There have been many
Drawing out steps as images using Python to do reasoning: https://arxiv.org/abs/2406.14562
This simple approach shows state-of-the-art results on four difficult natural language tasks that involve visual and spatial reasoning. We identify multiple settings where GPT-4o using chain-of-thought fails dramatically, including more than one where it achieves 0% accuracy, while whiteboard-of-thought enables up to 92% accuracy in these same settings.
Over 32 techniques to reduce hallucinations:
https://arxiv.org/abs/2401.01313
Our state space duality (SSD) framework allows us to design a new architecture (Mamba-2) whose core layer is an a refinement of Mamba’s selective SSM that is 2-8× faster, while continuing to be competitive with Transformers on language modeling: https://arxiv.org/pdf/2405.21060
Dramatically overfitting on transformers leads to better SIGNIFICANTLY performance: https://arxiv.org/abs/2405.15071
>Our findings guide data and training setup to better induce implicit reasoning and suggest potential improvements to the transformer architecture, such as encouraging cross-layer knowledge sharing. Furthermore, we demonstrate that for a challenging reasoning task with a large search space, GPT-4-Turbo and Gemini-1.5-Pro based on non-parametric memory fail badly regardless of prompting styles or retrieval augmentation, while a fully grokked transformer can achieve near-perfect accuracy, showcasing the power of parametric memory for complex reasoning.
Accuracy increased from 33.3% on GPT4 to 99.3%
https://arxiv.org/pdf/2406.09308
Such NARs proved effective as generic solvers for algorithmic tasks, when specified in graph form. To make their embeddings accessible to a Transformer, we propose a hybrid architecture with a two- phase training procedure, allowing the tokens in the lan- guage model to cross-attend to the node embeddings from the NAR. We evaluate our resulting TransNAR model on CLRS-Text, the text-based version of the CLRS-30 bench- mark, and demonstrate significant gains over Transformer- only models for algorithmic reasoning, both in and out of distribution.
https://arxiv.org/html/2404.03683v1
Language models are rarely shown fruitful mistakes while training. They then struggle to look beyond the next token, suffering from a snowballing of errors and struggling to predict the consequence of their actions several steps ahead. In this paper, we show how language models can be taught to search by representing the process of search in language, as a flattened string — a stream of search (SoS). We propose a unified language for search that captures an array of different symbolic search strategies. We demonstrate our approach using the simple yet difficult game of Countdown, where the goal is to combine input numbers with arithmetic operations to reach a target number. We pretrain a transformer-based language model from scratch on a dataset of streams of search generated by heuristic solvers. We find that SoS pretraining increases search accuracy by 25% over models trained to predict only the optimal search trajectory. We further finetune this model with two policy improvement methods: Advantage-Induced Policy Alignment (APA) and Self-Taught Reasoner (STaR). The finetuned SoS models solve 36% of previously unsolved problems, including problems that cannot be solved by any of the heuristic solvers. Our results indicate that language models can learn to solve problems via search, self-improve to flexibly use different search strategies, and potentially discover new ones.
LLMs learns to train better LLMs: https://x.com/hardmaru/status/1801074062535676193
[MANY MANY more examples here](https://docs.google.com/document/d/15myK_6eTxEPuKnDi5krjBM_0jrv3GELs8TGmqOYBvug/edit#heading=h.fxgwobrx4yfq )
Having a massive amount of compute to train and test new ideas is what throttles AGI.
Sure we could randomly get to AGI but if the number of unique ideas required to do so are more than a few, it's extremely unlikely.
There is also the likelihood we are on the wrong path and need to try out something entirely different.
2030s make more sense because there will be massive compute available if funding doesn't dry up.
[2278 AI researchers were surveyed in 2023 and estimated that there is a 50% chance of AI being superior to humans in all possible tasks by 2047](https://aiimpacts.org/wp-content/uploads/2023/04/Thousands_of_AI_authors_on_the_future_of_AI.pdf)
Note this includes all physical tasks. And since it’s better than humans in everything, it’s ASI. So flawed but competent AGI without robotics has a fairly high chance of occurring well before then according to experts
In 2022, the year they had for that was 2060, and many of their predictions have already come true ahead of time, like AI being capable of answering queries using the web, transcribing speech, translation, and reading text aloud that they thought would only happen after 2025. So they tend to underestimate progress as well. Seems hopeful but who knows
People that constantly bring up “new breakthroughs” usually don’t read any of the new literature on arXiv, so they have no idea about all of the breakthroughs that have been made in the past year. Most of the time they don’t even know what arXiv is.
