Transformers. AGI means Artificial General Intelligence. The transformer architecture enables the transfer of knowledge to new domains in arbitrary ways, allowing for the solving of arbitrary problem domains.
What if intelligence is an product of consciousness, and consciousness is an product of something that can never have a physical definition and is always ethereal... i.e. a "soul".
If we can achieve AGI simply through more and more computation, no matter how novel it is, its ultimately ifs, loops and arithmetic... then surely the human experience is ultimately just a 'wet LLM' (or whatever we end up calling the machine learning technology behind AGI).
Uhh again the "only human has soul" argument. What means by soul, please define this word, philosopher. Does bird know language grammars indicate that soul exist? Does fish can do arithmetic up to 5 show that their soul exist? Does elephant do funeral to their dead fellow show that there is a soul? Can this humancentric ideology just go away?
Like humans, they have physical bodies and the breath of life in them. For the sake of what this person was talking about it seems like it would apply to their intelligence as well.
What’s the difference between real consciousness and simulated in silico consciousness? If the fake consciousness is good enough there shouldn’t be any.
Is a soul made out of matrix multiplications and dot products worse than a soul made of neurons?
This is the kind of question that every NonSTEM people would struggle at. Given function F and G. For all input x, resulting F(x) = G(x), this indicate that obviously F and G is the same thing whether inside or outside.
I like to call it carbon chauvinism. There are people who believe that no matter how high the simulation fidelity, no matter the level you simulate the neurons, you can't simulate consciousness. I think they can be safely ignored until after we've tried.
Given that Harry Potter, Jedi Knights and Souls are fantasy items from literature your point with regards to testable silicon based intelligence is what exaclty?
This is kind of where I've been thinking lately. If I can speak of human bodies like computer hardware, there is a physical limit to our hardware and how much data it can process, and yet intelligence seems to be something beyond the hardware. Something that the living 'you' brings to your hardware.
We already have machines that are generally intelligent and require as much energy as a few light bulbs. Why wouldn’t it be eventually possible to replicate them in silicon?
There are plenty of animals with brain. We weren’t able to use them for their cognitive abilities so far. So even if we would replicate the hardware, software might prove challenging.
Over a hundred years ago, Babbage might have said:
>We already have machines that are generally intelligent and require as much energy as a few coalgas lights. Why wouldn’t it be eventually possible to replicate them in brass and steam?
My understanding of the argument of this article is that the conceptual design that replicates intelligence is what the industry has failed to generate today. Simply stating that it might be possible to create is failing to engage; the massive increase in compute power from Babbage to McCarthy didn't give us AGI, because they didn't figure out the right design to reason about anything in even a hundred times a human's energy consumption. If from McCarthy to today we still haven't actually found the proper recipe it just might be worth considering the point the article's making in spite of our other advancements since then.
Because they aren't machines, we don't understand how they work and it's very much an open question whether we ever will. OpenWorm tried and failed the get the fully documented behaviours of the C. Elegans nematode (for which we know the complete connectome and the full lineage of every one of the 959 cells of its body) to emerge from simulations of its 302 neurons. We don't understand how an individual neuron works. We don't even understand what an individual neuron really does and the brain contains 100 billion of them. The hubris involved in looking at biological nervous systems and thinking "how hard can it be? Give me a big data centre and 5 years!" is staggering.
I’m specifically speaking out against the bit data center approach and only saying that it’s demonstrably possible to do general intelligence on a small, low power machine.
Probably shouldn’t have mentioned the silicon though.
These meat machines aren't necessarily computers, that may be only our best available metaphor (previous metaphors for the brain have I think included telephone exchanges, steam engines and plumbing), and although in principle silicon can simulate anything, in practice not so much: there have been various projects to simulate C. elegans, such as OpenWorm, and they fail because the worm depends on the physics of its environment (possibly including the environment inside the worm) and it's all more subtle than just its nervous system. So yeah, it's reasonable that it might eventually be possible to replicate the human brain in something, once we have the slightest clue how and what.
> Why wouldn’t it be eventually possible to replicate them in silicon?
I mean if you simulate all the chemical processes in some being then yeah probably you get close (assuming you get everything right), let’s assume there is no unknown in how the atoms in our bodies interact.
Sounds like an expensive project.
If you are _not_ talking about a perfect emulation/simulation of such a machine, I will pose your question back to you: why _would_ it be possible to do something we don’t know what it is? Seems rather contrived to say “we can do this thing we don’t know what it is”.
That's exactly this 'generally intelligent' that confuses minds. People believe chat gpt has a consciousness but it does not at all. It's just a model of statistics. The first step toward AGI first of all is consciousness on silicon.
And with the current models, the only thing I see is : "let's add more neurons, give it more data to train, and let's hope a mind will come out of these neurons". It helps understand the human brain, and help humanity and all, but I'm sure we are missing some 'theorical ingredients' to get a recipe for a full working AGI.
The future is not known to us. But given how inefficient machine learning seems to be, algorithmic efficiency improvements may keep the scaling going for a while? Maybe that's not a "major breakthrough" but it's improvement nonetheless.
It's also going to take a while to learn to use the new toys we already have.
I'd love to see more algorithmic efficiency in ML. But hasn't ML been going in the other direction for at least a decade now? It seems to me that it's been going aggressively towards brute force algorithms. Specifically, figuring out how to do as much matrix multiplication as possible.
> given how inefficient machine learning seems to be
We emulate neurons mathematically but it is possible to build efficient analog circuits that emulate them physically.
