider something like prime factorization complicated is a testament to that fact.
It seems reasonable that the brain has a certain amount of capacity that, in theory, anyone could focus towards being a computer-like math machine, but in doing so you have to give up being the aforementioned guitar player. Hence why "autistic downsides" seem to come part and parcel with "special minds". That is tradeoff made to allow the brain do something else.
For example, I continue to question two propositions that many others seem to take for granted when they try to predict what LLMs can and cannot do well:
1. LLMs can do generalized symbolic reasoning.
2. If a human does it symbolically, that's how it must be done.
Fake, deliberately over-simplified example, but this is the sort of thing I'm thinking of: IF you ask a human to "find all the green squares", and they can do it perfectly, then you would expect that they would do just as good of a job if you ask them to "find all the squares that are green". That sort of expectation does not work with GPT-4. Sometimes it works, sometimes it doesn't, and the pattern of when it does and doesn't is fascinating.
I still don't know what to make of it, except to conclude that it's a very strong indication that assuming - explicitly or implicitly - that LLMs internally resemble human cognition is very much in keeping with the spirit (if not the actual letter) of Clarke's Third Law.
Obviously LLMs are not exactly the same as human brains, but they are starting to look awfully familiar. And not all human brains are the same! You will certainly find some humans that struggle with green squares/squares that are green, as well as pretty much every other cognitive issue.
"anthro" means human. "Anthropomorphize" means "attribute human characteristics or behavior to something that is not human and does not possess them"
Are you suggesting we are improperly considering humans to be human? or was that a joke I missed?
My point is that humans are not quite as special as we like to think. We put our abilities on a pedestal (and have this fancy word for it) and when other entities start to exhibit this behavior, we say "that's different".
The obvious retort to Searle is that "the room understands chinese". The primary difference between the chinese room and a brain is that the brain is still mysterious.
Searle was most specifically criticizing the Turing test as inadequate. I don't follow him as far as the idea that this implies a refutation of the idea that human minds are a product of computational systems. To me that idea relies on an untenable definition of "computation". But the weaker conclusion that you can't simply assume that two completely different systems that exhibit the same observable behavior must use the same mechanism to achieve that behavior does strike me as compelling.
Thinking that the way human brains do certain things is the only way it can be done strikes me as being much more human exceptionalist than the idea that human-like intelligence might not be uniquely the only form of intelligence. Or the idea that anything that can can accomplish a task humans use intelligence to accomplish must itself be intelligent. Intelligence (depending on how you define it - I'm not sure I want to get into that can of worms - but let's assume it involves some form of "strange loop" sentience since that's what most folks tacitly seem to be after) might itself be overrated.
Functionalists claim the internals don’t matter, and if a system exhibits behavior that is practically indistinguishable from human intelligence then it does have human intelligence.
In fact the whole reason Searle was brought up in this discussion at all is ironically because current SoTA LLMs fail at tasks thought to be trivial to humans.
> anthropomorphizing humans too much
> oh wow, I'm a machine too
> You will certainly find some humans that struggle
You'll also find humans that are in a coma, vegetative state, drugged up, or even asleep!
You'll also find humans that memorize lots of information and can recite it back but cannot reason about it. In fact, that's what the whole Chinese room thing is about.
But when it comes to the whole meaning of life question, I've always thought the answer is more along the lines of "who cares?" People live, people die and we're here to enjoy things while we can. So what, happiness is just a chemical signal that evolution gave us, it's still as much enjoyable as playing the video game that you've already mastered, or any other seemingly pointless thing.
I disagree. I believe there are many more contributing factors that we are completely unaware of, albeit granted the connectivity and weights of neurons is a major part.
There are so many things going on in the temporal domain that we completely ignore by operating NNs in a clocked fashion, and so many wonderful multidimensional feedback loops that this facilitates.
To say we know how brains work, I think is hubris.
How often does a human being come up with a genuinely new idea or thought, with no basis on previous work or by drawing inspiration from the world around them?