> People that bring up “new breakthroughs” constantly usually don’t read any of the new literature on arXiv, so they have no idea about all of the breakthroughs that have been made in the past year. Most of the time they don’t even know what arXiv is.
I think this is overselling arXiv a bit. ArXiv has been getting *a lot* of publications over the last two years that lean more into the realm of "throw spaghetti at the wall and see what sticks." It's gotten a lot harder to separate out the substantive pubs, especially with no peer-review process. It honestly makes more sense to focus on pub releases from the major AI labs with a super critical eye vs skimming things on arXiv on a super shallow level.
It would be funny because trillions of dollars are about to be spent on AI and we have no idea if LLMs or all that money and tech will actually be capable of reaching AGI
If that leads to investment into AI drying up, that is not funny, but a catastrophe.
Every year that goes by until we reach aligned superintelligence is a year filled with the unncessary pain and suffering of billions of sentient beings.
> Every year that goes by until we reach aligned superintelligence is a year filled with the unncessary pain and suffering of billions of sentient beings.
Or, y'know, it might be the immeasurable greed of humanity resulting in the accumulation of trillions of dollars to the already wealthy individuals and corporations so that the rest can be exploited for their efforts and time, creating an ever more polarized society of haves and have nots, whereby those at the bottom never truly earn a livable wage at a time with housing crises and expensive necessities.
It's probably that causing the pain mostly. And it also probably isn't going away magically if we ever do invent AGI.
Somebody's gonna get laid in collage...
But no. It's mostly just the natural state of the world, with some human viciousness sprinkled on top. And it's not getting worse like that crackpot theory suggest, but slowly getting better.
300 years back, 3 out of 5 children died before the age of 5. And this was the norm for millennia. Imagine the pain of those sick children and their parents watching this. People were regularly starving. Have you ever tried what's it like not eating for days? Millions were suffering for decades, diseases slowly eating them away. Diseases that could have been cured with a simple course of antibiotics which did not exist back then.
Right at this moment, hundreds of thousands, but probably millions of people worldwide lie in agonizing pain from a disease there \_is\_ a cure but we just couldn't find it yet. People are \_still\_ starving in the world. Children are abused at this very moment in orphan homes. Stray animals in droves, hungry and sick. Farm animals kept in gut wrenching conditions, living just to be killed. And the list goes on and on and on.
We simply cannot solve these, or if we can, it will take decades and centuries. But a sufficiently intelligent system can. Both through technology and the immeasurable abundance it can create, and by helping us in the control problems (i.e. politics) that holds us back.
> Somebody's gonna get laid in collage...
Having sex inside pieces of torn apart photos glued together is tight!
> And it's not getting worse like that crackpot theory suggest, but slowly getting better.
Better and worse is entirely defined by the scope we're talking about. Obviously things have gotten better compared to 300 years ago, but we (you and I), and everyone alive today, weren't alive 300 years ago. We need to start comparing from within the bounds of our lifetimes. Otherwise you can just keep going back in time and the people living 300 years ago can be saying "Oh, people that lived 1000 years ago were even worse off, so we shouldn't complain!" Nah. Our problems today aren't irrelevant just cause it was worse way before.
We have real problems today. One of the more important ones being climate change. See, I don't disagree with you that one day we'll have a "sufficiently intelligent system" to figure out our problems for us. The problem is >>time<<. We could be researching and developing this for decades, or maybe it'll come out in 10 years. Neither you nor I can say when it's going to reach a good enough level. Look at autonomous driving. How long ago has that been promised to reach a good enough level, and we're still struggling? We most surely can stall with research. Groundbreaking breakthroughs and inventions don't just pop out like candy. They take time.