I doubt any biological system can ever learn all the information gpt4 has learned. The gpt4 learning may not have been power efficient but neither were the first airplanes compared to birds yet today biological flight is rather limited compared to flight that uses technology.
Ever listen to Geoffrey Hinton speak about back propagation vs what biological systems use? Do you think he is wrong about this?
He isn't wrong, but with current GPU hardware back propagation is actually the superior algorithm compared to his latest forward forward algorithm. Swapping the training algorithm doesn't get around the fact that you need to perform lots and lots of forward passes and those need an insane amount of memory bandwidth. Back propagation isn't a significant bottleneck in computers and also not in terms of memory capacity. So the only benefit of the biologically plausible forward forward algorithm is that you could run it on a digital or analog NPU, but with Ryzen AI coming in Strix Point, every high end laptop is going to have 50 TOPS of AI compute and 200GB/s of memory bandwidth. Nothing except large datacenter GPUs or a hypothetical 5090 with 32 GB would be competitive against that anytime soon. Anything smaller and you will have to buy several 3090 on eBay for like $900 a pop. Analog circuits are far off for now.
> I doubt any biological system can ever learn all the information gpt4 has learned
The fact that human brains can become competent at language with training datasets with a size of a tiny fraction of common crawl points to how much more efficient they are
Minimal as in the amount a person needs to learn the language or minimal as in tiny if compared to English in common crawl but still orders of magnitude more than a person goes through for infancy language acquisition?
It’s anecdotal, but here [1] is an example of AI learning to translate a new language faster than a human could learn to do it, from a similar amount of data, using Claude 3 Opus.
Translating a language isn’t quite the same as understanding it, but this is still impressively data-efficient.
"The author has later responded and apologized a flawed / biased methodology which led them to believe that Opus has no prior knowledge of Circassian (i.e. wasn't trained on it)
Apparently it was, and is able to speak Circassian just not perfectly"
The result is bad, that is just an image recognition model we know those can be trained with relatively little words and just a bunch of images, the model didn't learn grammar or sentences or relationships like a baby would. The only models we have that can do grammar and sentences and relationships are LLMs with massive amounts of data.
Predictions aren't worth much without a bet, but I think the tech will plateau in the next decade, for several years or more, just like it has in the past
One main reason is that I think people underestimate how much work OUR brains are doing when we interact with LLMs. It seems like the initial "wow" has worn off for many people, but definitely not everybody.
For coding, people will get stuck in loops, trying to get LLMs to modify LLM-generated code
And I think the market will cool down, which seems inevitable considering Nvidia's stock price (I'm a shareholder), and the fact that they seem to be the only ones really making money
If you compare Google after 8 years (2004) to OpenAI after 8 years (2023), the business is uh very different
I feel like the space has already plateaued. Lots of improvements up to GPT4 were genuine milestones, but that’s now a year ago and everything since was marginal.
I’m not invested in any sense in the space. I’m actually more frequently turning off Copilot in VSCode recently. I’d like to see further breakthroughs as much as anyone, but am not holding my breath. In fact, shorting NVIDIA seems like one of the better ideas currently.
But couldn’t you have said that 5 years ago and 10 years ago? I’d give it 3-4 years to actually call it.
But if you believe strongly, shorting AI-enhanced stocks is a great way to capitalize on your prediction. You could also use the short as a hedge for your expectations (either way you win something).
There's a real chance of rapid decline as the advance we've seen on GPT3 was largely due to openAI being able to efficiently train it on common crawl but now this data body is getting poisoned by automatically generated content.
I don't see that as a threat just yet. It seems simpler: stock value prices expected future growth. Nvidia has already grown to a highly dominant position. I don't see how it can grow much more to fill expectations of the staggering stock price. I'm expecting more of a regression to the mean soon, with Nvidia losing a bit of their lead.
The problem with LLMs for code editing is that they generate new tokens, instead of performing in place context editing (IPCE). It probably shouldn't be that difficult to just add a final layer that is trained to perform IPCE. Then you would get a lot of the benefits of MemGPT without an external tool.
The LLM could go back and only rewrite a handful of lines.
I think we've way over done the 'general intelligence' part of AI already, that is already 'super general intelligence'.
What's lacking is agency/autonomy. I have a bad feeling even 'general autonomy' will take a fraction of the power we're already using which means 'super autonomy'... is probably already possible.
Which means ASI soonish.. which leads to uncontrolled ASI either deliberately or accidently.. which means.. well it's out of our hands at that point. Anything can happen.
It’s fine if I doesn’t ; current LLMs are already very helpful; we need them faster, smaller and eating less resources. If not AGI, let’s run 50 personal assistants on my phone.
I think this is where people will be disappointed when “AI” is brought to mass consumer.
The level of results does not scale well down to mass consumer hardware.
And yes I know people can buy an NVIDIA GPU and run these models, but the phone like you said is the most common computer and where this will be hardest to scale too.
It’s why I’m bearish on AI, and I think the pop will be due to being unable to scale down sufficiently
> The level of results does not scale well down to mass consumer hardware.
For now. I'm pretty bearish on all things "AI" but of all things one can say about the future, today's hardware is yesterday's news. And in this case, I'd say the same goes for algorithms.
I think it’s not really relevant though to the current timeframe.
The expectation of the current bubble is that something will be delivered soon (in the next year or two)
I don’t see mass consumer hardware scaling up quickly enough in that time to have a product that will match the hype that everyone is showing with cloud based tools.