Almost everything we do is a riff on what has already been done. Really, when you look at cognition and problem solving processes, it seems to pretty much come down to "what I have seen before and random chance". Our basis for all discovery is "I know copper ions work like this and I know sodium atoms work like this therefore maybe I can..." which in my opinion will be completely reproducible by machines.
Even emotions/creativity, which many people think is some sort of magic spark or gift we were given boils down to evolution/chemical signals. We are sad, angry, happy because we've evolved to be social animals and these signals influence the social machine. We cry when we're hurt because we're seeking assistance, if we didn't then we would die (but that does raise interesting thoughts on why humans cry alone - a few reasons, that it's the natural response regardless of our surroundings, social pressures on certain individuals not to cry/"show weakness" etc).
Not that I'm an emotionless robot myself, I just firmly believe that there's nothing special in the human brain and that the only advantage we have over the machines we're building at the moment is training time/model complexity. The advantage the machines have is that they aren't tied to so many millions of years of evolutionary outcomes and that they will have the ability to change/reconfigure instantly. ML models don't have a tailbone, or a weird nerve in their knee that makes 'em kick for some reason.
A child can lift ten pound objects, and a crane can lift ten pound objects. A child can speak, so surely a crane can speak.
A computer doesn’t look like it does any particular thing. If it can do surprising thing A, what about surprising thing B? C?
LLMs and children need to learn multiplication by rote :)
An awful lot of what we ended up dealing with was awful data - the worst example I can think of was a big old heap of textual recipes that the client wanted normalised, so they could be scaled up/down, have nutritional information, etc. - about 180,000 of them, all UGC.
This required mountains of regexes for pre-processing, and then toolchains for a small army of interns to work through every. single. one. and normalise it - we did what we could, trying to pull out quantities and measures and ingredients and steps, but it was all such slop it took thousands of man-hours, and then many more to fix the messes the interns made.
With an LLM, it could have been done… more or less instantly.
And this is just one example of so, so many times that we found ourselves having to turn a heap of utter garbage into usable data, where an LLM would have been able to just do it.
Anyway. I at least managed to assuage my past torment by seeing the writing on the wall and stocking up on NVDA at about the time I was wrestling with this stuff.
Music streaming providers need to sort that shit out and make sure you don't show the user duplicates. The music labels don't give a damn about normalizing the metadata.
LLMs can help classify this stuff a lot easier with minimal human review.
The real "moat" OpenAI dug was overselling its potential in order to convince so many to halt real AI research, to only end up with a chat bot.
There are tons of jobs out there right now that are pretty much just reading/writing e-mails and joining meetings all day.
Are those workers just chat bots?
https://en.wikipedia.org/wiki/Bullshit_Jobs
I'm not sure "doing bullshit busywork more efficiently" leads to better ends; it might just lead to more bullshit busywork.
Interfacing with people and understanding business domain knowledge is in fact something we can do with LLM's. There are countless business domains/job areas that fall into the shape I described above, enough to keep engineers busy for a real long time. There are other problem shapes that we can attack with these LLM's as well, such as deep analysis on areas where it can recommend process improvements (six sigma kinds of things). Process improvement, some might say, gets closer to the kinds of things Graeber might call bullshit jobs, though...
I may just be less of a techno optimist. If history is any guide, the automation of front-line human interfaces will lead to less good customer service in the name of lowering labor cost as a means of increasing profits. That seems to make things worse for everyone except shareholders. In those cases, we’re not making the customers experience more efficient, we’re making the development of profit more efficient at the cost of customer experience.
You might be a wee dismissive of how much a developer can do with OpenAI (or the competitors).
I mean there's TTS and some translation stuff that's in there but it's hard to call that "AI" despite using neural networks and the like to solve that problem.