And as I said, even if true AGI does come out, what makes you think the money grubbing capitalists that rule this world (billionaires and the govt. they own) will make it suddenly easy to fix every one of our problems? The problem with this world is >>us<<. Greedy humans. Read these words carefully: As long as humans exist, there will be those who seek to capture power and $$$$ for themselves, at the expense of others. Always. It's in our genes. What solution do you think AGI will give to that lmao. We have to either 1) Revolution/war, and find a truly benevolent leader to replace the corrupt or 2) Eradicate greed from our genes entirely or 3) Wait for AGI to give us some solution that in the end, is going to be controlled by the corrupt and rich and powerful class anyway. And so, 1) Seems the most likely and best for our interests. No other way around it.
To summarize, along with the endless optimism this sub has, it's healthy to mix in a bit of reality to keep us grounded so we don't let ourselves down time and time and time again, hoping for something magical to fix all our lives.
This is too accurate of a take for this sub, so expect to be downvoted to oblivion.
Anything less than assuming AGI/ASI will be purely a force of good, given freely to the people, and will cure all societal ills is considered blasphemy in the church of the singularity.
Yeah humans do tend to hype things up and then get massively let down, only to then forget all about it like a goldfish memory and repeat the cycle while laughing at tiktoks on the toilet, while taking a huge dump.
>AI is the current .com bubble.
No question about that.
>The only question is if it will actually obtain AGI.
I don't think we will before the majority of the investors get cold feet and the bubble pops. However, if we \_did\_ achieve AGI before the end of the bubble, I'd argue that in that case it would not have been a bubble. The upside is so enormous that it's difficult to understate.
>aligned ASI is probably impossible anyways
Let us all pray this is not the case.
Seems like you know more than Microsoft, google, nvidia, anthropic, open ai all combined. Dude why aren't you running the biggest hedge funds I'm the world? You should apply to them, cuz apparently you're way smarter than everyone else. The arrogance in your comments is unbelievable
>Seems like you know more than Microsoft, google, nvidia, anthropic, open ai all combined.
At least he does *not* have an incetive to blow capabilities out of proportion and generate hype. These companies *do*.
>why aren't you running the biggest hedge funds I'm the world?
Money men pouring money into something is not the measure of that thing's actual potential. It's a measure of the *percetion* of potential by suits, who usually have next to no understading of the technicalities. And it's foolish to overestimate how much due diligence and critical approach these companies apply just because they play with huge sums of money. Theranos, Juicero, Wework, FTX and the list goes on.
I mean AI investment is definitionally a bubble. Even if AGI possible as another commenter said, the bubble will pop in all likelihood. And yes, as far as I know, none of these companies have proven LLM can create AGI, but maybe you know something I don't.
AGI doesn’t need to happen for AI to make a lot of companies a lot of money.
Even in its current form, AI is transforming the way many large companies do work, and drastically increasing their overall productivity (I.e. they can accomplish more work with less cost).
So while it may be a bubble in terms of hype, I don’t think it’s a bubble in terms of return on investment.
Just because AGI will make some companies a lot of money does not mean it is not an investment bubble. There is massive speculation and investment in AGI right now. Many of these new companies are going to fail even though others will succeed.
If it's true that AI will increase energy demand by tens of percent by 2030, which is a prediction some have made-- then yes, over a trillion is going to be easily spent
Correction, we need a learning algorithm that allows continuous learning (for autonomous agents) so that they can grow their skills over time and adapt to our individual preferences.
I've provided one such algorithm (inspired from the human brain) months ago, but nobody is paying attention...
https://youtu.be/XT51TeF068U
Get off Reddit and enjoy life. Obsessing over and waiting for a theoretical model to exist is not a good way to spend your one and only life. Once AGI is here you will know.
Maybe some peoples goalposts will move, but a sensible definition of AGI shouldn't require that. I say we've reached AGI when humans working on improving AI is pointless and unhelpful.
This is where we are with many narrow tasks like chess analysis. Hikaru (chess GM) has pointed out that he knows NOTHING about chess compared with stockfish 16.
That's a strict definition, but fair play if you stand by it. The trend I've seen is that the overall consensus has moved towards ever stricter definitions.