> The level of results does not scale well down to mass consumer hardware.
I think that it does, though. A few years ago, running any remotely complex (and decent) generative AI on anything that wasn't a large compute server was out of the question. Nowadays, you can run a very respectable image or text generation algorithm on a middle-of-the-road gaming PC. Local models may not always be as good as the enormous things that companies run, but they're putting up a fight.
This isn't founded on research, but given how much people were able to scale all of it down, I feel like there's going to be more of this "fat" to trim - data that takes up a lot of space but isn't very important to the final result. Add onto that the constant improvement of hardware, and the lines are going to intersect eventually.
Strix Point isn't even out yet. Sure that is a laptop form factor and you won't be able to run those 130b models because of RAM constraints, but things like mixtral 8x7b will easily run at 20 tokens per second locally on your laptop within this year.
And what will 20t/s give the user as a user experience?
I don’t mean to sound combative, but my point is that the disconnect between what customers will get and what they see is very high. Will your metric meaningfully change that aspect?
I think "LLMs are using well-studied modeling techniques with overwhelming resource investment" is the most fundamental critique and why I've been skeptical of the future of this wave. That's not to say we won't (and haven't already) gotten useful tools! There's obviously a lot to do with human language interfaces and complex analysis. I'm just skeptical a whole new level is just around the corner.
I honestly don't see what the problem is. One can say the internet is just to "connect machines with wires and have a set of protocols allowing them to communicate". It's true, but the magic happens when simple ideas get scaled.
When you have to throw billions of dollars worth of compute at a problem to brute force it, you're not exactly 'scaling it' as much as scaling your costs for diminishing returns.
This isn't what is happening though. Philosophers keep poking holes in AGI arguments, previously Strong AI, and techbros keep using a new term, each more ambiguous than the last. The hope, it seems, is to use this ambiguity to prevent pointed criticism that would prevent investment and adoption.
One of the things I wonder about is whether “intelligence” can be linearly scaled or if it’s just a way of solving an optimization problem. In other words, humans have come pretty close to the peak of Mt. Smarts and therefore being 1000x as intelligent is more like the difference between 1 meter from the peak and a millimeter from the top. You’re both basically there.
In other words, maybe humans have basically solved the optimization problem for the environment we live in. At this point the only thing to compete on is speed and cost.
You don't see that many, or any really, von Neumanns walking around so there's probably still significant room to improve with all the benefits of having intelligence neatly packaged in a computer.
Even if all the computers can do is ask the right questions and it takes a big research project to figure it out, that would be an improvement in productivity.
I actually think it will come from the other direction. That people will get better at asking questions, because there is an automated tool that will build systems to answer larger problems than a single person could quickly answer.
Yeah, imagine spinning up 100 von Neumanns to attack a problem. They can all instantly share their thoughts & new skills, coordinate, choose new exploration directions, and spend decades developing new tools -- all within moments after pressing 'Enter'.
Even if our AI systems have only a minute fraction of von Neumann's intellect, we still have no idea what tomorrow will be like. I'm terrified and excited.
I don't think there is such a thing as general intelligence, there are only capabilities. What we call "general" intelligence is really just the set of capabilities that a human has, because we're self-centered.
If we had more intelligences around to compare with I think we'd find that some are "more intelligent" in that they have all of our capabilities, plus some. And that others are "less intelligent" in that we have all of the capabilities that they have, plus some. And then there would be the "differently intelligent" which have at least one capability that we don't and which lack at least one capability that we have.
Under this lens, I don't know if there's much utility in fine grained comparisons of intelligence re meters and millimeters. The space is discrete: subsets, not metrics.
I don't know if you could ever prove something like this (or maybe we just lack that capability). It seems more like an axiom-selecting notion than something to be argued. Anyhow, it's what my gut says.
I think it’s an interesting thought. But for the sake of it, can you name/imagine some examples of problems/forms of problems we as human can not solve? Or can not solve efficiently?
If you can find none, is it not the proof that our intelligence is general?
The first thing to come to mind is the traveling salesman problem and the host of other unsolved math problems which we suspect may be unsolvable-by-us.
There's also problems of self reference. A Turing machine may be able to solve the halting problem for pushdown automata, but it can't solve the halting problem for Turing machines. Whether or not we're as capable as Turing machines, there's a halting problem for us and we can't solve it.
I'm restricted to mathy spaces here because how else would you construct a well defined question that you cannot answer? But I see no reason why there wouldn't be other perspectives that we're incapable of accessing, it's just that in these cases the ability to construct the question is just as out of reach as the ability to answer it.
You may have heard talk about known unknowns and unknown unknowns, but there are also known unknowables and unknown unknowables, and maybe even unknowable unknowables (I go into this in greater detail here: https://github.com/MatrixManAtYrService/Righting/blob/master...).
In any case, I don't think it's ever valid to take one's inability to find examples as proof of something unless you can also prove that the search was exhaustive.
Instead of AGI, we should call it AHI: artificial human intelligence, or SHI: super human intelligence. That would be much clearer and would sidestep the generality issue.
We already have technology which beats what we would be capable of ever doing and almost instantaneously.
If you want a concrete example, astronomical image processing would be one: impossible for humans without AI.
