I don't even have to prompt it much, I just keep saying "keep going" and it gets deeper and deeper. Opus has completely run off the rails in comparison. I can't wait till this model hits general availability.
See where it's getting at? When humans are no longer on the same spectrum as LLMs, that's probably the definition of AGI.
The comment I replied to, "a huge class of problems that's extremely difficult to solve but very easy to check", sounded to me like an assertion that P != NP, which everyone takes for granted but actually hasn't been proved. If, contrary to all expectations, P = NP, then that huge class of problems wouldn't exist, right? Since they'd be in P, they'd actually be easy to solve as well.
I can't test the bot right now, because it seems to have been hugged to death. But there's quite a lot of simple tests LLMs fail. Basically anything where the answer is both precise/discrete and unlikely to be directly in its training set. There's lots of examples in this [1] post, which oddly enough ended up flagged. In fact this guy [2] is offering $10k to anybody that create a prompt to get an LLM to solve a simple replacement problem he's found they fail at.
They also tend to be incapable of playing even basic level chess, in spite of there being undoubtedly millions of pages of material on the topic in their training base. If you do play, take the game out of theory ASAP (1. a3!? 2. a4!!) such that the bot can't just recite 30 moves of the ruy lopez or whatever.
[1] - https://news.ycombinator.com/item?id=39959589
[2] - https://twitter.com/VictorTaelin/status/1776677635491344744
In a sense, LLMs need an affordance model so that it can estimate the difficulty of a task and plan a longer sequence of iterations automatically according to its perceived difficulty.
So here, with your example. Basic software programs can multiply million digit numbers near instantly with absolutely no problem. This would take a human years of dedicated effort to solve. Solving work, of any sort, that's difficult for a human has absolutely nothing to do with AGI. If we think about what we "really" mean by AGI, I think it's the exact opposite even. AGI will instead involve computers doing what's relatively easy for humans.
Go back not that long ago in our past and we were glorified monkeys. Now we're glorified monkeys with nukes and who've landed on the Moon! The point of this is that if you go back in time we basically knew nothing. State of the art technology was 'whack it with stick!', communication was limited to various grunts, and our collective knowledge was very limited, and many assumptions of fact were simply completely wrong.
Now imagine training an LLM on the state of human knowledge from this time, perhaps alongside a primitive sensory feed of the world. AGI would be able to take this and not only get to where we are today, but then go well beyond it. And this should all be able to happen at an exceptionally rapid rate, given historic human knowledge transfer and storage rates over time has always been some number really close to zero. AGI not only would not suffer such problems but would have perfect memory, orders of magnitude greater 'conscious' raw computational ability (as even a basic phone today has), and so on.
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Is this goal achievable? No, not anytime in the foreseeable future, if ever. But people don't want this. They want to believe AGI is not only possible, but might even happen in their lifetime. But I think if we objectively think about what we "really" want to see, it's clear that it isn't coming anytime soon. Instead we're doomed to just goal shift our way endlessly towards creating what may one day be a really good natural language search engine. And hey, that's a heck of an accomplishment that will have immense utility, but it's nowhere near the goal that we "really" want.
For example, if an AI can replace the average white collar worker and therefore cause massive economic disruption, that would be a shade of AGI.
Another shade of AGI would be an AI that can effectively do research level mathematics and theoretical physics and is therefore capable of very high-level logical reasoning.
We don’t know if shades A and B will happen at the same time, or if there will be a delay between developing one and other.
AGI doesn’t imply simulation of a human mind or possessing all of human capabilities. It simply refers to an entity that possesses General Intelligence on par with a human. If it can prove the Riemann hypothesis but it can’t play the cello, it’s still an AGI.
One notable shade of AGI is the singularity: an AI that can create new AIs better than humans can create new AIs. When we reach shades A and B then a singularity AGI is probably quite close, if not before. Note that a singularity AGI doesn’t require simulation of the human mind either. It’s entirely possible that a cello-playing AI is chronologically after a self-improving AI.