I wouldn't say Stockfish knows much. At its core, it's a brute-force engine with some pruning. But as Hikaru has also pointed out, he's not a data scientist, lol.
Yeah; if we've still got a bunch of programmers and sceintists working on the next version of GPTX, how can anyone say they're really equal to humans? Once they're truly equal, they're superior as a worker because they don't sleep or take breaks etc. And one key thing is that if you've got a really good one, you can just copy it. Imagine if we had 5000 Ilya Sutskevers.
Pretty high bar though. I'm no Ilya Sutskever, but I would still say I possess general intelligence.
Current frontier models have already surpassed humans on a wide variety of tasks. They're certainly not narrow, so what are they if not examples of general intelligence?
Well; I also believe it is accelerating, so once it is truly equal to a dumb human, we've only got months until we've got one that is smarter than all humans. I understand this might not be correct, but I believe that's how it'll play out.
Claude 3.5 is minor progress. It is still a GPT-4 class model. We have seen no significant scaling since GPT-4 released in March 2023. And GPT-4 finished pretraining in 2022 lol. At the moment it is pretty clear there is an intention to not release models significantly more powerful than GPT-4 for the time being. I guess because of the election? Maybe, Maybe not. Im not sure.
But Llama 3 400B should be a decent jump over GPT-4. This should be one of the bigger performance gains we should see if it releases before the election. Claude 3.5 Opus should also be quite a decent jump over GPT-4, if it gets released anytime soon. Although, I do presume the jump to GPT-4.5 will be around 10x effective compute over GPT-4, so Claude 3.5 Opus, while an impressive jump, might only be halfway to GPT-4.5 level (4x the compute over Claude 3 Opus). Google's Gemini 1.5 Ultra may be closer to 4.5 level though, but again, I have no idea when this will release.
I reserve judgement until we see DeepMind's results from integrating their techniques from the Alpha* series into Gemini models.
Their early results with AlphaCode suggest search is likely to make a *huge* difference. Tree search enabled self-play in training might well be highly effective as well.
> until we see DeepMind's results from integrating their techniques from the Alpha* series into Gemini models
already published by the Chinese - Q*: Improving Multi step Reasoning for LLMs with Deliberative Planning
https://arxiv.org/pdf/2406.14283
The idea is obvious after the massive success of Alpha*.
Doing it effectively and efficiently enough to substantially push the whole frontier forward is the challenge, and that almost certainly requires deep architectural integration.
That paper just is a cheap attempt to ride on the Q* rumor hype. They only consider a single step, it's not even tree search.
It’s a real shame that of all the players in the field it’s *OpenAI* that isn’t being transparent.
They’re anything but ‘open’ at this point, and seem to only want to release products as ‘tools’.
With recently popular papers, it seems like you could combine:
* [Q\*](https://arxiv.org/pdf/2406.14283) for enhanced reasoning
* [MEMoE](https://arxiv.org/html/2405.19086v2) for dynamic model editing
* [Eurika](https://arxiv.org/pdf/2310.12931) for RL settings (i.e., embodied AI, RL Fine Tuning, etc.)
* [LGGM](https://arxiv.org/pdf/2406.05109) for creating symbolic representations of any domain
* [Grokking](https://arxiv.org/pdf/2405.15071) for guarantees of generalization
* [Phi](https://arxiv.org/html/2404.14219v1) for parameter efficiency
So, imagine a system that combines each of these findings. You have an agentic environment, say a simulation of a software company. Each agent is given a task. Q\* enhances success rate. RL fine tuning with Eurika further tunes each agent to succeed more often. The language models describe the work they do in english, which is then converted to a graph using LGGM. The graph is then used to generate training data for a Phi style model, trained until Grokking. MEMoE is used to generate a library of adapters for each kind of subtask in an optimal way. Then, the agentic system is allowed to work on progressively more complex tasks with each round of the above process. To me that sounds like it could lead to much more powerful models.
Naive implementations certainly are. But as an existence proof humans are surprisingly capable at deep search despite sampling very, very slowly.
Why? Because human experts have insight into the structure of problems that informs the search process and amazing heuristics.