In that same logic, if we invent AGI and then it solves a problem for us then it counts for humanity? (and of course it does but here we're talking about something that humans wouldn't solve without AGI)
Yes we did, but there’s a difference between delegating a task (asking a computer to do it) and executing the task (running the calculation). Otherwise you might as well say humans can run 40mph because we can ride horses.
Also, no one person invented the calculator. The calculator is the culmination of hundreds or even thousands of years of invention. It’s not like the knowledge or creativity is in each of our brains and we could each build a calculator given the requisite materials. It took thousands of lifetimes of ingenuity. So there’s another answer to your question of things we aren’t efficient at solving: building a calculator.
In life I tend to encounter two common patterns of intelligent people: those who had a good education and those who did not. I worry that when AGI comes it is going to be able to do all the things the smooth fast taking wily folks can do, and none of the things the educated folks can do, and we’ll accelerate not a slide into the singularity but a slide into inane banality.
How do you provoke a model into being wacky, challenging, and innovative?
I can’t put my finger on it, but I think the intellectually challenging part is at the heart of the matter. A wily person can talk their way through a debate by saying things that sound compelling. An educated person has more ability to reason: they can challenge you when they know they are right, and explain why you are wrong.
It’s the difference between bluffing and sincerity, or dishonesty and truthfulness. Current LLMs are confident liars.
> we’ll accelerate not a slide into the singularity but a slide into inane banality
Half joking response: Have you looked at all the SEO garbage than any Google search produces these days? We are already in the great age of "inane banality".
I told it to be unfriendly and use black humour. It certainly is wacky, especially with that TTS voice. Running this locally made me realize that OpenAI's ChatGPT is way too uptight and the personality is way too bland. The LLM can already whip out "smooth talk" faster than me.
At the very least, it knows solved problems and repeat the solution. That's better than pretty much all humans, since humans can't know all known solutions.
What if 'AGI' was another over-promised scam to sell stochastic parrots marketed as "intelligence" for a product that not even its creators can even understand when it goes wrong badly?
"Oh don't worry, AGI is coming soon and we'll solve that later" - AI founders
Yet they don't even know how long that is since no-one knows or it never happens. Mistakes in AI are costly and are very expensive.
What if their startup fails before the time arrives because they still cannot make any money and need to constantly raise VC money every week or quarter?
Again, there will only be 90% - 95% of these 'AI' companies that will fail with the 5% to 10% still around including the incumbents.
This argument is, and always has been, utter bullshit.
All humans have the capacity to genuinely learn, create, and think, regardless of how their output appears to you in some subset of interactions with them.
"Some humans sometimes have trouble with critical thinking, or just regurgitate previously-memorized facts" is not in any way equivalent to "LLMs, by their fundamental nature, only have the capacity to produce various recombinations of their training data."
"no major AI technology breakthroughs in decades.everything we are seeing is larger compute scaling." This is false. Everything from the transformer to advancements in state space models have been foundational breakthroughs
I beg to differ. Transformers are purely an optimization. It’s not exactly right to call everything “compute scaling” but we are still, at the end of the day, fitting polynomials.
And frankly, that’s probably not what our brains are doing.
> And frankly, that’s probably not what our brains are doing.
I think that it is! It's much more likely to me that our brains are doing something big and simple than small and complicated. That's the way that nature tends to work. Fitting low-order million-dimensional polynomials would meet that description.
From the double slit experiment, to particle-wave duality, to the particle zoo of the 70s, to quantum chromodynamics, to asymptotic freedom, to more exotic theories like string theory, etc. tells us the complete opposite. Every major discovery in physics in the past 150 years seems to disagree. Things are extremely weird and complicated when we get extremely tiny. Why would our brains be different?
If we lived at the quantum scale, then classical physics would be the weird one. Quantum chromodynamics is only confusing for two reasons: it differs from our everyday experience so we don't have an intuition for it, and because it has a large number of mutually-interacting (but basic) components.
Richard Feynman put it very well:
"The world is strange, the whole universe is very strange, but see when you look at the details then you find out that the rules are very simple, of the game, the mechanical rules by which you can figure out exactly what's going to happen when the situation is simple. It's again this chess game; if you're in just the corner with only a few pieces involved, you can work out exactly what's going to happen. And you can always do that when there's only a few pieces. And so you know you understand it. And yet, in the real game there's so many pieces you can't figure out what's going to happen.
"There's such a lot in the world, there's so much distance between the fundamental rules and the final phenomena that it's almost unbelievable that the final variety of phenomena can come from such a steady operation of such simple rules... But it is not complicated, it's just a lot of it."
Our brain is however the very definition of small and complicated. It's the most complex known organic object known to humans and for all that any given normal brain handles in a given day, it uses just 0.3 kilowatt hours (kWh) to do it. No computer we have comes close to handling what a brain handles with that power consumption.
ChatGPT by contrast, consumes roughly a gigawatt hour per day serving its users. Yes, to do this it's handling a colossal amount of queries, but that's all it's doing. Your brain handles everything in your body and consciousness, in ways we don't even fully understand, while also letting you think and communicate and reason as a conscious being with self direction.
Moreover, there is evidence that at least part of our brain's functions may be exactly as the other reply here mentions, weird, subatomic and deeply complex in ways that are difficult to get a clear grip on.
It’s difficult to comprehend impact of something that’s widely adopted. Like, batch normalization alone was probably mind blowing when it came out. Yet it seems so simple and self-explanatory now.
It is a bit weird though… I mean, you could just as well say that the last breakthrough in all of computing was the transistor (Shockley 1946) – all we’ve been doing ever since is just “scaling” and combining them in new ways.