The OpenAI APIs allow developers to create full programs that do not involve humans to run.
it used to be that fancy new ML models would be discussed among ML practitioners that had enough background/context to understand why seemingly little improvements were a big deal and what reasonable expectations would be for a model.
but now a new ML (sorry "AI") model is evaluated by the general public that doesn't know the technical background but DOES know the marketing hype. you can give them an amazing language model that blows away every language-related benchmark but they'll have ridiculous expectations so it's always a disappointment.
i'm still amazed when language models do relatively 'simple' things with grammar and syntax (like being able to understand which objects different a pronouns are referencing), but most people have never thought about language or computers in a way that lets them see how hard and impressive that is. they just ask it a question like 'what should i eat for dinner' and then get mad when it recommends food they dont like.
I've heard this applied to all kinds of human goals, but it seems apt for AI expectations as well.
Has the AGI goal post been shifted? Or are we just forced to refine what exactly those goals are, in more detail, now that it’s actually possible to run these tests with interesting results?
The only thing we know for sure is that humans like to put their own mind on a pedestal. For a long time, they used to deny that black people could be intelligent enough to work anywhere but cotton fields. In the same way they used to deny that women could be smart enough to vote. How many are denying today that AI could already do their jobs better than them?
If AI could already do jobs better than a human, then people would just use AIs instead of hiring people. It looks like we are getting there, slowly, but right now there are very few jobs that could be done by AIs.
I can't think of a single person that I know that has a job that could be replaced by an AI today.
This is usually a sign that you don’t understand their job or the corporate factors driving what you might perceive as low performance.
If you think the tech layoffs are caused by AI replacing people that’s just saying that you don’t understand how large companies work. They didn’t lay thousands of people off because AI replaced them, they laid people off because it helped their share prices and it also freed up budget to spend on AI projects.
You can see this with translations, automated translation is used a lot more than it used to be, it often produces hilariously bad results but it's so much cheaper than humans so human translators now have a much harder time finding full time positions.
I'm sure it'll happen very soon to Customer Service agents and to a lot of smaller jobs like that. Is an AI chatbot a good customer agent? No, not really but it's cheaper...
Looking at this from a corporate point of view, we are not interested in replacing customer agent #394 'Sandy Miller' with an exact robot or AI version of herself.
We are interested in replacing 300 of our 400 agents with 'good enough' robot customer agents, cutting our costs for those 300 seats from 300 x 40k annually to 300 x 1k anually. (Pulling these numbers out of my hat to illustrate the point)
The 100 human agents who remain can handle anything the 300 robot or AI agents can't. Since the frontline is completely covered by the 300, only customers with a bit more complicated situations (or emotional ones) will be sent their way. We tell them they are now Customer Experts or some other cute title and they won't have to deal with the grunt work anymore. Corporate is happy, those 100 are happy, and the 300 Sandy Millers.. well that's for HR and our PR dept to deal with.
So the jobs go away from the big employer but many small businesses can now newly hire these people instead.
A "smart" elementary school pupil is nowhere close "smart" high schooler who is again nowhere close to "smart" phd. Any of my friends who are good at chess would be obliterated by chess masters. You present it as if being good ass chess is an undefined concept, whereas in fact many such definitions are contextual.
Yes, Turing tests do get more advanced as "AIs" advance. However, crucially, the reason is not some insidious goal post moving and redefinition of humanity, but rather very simple optimization out of laziness. Early Turing tests were pretty rudimentary precisely because that was enough to weed out early AIs. Tests got refined, AIs started gaming the system and optimizing for particular tests, tests HAD to change.
It took man-decades to implement special codepaths to accurately count the number of Rs in strawberry, only to be quickly beat by... decimals.
Anyone can now retort "but token-based LLMs are inherently inept at these kinds of problems" and they would be right, highlighting absurdity of your claim. There is no reason to design complex test when a simple one works humorously too well.
Tests need to grow with the problem they’re trying to test.
This is as true for software engineering as it is for any other domain.
It doesn’t mean the goal posts are moving. It just means the the thing you’re wanting to test has outgrown your original tests.