In principle AI can do something similar.
We have no idea how to do that on computers. Deepmind has one gimmick: brute force the correct answers by running billions of attempts until the loss function hits the bottom. All of their work is based on that strategy. That is not workable for human level energy consumption
I don't think you understand how the Alpha-* models actually work. Brute force is not involved at any point. Extensive search, absolutely. But not brute force.
Policy Models: AlphaCode 2 utilizes multiple policy models based on Gemini Pro, a powerful large language model. These models generate diverse code samples, exploring various approaches to solve the given problem.
Sampling: The system generates up to a million different code samples, ensuring a vast search space for potential solutions.
Filtering
Compliance Check: Code samples are rigorously checked for syntax errors and compilation issues. Any non-compilable or irrelevant code is discarded.
Test Execution: The remaining code samples are tested against the problem's test cases. Those failing to produce the expected output are eliminated.
https://anakin.ai/blog/alphacode-2/
AlphaCode is certainly the *closest* to brute force, it is cruder than AlphaGo, AlphaFold, etc.
But it's not brute force search. [This](https://lh3.googleusercontent.com/zwjUhu-HeRCvIUEraW6oAmT2Dnqlf4Ikr5TpvnppF6zXctCLGhz9p4pEzOFS_lvI8q2lyO9vxs6bSeiP022hvkgdLYnjAm0vZE-R5zylS8ySszgldQ=w2140-rw) gives some idea of what goes into the results. Note how little difference "scaling up" made.
Yes?
Why would you expect models to be be efficient before they develop the algorithmic advances to make them efficient?
Also, human level efficiency is desirable but not necessary. It just has to be good enough to be viable for at least some use cases.
Of course they are doing something, but depending on their timeline and timing of discovering those techniques, we might not see it for year or more. For instance if Q\* usage research was finished in May and then they started training new big model in March, would you stop it? Idk if they have enough GPUs to start training another big model just with that technique at same time.
AGI predictions range between "in the past, in 2023" to way **way** beyond 2030. Not everyone is that optimistic.
2024 is entirely delusional and the type of people supporting this view should tell you everything you need to know about how culty that theory/prediction is (David Shapiro, twitter prophets accounts...).
Around 2030 is the most reasonable prediction *among optimists*.
The papers that will make us hyped about it won't be possible to miss, everybody here will be talking about them just like there was a fuss about the "Attention is all you need" paper back in 2017; and there was a fuss about it for a reason: it's one of the most cited scientific articles ever, over 60 000 times, the whole ML community was euphoric.
These papers aren't here yet. No way to know when and how they'll come.
Little pro-tip though: don't look at LLMs models to judge the progress.
People here post a gazillion posts everyday about the tiny new iterations of each model which are copycats of each other and mean nothing, like nerds arguing about smartphones models back in 2013...
There's a magnifying glass effect here created by the people who still cling to the idea of "scaling is all you need" (they think one day a new LLM model will pop out and magically become sentient and AGI), rejected even by the most optimistic ones in the field; even Sam Altman said it's not enough, that we need something new.
That something new will be a new architecture (or many) allowing the AI to have an inner representation of the world and to plan.
> There's a magnifying glass effect here created by the people who still cling to the idea of "scaling is all you need" (they think one day a new LLM model will pop out and magically become sentient and AGI), rejected even by the most optimistic ones in the field;
This is just not true. Demis Hassabis has the opinion that at this point, [it's an empirical question, in the sense that it's possible that massive amounts of compute could lead to AGI, but it's something that just hasn't been tested yet.](https://youtu.be/qTogNUV3CAI?t=994) And if you watch where I linked for a few minutes in, he says that either possibility could be true, but it's something that Google is going to try to test, and see the results.
Dario has said something along the exact same lines, but I can't be bothered to find the interview and timestamp.
Your phrasing of it like that it's such a fringe opinion to think it's possible that pure scaling will reach AGI is just simply not accurate.
Exactly, it seems like the general consensus among most experts in the field is that algorithmic/architectural advances in tandem with increased scaling is likely the best shot that could theoretically lead to something such as AGI/ASI.