AI has been a 'moving aim' as opposed to moving target. It's a label slapped on whatever slice of software engineering seeming most magic-like at any given decade.
Early computers in late 40s were called 'electronic brains' by the media...
People who say things like "major breakthroughs" often imagine a cliff/steep rise. The reality is that most "breakthroughs" are small, incremental, almost invisible steps in all aspects of the field, and potentially even in fields that are only tangentially related.
Then, one day, they hear about it in the news, because there's now some hype, or some event that makes it newsworthy. This makes it feel like the breakthrough was instantaneous or steep, but in fact has been in the making for decades or even more.
I have no doubt AGI is coming, but it will be gradual and slow. It will be the accumulation of more advances in everything, including hardware, as well as software. It might even include economic changes.
From nothing to ChatGPT opened to the public and billions students' lifes rely on that in the next month, isn't this the biggest step in the tech history?
It's a major societal failure that education has been reduced to turning in coherent enough strings of characters, that's what I've learned in the past year.
You mean the use of a writing system to share knowledge amongst ourselves?
I find that absolutely wonderful and it worked decently well for me (and possibly you.) Now we have a never seen before technology and society will adapt, that's it. No failure.
> You mean the use of a writing system to share knowledge amongst ourselves?
No. And what's with the "it is so bad yet you used it"? I am very much allowed and required to denounce a system even if I cannot escape it or if I could have somehow profited from it or chosen to use it.
I very much reached the point where I am despite the educational systems I was exposed to. And a system geared towards memorization and regurgitation of data in textual format where pupils can successfully use a chatbot to avoid doing work is certainly failing its goals of educating the youth.
I would point you to the complaints of American teachers about the reading and mathematics levels of students, if only because that is widely accessible. I did not grow up in the US and the school system in my home country is leagues behind the US.
excluding... antibiotics, electricity, refrigeration, the combustion engine, the digital computer...
like even restricted to the domain of strictly computation, I'd say it barely scratches the surface... like even if we ignore computer engineering ("the transistor," "silicon microprocessors", etc.)... foundational tech like "compilers" are more significant.
even restricted to modern applications, GPS is more useful and life-changing.
so, no. It's not the biggest step in tech history.
There were hundreds of competitive, even SOTA LLMs before ChatGPT existed. You're basically just proving the parent comment right in how small of a leap ChatGPT is from t5-flan or BERT.
Curious about how many kids in India and China are relying on it.
I'll accept the prospect of hundreds of millions, generously.
And "relying on it" is a strong phrase. Using it as a curiosity, sure. The ones relying on it seem to keep ending up in the news because of how, well, unreliable it is.
We cannot create life on the simplest scale, there have been experiments with the creation of life in the Miller experiment have only produced so called building blocks, amino acids.
However we are unable to create life in dead creatures that have all the building blocks in place.
What is happening is the belief that the laws of thermodynamics are probabilistic, like a law that can be broken.
Laws like gravity and thermodynamics are deterministic and the hubris of those who make claims of real intelligence in machines we create are going to be as disappointed as those who design perpetual motion machines.
I don't really understand, if physicist is right and deterministic we already have thinking machines. They're called brains. As far as anyone can tell, thermodynamics works fine for them.
It is a law that cannot be broken like perpetual motion or the creation of life.
You may also argue that the universe is a perpetual motion machine and you would be wrong.
I've just created a new benchmark to see how top LLMs do on NYT Connections (https://www.nytimes.com/games/connections). 267 puzzles, 3 prompts for each, uppercase and lowercase.
GPT-4 Turbo: 31.0
Claude 3 Opus: 27.3
Mistral Large: 17.7
Mistral Medium: 15.3
Gemini Pro: 14.2
Qwen 1.5 72B Chat: 10.7
Claude 3 Sonnet: 7.6
GPT-3.5 Turbo: 4.2
Mixtral 8x7B Instruct: 4.2
Llama 2 70B Chat: 3.5
Qwen 1.5 14B: 3.1
Nous Hermes 2 Yi 34B: 1.5
Notes: 0-shot. Maximum possible is 100. Partial credit is given if the puzzle is not fully solved. There is only one attempt allowed per puzzle. In contrast, humans players get 4 attempts and a hint when they are one step away from solving a group. Gemini Advanced is not yet available through the API.
What I found interesting is how this benchmark reveals a large capabilities gap between the top, large models and the rest, in contrast to existing over-optimized benchmarks.
Also these puzzles can be _really_ hard. As a French person who's lived 10+ years in English-speaking countries, I am often completely baffled. I am not sure humans would do a lot better with 0-shot.
It's probably somewhat g loaded. I don't know how much, but someone could look at the curves (if they have access?) for the similar sub-section of an IQ test.
This article does not debate the question in its title, makes ridiculous claims like “there hasn’t been any major breakthrough in AI in decades”, and does not offer any real argument.
LLMs solve for the next word. Human intelligence solves for survival with many types of input, visual, audio etc. You can't create an AGI if you don't solve for the problems that created human GI.
Absolutely. The physical world is the input that creates the feedback loop for learning.
I would propose a definition of AGI. "A model capable of effecting the physical world through speech or physical action in a manner indistinguishable from a human."
Why not? For a hypothetical example - if we assume that simulating a human is AGI, and we have some hypothetical space-age magic tech bruteforce the problem by simulating every neuron and connection in the brain... why would being "embodied" factor into this?