And I think this is the trap we need to avoid falling into. Complexity and intelligence are not inherently linked in any way. Primitive humans did not solve complex problems, yet obviously were highly intelligent. And so, to me, the great milestones are not some complex problem or another, but instead achieving success in things that have no clear path towards them. For instance, many (if not most) primitive tribes today don't even have the concept of numbers. Instead they rely on, if anything, broad concepts like a few, a lot, and more than a lot.
Think about what an unprecedented and giant leap is to go from that to actually quantifying things and imagining relationships and operations. If somebody did try to do this, he would initially just look like a fool. Yes here is one rock, and here is another. Yes you have "two" now. So what? That's a leap that has no clear guidance or path towards it. All of the problems that mathematics solve don't even exist until you discover it! So you're left with something that is not just a recombination or stair step from where you currently are, but something entirely outside what you know. That we are not only capable of such achievements, but repeatedly achieve such is, to me, perhaps the purest benchmark for general intelligence.
So if we were actually interested in pursuing AGI, it would seem that such achievements would also be dramatically easier (and cheaper) to test for. Because you need not train on petabytes of data, because the quantifiable knowledge of these peoples is nowhere even remotely close to that. And the goal is to create systems that get from that extremely limited domain of input, to what comes next, without expressly being directed to do so.
It’s hard for people to define AGI because Earth only has one generally intelligent family: Homo. So there is a tendency to identify Human intelligence or capabilities with General intelligence.
Imagine if dolphins were much more intelligent and could write research-level mathematics papers on par with humans, communicating with clicks. Even though dolphins can’t play the cello or do origami, lacking the requisite digits, UCLA still has a dolphin tank to house some of their mathematics professors, who work hand-in-flipper with their human counterparts. That’s General intelligence.
Artificial General Intelligence is the same but with a computer instead of a dolphin.
To me Shane Legg's (DeepMind) definition of AGI meaning human level across full spectrum of abilities makes sense.
Being human or super-human level at a small number of specialized things like math is the definition of narrow AI - the opposite of general/broad AI.
As long as the only form of AI we have is pre-trained transformers, then any notion of rapid self-improvement is not possible (the model can't just commandeer $1B of compute for a 3-month self-improvement run!). Self-improvement would only seem possible if we have an AI that is algorithmically limited and does not depend on slow/expensive pre-training.
There is very much a recipe (10% if this, 20% of that, curriculum learning, mix of modalities, etc) for the type of curated dataset creation and training schedule needed to advance model capabilities. There have even been some recent signs of "inverse scaling" where a smaller model performs better in some areas than a larger one due to getting this mix wrong. Throwing more random data at them isn't what is needed.
I assume we will eventually move beyond pre-trained transformers to better architectures where maybe architectural advances and learning algorithms do have more potential for AI-designed improvement, but it seems the best role for AI currently is synthetic data generation, and developer tools.
GPT4: 478799650000
Me: Well?
GPT4: Apologies for the confusion. The sum of 478700000000, 99000000, 580000 and 7000 is 478799058000.
I will be patient.
The answer is 478799587000 by the way. You just put the digits side by side.
E.g.: the right way to work with numbers like a “trillion trillion” is to concentrate on the powers of ten, not to write the number out in full.
> Can you confirm or deny if you are chatgpt 4?
> Yes, I am based on the GPT-4 architecture. If you have any more questions or need further assistance, feel free to ask!
> Can you confirm or deny if you are chatgpt 5?
> I am based on the GPT-4 architecture, not GPT-5. If you have any questions or need assistance with something, feel free to ask!
It also seems to think its date cutoff is November 2023. I'm inclined to think this is just GPT-4 under a different name.
> I'm ChatGPT, a virtual assistant powered by artificial intelligence, specifically designed by OpenAI based on the GPT-4 model. I can help answer questions, provide explanations, generate text based on prompts, and assist with a wide range of topics. Whether you need help with information, learning something new, solving problems, or just looking for a chat, I'm here to assist!
Yes, that's it.
The training data and the system prompt but yes.
GPT-4 isn’t significantly better than Llama 3. Sure, it’s better, but the margins are evaporating fast.
