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What if AGI is not coming?
- cykros 3y agoIt strikes me as amazing that we went from the general recognition that AGI wasn't anywhere soon to suddenly having this widespread idea that it was right around the corner. Sort of reminds me of the late 90s super-proto-VR stuff where people thought any day now we'd be jacking into full immersion (tactile, smell and all) virtual reality. Don't get me wrong, LLM's are useful tools. But ChatGPT aint Neuromancer. Or even Wintermute. It's Clippy after a few years of community college.
- necovek 3y agoThis does not really consider the "what-if" in the title, but mostly puts out the arguments for why is it not coming. So a bit of a cop-out not wanting to say it outright :)
- cykros 3y agoThey know it's not coming, but don't mind seeing their 401(k) go up for awhile. Why tell the children Santa's not real, when he's the reason they're being so good?
- imadierich 3y ago[dead]
- adastra22 3y agoAGI arrived in 2017.
- colordrops 3y agowhat happened in 2017?
- imadierich 3y ago[dead]
- motoxpro 3y agoI think they are talking about the transformer paper?
- adastra22 3y agoTransformers. 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.
- jimmcslim 3y agoWhat 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).
- deleted 3y ago[deleted]
- laerus 3y agoUnrelated but do you believe animals have a soul?
- zhugeyangyang 3y agoI think it's just a bubble, containing some internal context
- akasakahakada 3y agoUhh 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?
- namaria 3y agoThis person literally just asked a question. Put down your stones
- err4nt 3y agoLike 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.
- exitb 3y agoWe 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?
- aumerle 3y ago"eventually" is doing a lot of heavy lifting here.
- vbezhenar 3y agoThere 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.
- 2muchcoffeeman 3y agoThis sort of thinking might be part of the problem. Why are we analogous to software running on hardware?
- deleted 3y ago[deleted]
- Phiwise_ 3y agoOver 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.
- 3y ago
- skybrian 3y agoThe 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.
- rented_mule 3y agoI'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.
- skybrian 3y agoAmong other things, there seems to be progress in fixing attention to scale better.
- czl 3y ago> 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?
- imtringued 3y agoHe 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.
- hatenberg 3y agoWHat a strange piece of writing. "Planes and Cars today fundamentally use the same technology we had for almost decades, henceforth ...." The real question to ask is "does AGI matter"
- maxbaines 3y agoYes I found it an odd piece also.
- deleted 3y ago[deleted]
- aorloff 3y agoIf with each step we are halfway closer to the goal of AGI, how long before we get there ?
- mirekrusin 3y ago…assuming improvements are halfway or less - which is not what we’re seeing.
- beambot 3y agoAh, Zeno's paradox. Clearly the turtle will win.
- chubot 3y agoPredictions 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
- diarrhea 3y agoI 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.
- seanmcdirmid 3y agoBut 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).
- namaria 3y agoThere'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.
- diarrhea 3y agoI 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. I have no data or sources to back this up.
- bottlepalm 3y agoI 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.
- topbanana 3y agoOne thing's for sure, there are now a lot more people looking to make it happen
- anonzzzies 3y agoIt’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.
- dagmx 3y agoI 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
- klyrs 3y ago> 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.
- dagmx 3y agoI guess I should clarify: I think it’ll get better on mass consumer hardware. 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
- czl 3y agoWith time mass consumer hardware scales up. You expect that trend to stop?
- dagmx 3y ago
- aeturnum 3y agoI 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.
- raincole 3y agoI 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.
- namaria 3y agoWhen 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.
- akasakahakada 3y agoAs long as keep philosophers keep shifting the definition of AGI, that should never come to us.
- TuringTest 3y agoWow, there is a definition? I'd love to hear it ;-)
- akasakahakada 3y agoironic name
- tolleydbg 3y agoThis 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.
- janalsncm 3y agoOne 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.
- blfr 3y agoYou 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.
- kurthr 3y agoEven 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.
- a_wild_dandan 3y agoYeah, 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.
- deleted 3y ago[deleted]
- throwaway48r7r 3y ago>the environment we live in This has changed drastically and thus our definition of smart has too.
- gorgoiler 3y agoIn 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?
- throwaway48r7r 3y agoThis is exactly what's happening.