Because I think intelligence formed in human beings is connected directly to embodiment and not some kind of abstraction that can be simulated. My guess is that the best AI developments will ultimately come from mimicking the processes of how humans learn from their environment, and not from merely simulating (or trying to simulate, as I don’t really buy the positivist approach) human brains.
Digital data is all 1s and 0s, whether it encodes words, sounds, or pictures. Why do you think transformers only work for predicting words, when they're already successfully being used for other applications as well?
I think much like with a basic Turing machine definition compute is possible on a variety of substrates that some kind of intelligence can be created with a whole class of implementations, transformers included. Indeed the video and image input of LLMs is one of the most exiting use cases.
They’re trained to optimize guessing the next word. What they solve for to get this good at predicting the next word is an open question with answers hidden in plain sight in the weight blob.
In the grand scale, human intelligence evolved over millions of years. We went from personal computers to LLMs in mere decades. I get that everyone wants Singularity now, so do I. But there’s too much over-promise and delusion.
This is nonsense statement in and of itself. Its like wondering why an orange fails to turn into a chicken.
There are SO many missing pieces an LLM just doesnt have. LLMs could certainly be a small part of some sort of AGI _system_, but they themselves can never be AGI
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[ 39.6 ms ] story [ 783 ms ] threadSo a bit of a cop-out not wanting to say it outright :)
Why tell the children Santa's not real, when he's the reason they're being so good?
If we can achieve AGI simply through more and more computation, no matter how novel it is, its ultimately ifs, loops and arithmetic... then surely the human experience is ultimately just a 'wet LLM' (or whatever we end up calling the machine learning technology behind AGI).
Is a soul made out of matrix multiplications and dot products worse than a soul made of neurons?
So that's why it is similarly exclusionary: I think we should try, but also look to see if we can learn more to maybe rule it out altogether.
Who's stopping you?
>We already have machines that are generally intelligent and require as much energy as a few coalgas lights. Why wouldn’t it be eventually possible to replicate them in brass and steam?
My understanding of the argument of this article is that the conceptual design that replicates intelligence is what the industry has failed to generate today. Simply stating that it might be possible to create is failing to engage; the massive increase in compute power from Babbage to McCarthy didn't give us AGI, because they didn't figure out the right design to reason about anything in even a hundred times a human's energy consumption. If from McCarthy to today we still haven't actually found the proper recipe it just might be worth considering the point the article's making in spite of our other advancements since then.
Probably shouldn’t have mentioned the silicon though.
I mean if you simulate all the chemical processes in some being then yeah probably you get close (assuming you get everything right), let’s assume there is no unknown in how the atoms in our bodies interact.
Sounds like an expensive project.
If you are _not_ talking about a perfect emulation/simulation of such a machine, I will pose your question back to you: why _would_ it be possible to do something we don’t know what it is? Seems rather contrived to say “we can do this thing we don’t know what it is”.
And with the current models, the only thing I see is : "let's add more neurons, give it more data to train, and let's hope a mind will come out of these neurons". It helps understand the human brain, and help humanity and all, but I'm sure we are missing some 'theorical ingredients' to get a recipe for a full working AGI.
It's also going to take a while to learn to use the new toys we already have.
We emulate neurons mathematically but it is possible to build efficient analog circuits that emulate them physically.
I doubt any biological system can ever learn all the information gpt4 has learned. The gpt4 learning may not have been power efficient but neither were the first airplanes compared to birds yet today biological flight is rather limited compared to flight that uses technology.
Ever listen to Geoffrey Hinton speak about back propagation vs what biological systems use? Do you think he is wrong about this?
The fact that human brains can become competent at language with training datasets with a size of a tiny fraction of common crawl points to how much more efficient they are
I think you know the difference.
Translating a language isn’t quite the same as understanding it, but this is still impressively data-efficient.
[1] https://twitter.com/hahahahohohe/status/1765088860592394250
"The author has later responded and apologized a flawed / biased methodology which led them to believe that Opus has no prior knowledge of Circassian (i.e. wasn't trained on it)
Apparently it was, and is able to speak Circassian just not perfectly"
https://www.nyu.edu/about/news-publications/news/2024/februa...
"Planes and Cars today fundamentally use the same technology we had for almost decades, henceforth ...."
The real question to ask is "does AGI matter"
One main reason is that I think people underestimate how much work OUR brains are doing when we interact with LLMs. It seems like the initial "wow" has worn off for many people, but definitely not everybody.
For coding, people will get stuck in loops, trying to get LLMs to modify LLM-generated code
And I think the market will cool down, which seems inevitable considering Nvidia's stock price (I'm a shareholder), and the fact that they seem to be the only ones really making money
If you compare Google after 8 years (2004) to OpenAI after 8 years (2023), the business is uh very different
I’m not invested in any sense in the space. I’m actually more frequently turning off Copilot in VSCode recently. I’d like to see further breakthroughs as much as anyone, but am not holding my breath. In fact, shorting NVIDIA seems like one of the better ideas currently.
But if you believe strongly, shorting AI-enhanced stocks is a great way to capitalize on your prediction. You could also use the short as a hedge for your expectations (either way you win something).
I have no data or sources to back this up.
What's lacking is agency/autonomy. I have a bad feeling even 'general autonomy' will take a fraction of the power we're already using which means 'super autonomy'... is probably already possible.
Which means ASI soonish.. which leads to uncontrolled ASI either deliberately or accidently.. which means.. well it's out of our hands at that point. Anything can happen.