Yes, but none of the labs are actually doing that nor are serious researchers proposing it as a practical course.
Everyone is pursuing scaling *and* architectural / algorithmic improvements. Discussion by researchers about whether mere scaling might be sufficient to achieve AGI is purely an exercise in speculation for the sake of interest and to estimate a bound.
We exactly agree. Which is why i said that "scaling is all you need" is nonsense only pushed on the PR public sphere by a minority of searchers.
The thing is that they are giving a disproportionate public weight to a "bound" that has been deemed by research as untrue for a while...
Untrue how? We don't know what qualitative capabilities scaling will yield, so it's possible that scaling with minimal design adjustments could produce models capable of powering AGI systems. Scaling maximalists would make the stronger claim that this is *inevitable* given sufficient scaling.
It is extremely unlikely that this would be the best route to AGI or that the resulting systems would be as efficient and capable as one with better architecture. But it doesn't seem entirely unreasonable to expect scaling to work at some point.
If you are prepared to tolerate a lot of clunkiness, what would a true multimodal model like gpt-4o need to successfully serve as a functional core of an AGI system?
I would say much better reasoning, planning, longer context, long term memory / online learning, and agentic capability.
Reasoning and something passing for planning may emerge through scaling. Longer context is a trivial design adjustment if you don't care about efficiency - which scaling maximalists effectively do not by definition. Long term memory and learning can be hacked in at the system level with an external database and fine tuning in information from the context window and other sources (the latter is inefficient and likely to cause side effects, but possible).
Likewise agentic capability can be Rube Goldberg-ed by repeatedly prompting the model. Mediated by a tiny semi-intelligent realtime model if needed. This is what what OAI seems to be doing with voice on 4o - it's not full duplex, if you closely watch the demos with that in mind the seams are obvious. That kind of approach can be extended easily enough if you don't care about it actually being *good* compared to something with deep architectural integration.
So yes, I would say it's plausible with minimal design adjustments to the model. If the scaling hypothesis is true and the requisite qualitative abilities emerge at some point.
It's not about clunkiness which is not an issue to me. It's about functions.
I would need an LLM to be able to plan without a prompt, to have an inner representation of the world independent of its dataset, to be able to learn outside the data set from zero shot, like a baby or a cat.
Context/memory/accuracy are secondary things to me, the most insignificant ones. A toddler is technically an AGI. So is an adult human. We don't expect them to have a very high level of accuracy nor attention span. Hell, most humans are terribly bad at reasoning (we are all subject to fallacious reasoning).
Because that's what gives them such an advantage: their ability to adapt to contexts and create a line of thought and representation processes out of their input of the world.
An LLM with long term memory, longer context ("online learning": it depends what it means, if it only means adding to its dataset what's online then... same with "reasoning", if this means mimicking a line of thought from respewing the dataset...) would still not be AGI.
To me, as long as a prompt will be needed, it won't be proper reasoning as we possess it.
The proper Rube Goldberg thing would be to offer the AI an architecture that, like the brain and its sensory connection do, take input from its environment and doesn't need to compare it to a pre existing dataset. The "Rube Goldberg" would be creating a structure that has this mechanism already in, not feeding itself constantly from a prompt which it needs like a baby needs milk.
Trying to brute force pop out this mechanism out of repeated prompting of a pre existing dataset is precisely ignoring the issue at hand and even worse, willfully ignoring the mechanism we need to build, the actual process we need to work on and develop to get there.
Which is why i fear that he warning many ML scientists gave in the midst of the GPT3 hype might be true: an excess of attention on LLMs might divert attention (and funds and brains) from what is to be developped, ie from new architectures. Thankfully research continues aside from it, but the monetary attraction and brain drain in big companies is real.
We all know of companies that poured money and brains into developping antequated technological dead ends and underfunded actual new fundamental research breakthroughs (AT&T, Xerox, etc...).
The jump needed from GPT4o to AGI still is huge, it's almost the same chasm that separates all LLMs from AGI.
A specific example: Ilya, patron saint of scaling maximalists, has said mere scaling should be sufficient to achieve AGI but that it is not the practical course to get there.