- bjornsing 3y ago> How do you provoke a model into being wacky, challenging, and innovative? Are those the typical qualities of the educated…?
- gorgoiler 3y agoI 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.
- gorgoiler 3y ago*…are often confidently incorrect
- throwaway2037 3y ago> 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".
- rvz 3y agoWhat 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.
- throwaway48r7r 3y agoThis undersells the fact that to a not insignificant degree humans are stochastic parrots too.
- danaris 3y agoThis 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."
- Arthanos 3y ago"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
- dvt 3y agoI 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.
- mitthrowaway2 3y ago> 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.
- deleted 3y ago[deleted]
- dvt 3y ago> That's the way that nature tends to work 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?
- mitthrowaway2 3y agoIf 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."
- peter_retief 3y agoWe 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.
- Vecr 3y agoI 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.
- peter_retief 3y agoLife cannot be created even on the simplest form let alone a brain.
- Vecr 3y agoFrom scratch, sure. Where's the thermodynamics coming from though?
- peter_retief 3y agoIt 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.
- Vecr 3y agoI still don't understand, why does thermodynamics (or statistical mechanics or whatever) say you can't make an AGI?
- f6v 3y agoIn 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.
- zone411 3y agoI've just created a new benchmark to see how top LLMs do on NYT Connections (https://www.nytimes.com/games/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.
- d--b 3y agoAlso 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.
- Vecr 3y agoIt'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.
- mike986 3y agoDo you know the average or top human score / SD? Not sure if that data is available on the link or elsewhere. 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. Select four items and tap 'Submit' to check if your guess is correct. Find the groups without making 4 mistakes! Category Examples FISH: 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
- d--b 3y agoThis 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.
- aaron695 3y ago[dead]
- throwaway48r7r 3y agoLLMs 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.
- keiferski 3y agoYes I don’t think AGI (which is entirely an ill-defined concept, but put that aside) will happen until AI is embodied in the physical world.
- throwaway48r7r 3y agoAbsolutely. 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."
- tavavex 3y agoWhy 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?
- throwaway48r7r 3y agoBecause it would be an intelligence but not one we would recognize as human like GI.
- keiferski 3y agoBecause 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.
- fullstackchris 3y ago> What if our LLMs fail to turn into AGI? 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
- erezsh 3y ago"We will soon be reaching the limits of hardware scaling for larger AI models" Worth noting it's been said before for each version of GPT, only to be proven wrong.
- NoGravitas 3y agoThere's also the question of input data, though. Current large models have been trained on all the available human-created input. Trying to add more will lead to poisoning by AI-generated data and model collapse.
- Vecr 3y agoIt's probable you can train the same system on the same data multiple times and still get an improvement. You could also train on universal sequence prediction data in between as well.
- MacsHeadroom 3y agoModel collapse is basically a myth and is a joke in the ML community. The assumptions for the model collapse paper do not hold in the real world even when training on uncrurated generated data. In fact, LLMs of equal size trained on newer web scrapes which include generated data have enhanced capabilities. But in practice training data is curated and synthetic generated (curated) training data is even better than human data. State of the art LLMs like Phi;2 or the recent GPT-4 killer Claude 3 are trained entirely or mostly on generated data.
- dondeee 3y agoCome on, what’s next? Are we going to doubt that Jesus is coming, too? Not cool
- DeathArrow 3y ago>What if AGI is not coming? Nothing of value will be lost.
- zhugeyangyang 3y agoThe next generation of large models is to train several models with different specialties (small and large), and then have a front-end for task scheduling, which is then assigned to different sub models to obtain strong capabilities and professionalism while also controlling costs?