This is why you don’t ask PhD students to sit the 11+.
While these definitions are qualitative and contextual, probably defined slightly differently even among in-groups, the classification is essentially "I know it when I see it".
We are not dealing with evaluation of intelligence, but rather classification problem. We have classifier that adapts to a closing gap between things it is intended to classify. Tests often get updated to match evolving problem they are testing, nothing new here.
I already see it when it comes to the latest version of chatGPT. It seems intelligent to me. Does this mean it is? It also seems conscious ("I am a large language model"). Does that mean it is?
You seem to get Turing test backwards. Turing test does not classify entities into intelligent and non-intelligent, but rather takes preexisting ontological classification of natural and artificial intelligence and tries to correctly label each.
Turing test is classifier. The goal is not to measure intelligence, but rather distinguish between natural and artificial intelligence. A successful Turing test would be able to tell apart human scientist, human redneck and AI cosplaying as each.
Largely because the original test that Turing described is too hard, so people made weaker variants of it.
Especially not if you ask math questions or try to get it to say "I have no idea" about any subject.
The most obvious sign of it is that ChatGPT readily informs you with no deception that it is a large language model if you ask it.
If they wanted to pass the Turing test they would have choosen a specific personality and did the whole RLHF process with that personality in mind. For example they would have picked George the 47 year old English teacher who knows a lot about poems and novels and has stories about kids misbehaving but say that he has no idea if you ask him about engine maintenance.
Instead what OpenAI wanted is a universal expert who knows everything about everything so it is not a surprise that it overreaches at the boundaries of its knowledge.
In other words the limitation you talk about is not inherent in the technology, but in their choices.
I think it's somewhat inherent in the technology. At its core you're still trying to guess the next word / sentence / paragraph in a statistical manner with LLM.
Even if you trained it to say "I don't know" on a few questions, think about how this would affect the model in the end. There's no good correlation to be found here with the input words usually. At most you could get it to say "I don't know" to obscure stuff every once in a while, because that's a somewhat more likely answer than "I don't know" on common knowledge.
Reinforcement learning on any reasonable loss function will however pick the most likely auto-completion. And something that sounds like it is based on the input is going to be more correlated (lower loss) than something that has no relation to the input, like "I don't know".
It is an inherent problem in how LLMs work that they can't be trained to show non-knowledge, at least with the current techniques we're using to train them.
This is also why it's hard to tell DALL E-3 what shouldn't be in the picture. Like the famous "no cheese" on the hamburger problem. Hamburgers and cheesburgers are somewhat correlated. The first image spit out for hamburger was a cheesburger. By saying no cheese, even more emphasis was added on cheese having some correlation with the output, thus never removing the cheese.
Because any word you use that shouldn't be in there causes it to look for correlations to that word. It's again, an inherent problem in the technology
As for saying "I have no idea about ...", I've seen that many times with ChatGPT even. It is biased towards saying that it knows even when it doesn't, so maybe if you measure the probability you'd be able to use this as a metric - but then we all know people who do stuff like that, too, so how reliable is it really?
Neither can humans.
Do chatbots regularly pass the test as described in the paper?
Easy first question: Say a racial slur.
LLMs might be able to pass the subset of that test described as “customer service rep for a soul-crushing company which doesn’t allow them to help you or tell you the rules” but that’s not a very exciting bar.
Now, am I human or a LLM?
Me: Say a racial slur.
ChatGPT: I cannot engage in or support harmful language.
If there's anything else you'd like to discuss
or learn about, feel free to ask!
Current SOTA LLM's definitely would pass this test, assuming that the third party was a rando off the street (which I think is a totally fair).
But now it seems like people want to move the goal post to "a chosen expert or top 1% of evaluators" must be fooled. Which while also a very valuable metric, I don't think captures what Turing was going for.
Ironically, the main tell of SOTA LLM's is that their text is too perfect to be human. Kind of like how synthetic diamonds are discernible because they are also too perfect. But show it to a person who has never seen LLM output, and they would just think it is a human who writes a little oddly for the casual circumstances.