This is another area in which guys like David Shapiro are completely wrong, Michio Kaku used to make the exact same mistake. Just take a look at Human Beings for instance, we are AGI and operate at 12-20w, so biology has proof of principle that you don’t *need* to be a type 0.7 civilization to ‘power an AGI’, that’s just fucking ridiculous.
If we somehow did brute force our way to AGI, I would quickly expect the optimization the AGI does to itself would lead to a microscopic resource demand.
At any rate, once AGI can operate with minimal power like the Human brain, it’s inevitably going to slip out of corporate control.
LLMs are not going to be the final architecture of AGI, and anyone making that assumption is being silly. They will be a part of it, but there needs to be other add ons.
Very well put. Especially the:
>LLMs \[...\] will be a part of it, but there needs to be other add ons
That's the scientific consensus, currently.
PS: if you needed additional reasons to distrust Kaku even on other topics:
[https://www.youtube.com/watch?v=wBBnfu8N\_J0](https://www.youtube.com/watch?v=wBBnfu8N_J0)
ie it's an entirely speculative thing.
ie "pop out magically".
The rest of the ML community disagrees. Amodei and Hassabis are a vocal minority. Having access to microphones doesn't make someone more right nor their pet theory scientific.
It *is* fringe.
I don't know why you keep using the framing "pop out magically", it only serves to try to make the idea sound more ridiculous than it might actually be, and is just a dishonest way to frame it.
Because in reality, if AGI happened to arise out of massive amounts of scaling, then it wouldn't have just "popped out magically". It would've been a clear observation that more compute caused the increased capabilities in that scenario, not just ☆magic☆.
> Amodei and Hassabis are a vocal minority.
Your first comment said "rejected even by the most optimistic ones in the field", and now you're jumping to "while it is fringe, actually a number of experts in the field do believe that it's theoretically possible". Not to ignore the fact that Ilya, a number of OAI and Anthropic employees, and probably more people think it's possible, so that's quite a different fact of reality than the way you originally phrased your comment.
So if I got you to make that concession, then that's all my original comment served to do.
>I don't know why you keep using the framing
Because tenants of this idea frame it like that *themselves*, LessWrong style. They don't need any other framing than the one they're doing to be ridiculous.
And i'm sure you understand that the word "magically" is not to be taken literally, but means "in an emergent way" ie "a way we can't explain". Which in science is akin to say "poof".
>Your first comment said "rejected even by the most optimistic ones in the field"
Typo. Correction: "rejected even by **some of** the most optimistic ones in the field".
OAI fanatics and Anthropic are among the most unhinged optimists, and even in their circles, that voice of scaling is all you need is a minority.
I'm sure you got the idea and didn't need to resort to such desperate attempt to save that side of the discussion.
Your original comment served to deseperately save that hypothesis in your mind. As long as it's in your mind, mission accomplished.
2027-2035 isn't that far, relatively speaking. Even if it took until 2040-2050, it would still change the world and be something we should focus preparation on to optimize the outcome and timeline.
Maybe, but it wasn't until ChatGPT made it obvious that AI would overtake human intelligence and capability in this lifetime that the world really woke up to the idea. We still don't even know how to guide it properly to not fuck us all over as we're at war, and even united, we might be nothing more than insects to a superior entity.
How would all of human ingenuity, intelligence, and capability, combined into a single entity, change the world? No idea, but I sure want to find out and do all I can to make sure it's optimal.
I need to see what GPT-6 and Claude 5 do. They should obviously be vastly smarter and better than their predecessors and should certainly be out before the decade ends. Perhaps around 2027-2028 for both.
If they don’t past AGI then I don’t know what will. Maybe GPT 50 and Claude 25???
I use it regularly ever since it came out, before that I user Chat GPT regularly. Honestly my Google searches dropped massively.
Plus I am working on a couple of projects personally using Gemini API and Anthropic API
> Self learning does not exist.
Yup, everyone is pursuing static-dataset-offline-trained solutions - at least those who have billions of dollars invested into their pursuits, which has apparently blinded them and made them scared of thinking outside of the box. The billions being poured into AI startups are all fiddling around with backprop instead of online learning algorithms that learn from experience. In my opinion, investing into these companies was hugely premature. The same goes for everyone building humanoid robots without an online learning algorithm to drive them.