- JoshCole 3y agoThe article claims as part of its argument that AI has not had algorithmic advances since the 80s. This is an exceedingly false premise and a common misconception among the ignorant. It would actually be fairer to say that every aspect of neural network training has had algorithmic advances than that no advances have been made. Here is a quote from research related to this subject: > Compared to 2012, it now takes 44 times less compute to train a neural network to the level of AlexNet (by contrast, Moore’s Law would yield an 11x cost improvement over this period). Our results suggest that for AI tasks with high levels of recent investment, algorithmic progress has yielded more gains than classical hardware efficiency. When you apply the principle of charity you can make their claim increasingly vacuous and eventually true. We're still doing optimization - we're still in the same general structure. The thing is, it becomes absurd when you do that. Its not appropriate to take such a premise seriously. It would be like taking seriously the argument that we haven't had any advancement in software engineering since bubble sort since we're still in the regime of trying to sort numbers when we sort numbers. Its like, okay, sure, we're still sorting numbers, but it doesn't make the wider point it wants to make and its false even under the regime it wants to make the point under. This isn't even the only issue that makes this premise wrong. For one, AI research in the 80s wasn't centered around neural networks. Hell even if you move forward to the 90s PAIP puts more emphasis on rule systems with programs like Eliza and Student than it does learning from data. So it isn't as if we're in a stagnation without advance; we moved off other techniques to the ones that worked. For another, it tries to narrow down AI research progress myopically to just particular instances of deep learning, but in reality there are a huge number of relevant advances which just don't happen to be in publicly available chat bots but which are already in the literature and force a broadening. These actually matter to LLMs too, because you can take the output of a game solver as conditioning data for an LLM. This was done in the Cicero paper. And the resulting AI has outperformed humans on conversational games as a consequence. So all those advancements are thereby advances relevant to the discussion, yet myopically removed from the context, despite being counterexamples. And in there we find even greater than 44x level algorithmic improvements. In some cases we find algorithmic improvements so great that they might as well be infinite as previous techniques could never work no matter how long they ran and now approximations can be computed practically.
- somenameforme 3y agoI find a simple thought experiment answers this question. Imagine we trained an LLM using modern methods, and gave it infinite compute, on the entirety of human knowledge from 200,000 years ago. Would that AI then be able to create calculus, even if by another name obviously? I offer that as an example, because there's 0 need for knowledge of the physical world to derive calculus. All of mathematics is entirely an invention of the human mind. I think the answer is quite obviously no. LLMs can recite their training, and recombine it in ways that correlates strongly to how a human might do so. But creating entirely new knowledge, that goes above and beyond recombinations of what is already known, remains entirely outside the domain of LLMs. An LLM trained on slow classical music is not going to create rap. And an LLM trained on rap is not going to create classical music. And those are trivial examples since it's not entirely new, but just taking a concept and using it a slightly different way than 'normal.' Math, by contrast, is literally creating something from nothing. And this ability to create something from nothing is probably the most key indicator of intelligence. And we've yet to even step foot on the path towards creating software with this ability.
- keiferski 3y agoAnother way of saying this point is that intelligence requires interaction with the world, or embodiment. Hip hop can’t be derived from European classical music without the context of 70s-80s NYC.
- somenameforme 3y agoWe could debate that, but I think it's easier to just refer to the primary example of math. I offered that as an issue that requires absolutely no external interaction required there, whatsoever. It's entirely a mental creation. Like a child might say we somehow just 'thought it up.'
- keiferski 3y agoI might be talking past you here, but: The idea that mathematics is entirely mental is a contentious statement and not one a lot of mathematicians would necessarily agree with. https://plato.stanford.edu/entries/philosophy-mathematics/ https://plato.stanford.edu/entries/philosophy-mathematics/ I think your intuition is right but I think it’s more because of the embodiment of humans in the world. The machine wouldn’t invent calculus because it wouldn’t be in a physical world that needed to.
- freilanzer 3y ago> Billions to trillions of dollars will be poured into research over the next decade. More humans than ever are looking for breakthroughs. We have exponentially increased the parallel efforts. LLM architecture might be unable to deliver in its current state, but it has ignited monumental investments into research that might find other paths. That's not really true, though. Neural network based approaches are funded, and among those mostly transformers and large language models. Real alternatives aren't funded that much, imo.
- simne 3y agoArticle based on so many erroneous assumes, I can't believe I see it on HN! Most important, authors don't know, that all modern AI based on Back Propagation calculations, just because they are easier to implement on cheap old hardware, but natural neurons working on Forward Propagation, which is magnitudes faster on inference. Unfortunately, for FP we need other hardware, but it is not mean "reaching the limits of hardware scaling", it is just scaling limits for CURRENT hardware, totally other sense. Sure, if people will play blind and avoid to see obvious things, we will have new AI winter, before somebody will reconsider FP technology.
- tennisflyi 3y agoIt and automation have always been coming. However, it will be here one day.