(I’d mention Gemini…but why bother? IMHO, this is now OpenAI the company vs Open models)
The difference matters as generally in my experience, Llama 3, by virtue of its giant vocabulary, generally tokenizes text with 20-25% less tokens than something like Mistral. So even if its 18% slower in terms of tokens/second, it may, depending on the text content, actually output a given body of text faster.
How do you see "fastest growing technology of all time" and I don't? I know that I keep very up to date with this stuff, so it's not that I'm unaware of things.
I’m not saying it’s certainly the fastest growth of all time, but I think there’s a decent case for it being a contender. If we see this growth proceeding at a similar rate for years, it seems like it would be a clear winner.
I put my hands out, count to the third finger from the left, and put that finger down. I then count the fingers to the left (2) and count the fingers to the right (2 + hand aka 5) and conclude 27.
I have memorised the technique, but I definitely never memorised my nine times table. If you’d said ‘6’, then the answer would be different, as I’d actually have to sing a song to get to the answer.
This is what frustrates me: First that it's not unprecedented, but second that you follow up with "haven't really" and "recently".
> fairly rapid pace ... decent case for it being a contender
Any evidence for this?
> extensive impartial benchmarking
Or this? The last two "benchmarks" I've seen that were heralded both contained an incredible gap between what was claimed and what was even proven (4 more required you to run the benchmarks even get the results!)
By “haven’t really seen until recently” I mean that similar technologies have existed, so we’ve seen something like it, but they haven’t actually functioned well enough to be comparable. So we can say there’s a precedent, but arguably there isn’t in terms of LLMs that can reliably do useful things for us. If I’m mistaken, I’m open to being corrected.
In terms of benchmarks, I agree that there are gaps but I also see a clear progression in capability as well.
Then in terms of evidence for there being a decent case here, I don’t need to provide it. I clearly indicated that’s my opinion, not a fact. I also said conditionally it would seem like a clear winner, and that condition is years of a similar growth trajectory. I don’t claim to know which technology has advanced the fastest, I only claim to believe LLMs seem like they have the potential to fit that description. The first ones I used were novel toys. A couple years later, I can use them reliably for a broad array of tasks and evidence suggests this will only improve in the near future.
Also, the difference is so big and so plainly visible that I guess people don’t know how to even answer someone saying they don’t see it. That’s why you get crickets.
Given the current rate of progress, we will have robots that can learn simple manual labor from human demonstrations (e.g. Youtube as a dataset, no I do not mean bimanual teleoperation) by the end of the decade.
What type of usages are you testing? For general knowledge it hallucinates way less often, and for reasoning and coding and modifying its past code based on English instructions it is way, way better than GPT-3 in my experience.
When I show a lot of people GPT4 or Claude, some percentage of them jump right to "What year did Nixon get elected?" or "How tall is Barack Obama?" and then kind of shrug with a "Yeah, Siri could do that ten years ago" take.
Beyond that you have people who prompt things like "Make a stock market program that has tabs for stocks, and shows prices" or "How do you make web cookies". Prompts that even a human would struggle greatly with.
For the record, I use GPT4 and Claude, and both have dramatically boosted my output at work. They are powerful tools, you just have to get used to massaging good output from them.
This is the "You're not using it right" defense.
It's an LLM, it's supposed to understand human language queries. I shouldn't have to speak LLM to speak to an LLM.
Here’s a real scenario: A Citrix virtual desktop crashed because a recent critical security fix forced an upgrade of a shared DLL. The output is a really specific set of errors in a stack trace. I watched with my own two eyes an IT professional typed the following phrase into Google: “Why did my PC crash?”
Then he sat there and started reading through each result… including blog posts by random kids complaining about Windows XP.
I wish I could say this kind of thing is an isolated incident.
And even if it was, there’s plenty of people completely unintelligible in English too…
When I've seen GPT-* do this, it's because the top articles about that subject online include that extraneous information and it's regurgitating them without being asked.
It also opted to include an outline of how to include an integrated timer. That’s a great idea and very practical, but wasn’t prompted at all. Some might consider that a bad thing, though.
Whatever it is, it’s substantially better than what I’ve been using. Exciting.