The level of results does not scale well down to mass consumer hardware.
And yes I know people can buy an NVIDIA GPU and run these models, but the phone like you said is the most common computer and where this will be hardest to scale too.
It’s why I’m bearish on AI, and I think the pop will be due to being unable to scale down sufficiently
For now. I'm pretty bearish on all things "AI" but of all things one can say about the future, today's hardware is yesterday's news. And in this case, I'd say the same goes for algorithms.
I just don’t think it will live up to the hype people have.
People are seeing Sora and StableDiffusion when they think of AI.
And yes I can run SD on my iPhone, but it’s a poor experience that’s difficult to productize.
The first real products will be so underwhelming compared to what people expect.
Eventually the hardware and algorithms will improve and meet in the middle, but I think it’s so far out, that people will have moved on
The expectation of the current bubble is that something will be delivered soon (in the next year or two)
I don’t see mass consumer hardware scaling up quickly enough in that time to have a product that will match the hype that everyone is showing with cloud based tools.
Where have you seen this? And what is the claim that will be delivered?
I'm absolutely certain something will be delivered in the next year or two. It's probably not AGI.
I think that it does, though. A few years ago, running any remotely complex (and decent) generative AI on anything that wasn't a large compute server was out of the question. Nowadays, you can run a very respectable image or text generation algorithm on a middle-of-the-road gaming PC. Local models may not always be as good as the enormous things that companies run, but they're putting up a fight.
This isn't founded on research, but given how much people were able to scale all of it down, I feel like there's going to be more of this "fat" to trim - data that takes up a lot of space but isn't very important to the final result. Add onto that the constant improvement of hardware, and the lines are going to intersect eventually.
I don’t mean to sound combative, but my point is that the disconnect between what customers will get and what they see is very high. Will your metric meaningfully change that aspect?
I'd love to hear it ;-)
In other words, maybe humans have basically solved the optimization problem for the environment we live in. At this point the only thing to compete on is speed and cost.
I actually think it will come from the other direction. That people will get better at asking questions, because there is an automated tool that will build systems to answer larger problems than a single person could quickly answer.
Even if our AI systems have only a minute fraction of von Neumann's intellect, we still have no idea what tomorrow will be like. I'm terrified and excited.
This has changed drastically and thus our definition of smart has too.
If we had more intelligences around to compare with I think we'd find that some are "more intelligent" in that they have all of our capabilities, plus some. And that others are "less intelligent" in that we have all of the capabilities that they have, plus some. And then there would be the "differently intelligent" which have at least one capability that we don't and which lack at least one capability that we have.
Under this lens, I don't know if there's much utility in fine grained comparisons of intelligence re meters and millimeters. The space is discrete: subsets, not metrics.
I don't know if you could ever prove something like this (or maybe we just lack that capability). It seems more like an axiom-selecting notion than something to be argued. Anyhow, it's what my gut says.
If you can find none, is it not the proof that our intelligence is general?
There's also problems of self reference. A Turing machine may be able to solve the halting problem for pushdown automata, but it can't solve the halting problem for Turing machines. Whether or not we're as capable as Turing machines, there's a halting problem for us and we can't solve it.
I'm restricted to mathy spaces here because how else would you construct a well defined question that you cannot answer? But I see no reason why there wouldn't be other perspectives that we're incapable of accessing, it's just that in these cases the ability to construct the question is just as out of reach as the ability to answer it.
You may have heard talk about known unknowns and unknown unknowns, but there are also known unknowables and unknown unknowables, and maybe even unknowable unknowables (I go into this in greater detail here: https://github.com/MatrixManAtYrService/Righting/blob/master...).
In any case, I don't think it's ever valid to take one's inability to find examples as proof of something unless you can also prove that the search was exhaustive.
Instead of AGI, we should call it AHI: artificial human intelligence, or SHI: super human intelligence. That would be much clearer and would sidestep the generality issue.
Also, no one person invented the calculator. The calculator is the culmination of hundreds or even thousands of years of invention. It’s not like the knowledge or creativity is in each of our brains and we could each build a calculator given the requisite materials. It took thousands of lifetimes of ingenuity. So there’s another answer to your question of things we aren’t efficient at solving: building a calculator.
How do you provoke a model into being wacky, challenging, and innovative?
Are those the typical qualities of the educated…?
It’s the difference between bluffing and sincerity, or dishonesty and truthfulness. Current LLMs are confident liars.
"Oh don't worry, AGI is coming soon and we'll solve that later" - AI founders
Yet they don't even know how long that is since no-one knows or it never happens. Mistakes in AI are costly and are very expensive.
What if their startup fails before the time arrives because they still cannot make any money and need to constantly raise VC money every week or quarter?
Again, there will only be 90% - 95% of these 'AI' companies that will fail with the 5% to 10% still around including the incumbents.
All humans have the capacity to genuinely learn, create, and think, regardless of how their output appears to you in some subset of interactions with them.
"Some humans sometimes have trouble with critical thinking, or just regurgitate previously-memorized facts" is not in any way equivalent to "LLMs, by their fundamental nature, only have the capacity to produce various recombinations of their training data."
And frankly, that’s probably not what our brains are doing.
I think that it is! It's much more likely to me that our brains are doing something big and simple than small and complicated. That's the way that nature tends to work. Fitting low-order million-dimensional polynomials would meet that description.