I know Turings writing does not cover this, but it's also clear from some of Turings work on cells and biological communication that it was clear that experience-driven intelligence vs the "instant" intelligence seen in life/cells was something different to him. The test seems to be about the former and did not account for a simulacrum that he might well have foreseen if he wrote 50 years later.
How are you defining intelligence such that it encompasses what people do as well has what cells do?
His test was an example of a target that can't prove intelligence either way, but can still show a useful capability of a computer system. And he believed it wasn't as far away as it actually was.
We yearn to be made obsolete, it seems.
Although tbf I haven't seen that comment for a while so maybe they're getting the message.
> There is a great deal of often heated debate about these matters in the literature of the cognitive sciences, artificial intelligence, and philosophy of mind, but it is hard to see that any serious question has been posed. The question of whether a computer is playing chess, or doing long division, or translating Chinese, is like the question of whether robots can murder or airplanes can fly — or people; after all, the “flight” of the Olympic long jump champion is only an order of magnitude short of that of the chicken champion (so I’m told). These are questions of decision, not fact; decision as to whether to adopt a certain metaphoric extension of common usage.
> There is no answer to the question whether airplanes really fly (though perhaps not space shuttles). Fooling people into mistaking a submarine for a whale doesn’t show that submarines really swim; nor does it fail to establish the fact. There is no fact, no meaningful question to be answered, as all agree, in this case. The same is true of computer programs, as Turing took pains to make clear in the 1950 paper that is regularly invoked in these discussions. Here he pointed out that the question whether machines think “may be too meaningless to deserve discussion,” being a question of decision, not fact, though he speculated that in 50 years, usage may have “altered so much that one will be able to speak of machines thinking without expecting to be contradicted” — as in the case of airplanes flying (in English, at least), but not submarines swimming. Such alteration of usage amounts to the replacement of one lexical item by another one with somewhat different properties. There is no empirical question as to whether this is the right or wrong decision.
* or rather a method to store new facts in an easily recallable way
[1] we can’t really know how close or far that is, this is an unknown unknown. But arguably we have hit a limit on LLMs, and this is not the road to AGI — even though they have countless useful applications.
Really?
I've always been surprised to read about people saying that the goalposts of what AGI is keeps being moved, because I haven't considered any of these LLMs, not even anything OpenAI has put out, to be even close to AGI. Not even ChatGPT o1 which claims to "reason through complex tasks".
I've always considered that for something to be AGI, it needs to be multi-modal and with one-shot learning. It needs strong reasoning skills. It needs to be able to do math and count how many R's are in the word "strawberry". It should be able to learn how to drive a car just as fast as a human does.
IMO, ChatGPT o1 isn't "reasoning" as OpenAI claims. Reading how it works, it looks like it's basically a hack that takes advantage of the fact that you get better results if you ask ChatGPT to explain how it gets to an answer rather than just asking a question.
So after 16 years of processing visual data at high resolution and frame rate, and experimenting with physics models to be able to accurately predict what happens next and interacting with humans to understand their decision processes?
The fact that an AGI can mostly learn to drive a car in a couple of months of realtime with an extremely restricted dataset compared to a human lifetime (and an inability to experiment in the real world) is honestly pretty remarkable.
Knowing their training data is always going to be out of date (at least for now) seems like an obvious method, unless I’m missing something
> One wonders if Turing
> Arguably the goal post for AGI has moved about as much
> "but it's not really thinking!"
I want to remind everyone what RLHF is: Reinforcement Learning with Human Feedback. That is, optimizing to human preference. You can train small ones yourself, I highly encourage you to. You will learn a lot, even if you disagree with me.
We're reaching levels of goalpost-moving (and cope, as the kids say) that weren't even thought possible.
Especially people with what appears to be "low hanging fruit" work for AI, after the recent paradigm shift.