From the double slit experiment, to particle-wave duality, to the particle zoo of the 70s, to quantum chromodynamics, to asymptotic freedom, to more exotic theories like string theory, etc. tells us the complete opposite. Every major discovery in physics in the past 150 years seems to disagree. Things are extremely weird and complicated when we get extremely tiny. Why would our brains be different?
Richard Feynman put it very well:
"The world is strange, the whole universe is very strange, but see when you look at the details then you find out that the rules are very simple, of the game, the mechanical rules by which you can figure out exactly what's going to happen when the situation is simple. It's again this chess game; if you're in just the corner with only a few pieces involved, you can work out exactly what's going to happen. And you can always do that when there's only a few pieces. And so you know you understand it. And yet, in the real game there's so many pieces you can't figure out what's going to happen.
"There's such a lot in the world, there's so much distance between the fundamental rules and the final phenomena that it's almost unbelievable that the final variety of phenomena can come from such a steady operation of such simple rules... But it is not complicated, it's just a lot of it."
ChatGPT by contrast, consumes roughly a gigawatt hour per day serving its users. Yes, to do this it's handling a colossal amount of queries, but that's all it's doing. Your brain handles everything in your body and consciousness, in ways we don't even fully understand, while also letting you think and communicate and reason as a conscious being with self direction.
Moreover, there is evidence that at least part of our brain's functions may be exactly as the other reply here mentions, weird, subatomic and deeply complex in ways that are difficult to get a clear grip on.
Early computers in late 40s were called 'electronic brains' by the media...
Then, one day, they hear about it in the news, because there's now some hype, or some event that makes it newsworthy. This makes it feel like the breakthrough was instantaneous or steep, but in fact has been in the making for decades or even more.
I have no doubt AGI is coming, but it will be gradual and slow. It will be the accumulation of more advances in everything, including hardware, as well as software. It might even include economic changes.
I find that absolutely wonderful and it worked decently well for me (and possibly you.) Now we have a never seen before technology and society will adapt, that's it. No failure.
No. And what's with the "it is so bad yet you used it"? I am very much allowed and required to denounce a system even if I cannot escape it or if I could have somehow profited from it or chosen to use it.
I very much reached the point where I am despite the educational systems I was exposed to. And a system geared towards memorization and regurgitation of data in textual format where pupils can successfully use a chatbot to avoid doing work is certainly failing its goals of educating the youth.
I would point you to the complaints of American teachers about the reading and mathematics levels of students, if only because that is widely accessible. I did not grow up in the US and the school system in my home country is leagues behind the US.
like even restricted to the domain of strictly computation, I'd say it barely scratches the surface... like even if we ignore computer engineering ("the transistor," "silicon microprocessors", etc.)... foundational tech like "compilers" are more significant.
even restricted to modern applications, GPS is more useful and life-changing.
so, no. It's not the biggest step in tech history.
There were hundreds of competitive, even SOTA LLMs before ChatGPT existed. You're basically just proving the parent comment right in how small of a leap ChatGPT is from t5-flan or BERT.
Curious about how many kids in India and China are relying on it.
I'll accept the prospect of hundreds of millions, generously.
And "relying on it" is a strong phrase. Using it as a curiosity, sure. The ones relying on it seem to keep ending up in the news because of how, well, unreliable it is.
What is happening is the belief that the laws of thermodynamics are probabilistic, like a law that can be broken. Laws like gravity and thermodynamics are deterministic and the hubris of those who make claims of real intelligence in machines we create are going to be as disappointed as those who design perpetual motion machines.
GPT-4 Turbo: 31.0
Claude 3 Opus: 27.3
Mistral Large: 17.7
Mistral Medium: 15.3
Gemini Pro: 14.2
Qwen 1.5 72B Chat: 10.7
Claude 3 Sonnet: 7.6
GPT-3.5 Turbo: 4.2
Mixtral 8x7B Instruct: 4.2
Llama 2 70B Chat: 3.5
Qwen 1.5 14B: 3.1
Nous Hermes 2 Yi 34B: 1.5
Notes: 0-shot. Maximum possible is 100. Partial credit is given if the puzzle is not fully solved. There is only one attempt allowed per puzzle. In contrast, humans players get 4 attempts and a hint when they are one step away from solving a group. Gemini Advanced is not yet available through the API.
What I found interesting is how this benchmark reveals a large capabilities gap between the top, large models and the rest, in contrast to existing over-optimized benchmarks.
Just to make sense of your result, can you show your prompt?
When you say 3 prompts and one attempt, what does that mean?
Also regarding 0-shot, did you give the LLM the instruction that is given to human by the game?
If yes, I would count that as one shot as an example of how to properly solve one example puzzle is given.
``` How to Play
Find groups of four items that share something in common.
Category ExamplesFISH: Bass, Flounder, Salmon, Trout FIRE ___: Ant, Drill, Island, Opal
Categories will always be more specific than "5-LETTER-WORDS," "NAMES" or "VERBS."
Each puzzle has exactly one solution. Watch out for words that seem to belong to multiple categories!
Each group is assigned a color, which will be revealed as you solve ```
Thanks
I would propose a definition of AGI. "A model capable of effecting the physical world through speech or physical action in a manner indistinguishable from a human."
This is nonsense statement in and of itself. Its like wondering why an orange fails to turn into a chicken.
There are SO many missing pieces an LLM just doesnt have. LLMs could certainly be a small part of some sort of AGI _system_, but they themselves can never be AGI