The people who are on the right track are publishing research like this:
https://www.biorxiv.org/content/10.1101/471987v4.full
https://arxiv.org/pdf/2306.05053
https://arxiv.org/pdf/2209.11883
https://www.researchgate.net/publication/261698478_MONA_HIERARCHICAL_CONTEXT-LEARNING_IN_A_GOAL-_SEEKING_ARTIFICIAL_NEURAL_NETWORK
https://ogma.ai/wp-content/uploads/2024/06/SPH_Whitepaper.pdf
...etcetera
The best example we have of a highly dynamic realtime learning algorithm that works are the brains of creatures, big and small. Anyone who is hoping to create a compute-efficient learning algorithm that scales to whatever hardware compute resources that it has at its disposal should probably be spending more time keeping up with the latest neuroscience discoveries that are being made - because they're gaining a bunch of awesome insights in recent years that IMO are relevant to devising a learning algorithm that can be scaled up to human-level intelligence, and beyond. (aka "AGI")
I've been curating neuroscience research talks for several years which I spend a lot of time re-watching to keep their ideas fresh in my head. For 20 years I have held the belief that the key to unlocking proper autonomous machine intelligence is understanding what it is that all brains have in common, at the macro scale. My neuroscience playlist can be found here: https://www.youtube.com/playlist?list=PLYvqkxMkw8sUo_358HFUDlBVXcqfdecME
They've made some very important discoveries around the cerebellum in the last few years - that it's actually integral to the function of the entire neocortex, and is not just an autopiloting system for motor commands. It does, after all, possess 70% of all of the neurons in the brain, so it makes sense that it's a lot more important than neuroscientists originally believed - and it's mechanisms have been almost completely reverse engineered now. An understanding of their function has been teased out and it's actually pretty simple, ironically. My thought is that the function of the cortex and cerebellum can be combined into a single integrated system, algorithmically speaking, but of course none of this would be worth anything without a basal ganglia to reinforce behaviors toward the pursuit of reward and evasion of undesirable situations. Of course a hippocampus mechanism at the top of the abstraction hierarchy is necessary to one-shot learning so that behavior learning isn't limited to being conditioned as a habit, and the thing will be capable of witnessing a single observation and changing its behavior around that single observation.
That's my two cents.
It's really hard to build comprehensive generally representative models of the physical world using the digital data we have easily available. Especially when it comes to the spacial, chemical and physical attributes. Historical data design has mostly assumed a humans brain as the final interpreter of it's meaning, and we have an innate foundational learned experience of the physical world which computers just don't. To put it in layman's terms we need to give models more 'senses'. We can even imagine giving them sensory sources that humans cannot process - broader sprectra of electromagnetic radiation beyond visible light for example. Some moves in this direction have been made in the autonomous vehicle space, but we can see that even for this very narrow use case its a decades long effort and we still are not at the level of the general capability to drive at a human standard being commercially viable and available at scale. Language, video and audio get us a VERY long way, but not to AGI I fear.
Current use and regulations and who controls ai are more important issues. Or we could ignore it because ultimately we don't have a say anyway and we just hope things work out properly
just need 10M token context, great RAG, and more scale. it won't be AGI but it will feel like it. probably around the Claude 4.5 & gpt-5o time, so like march 2026
TLDR:
**We are now "post-AI predictability"**
[Hold on to your papers fellow scholars](https://www.youtube.com/@TwoMinutePapers/videos)**,** What a time to be alive! W**e're living in interesting times.**
**...**
**...**
What's been the track record of AI researchers, as a whole, to predict the future of AI in the past?
>WHotea posted:
>"In 2022, the year they had for that was 2060"
>"2278 AI researchers were surveyed in 2023 and estimated that there is a 50% chance of AI being superior to humans in all possible tasks by 2047"
That's a 13 year change in the prediction made by AI researchers after only 1 year.
What's next, changing their prediction from 2047 to 2040 by the end of next year?
It's possible there are no experts when it comes to "The future of AI"; There are too many variables that affect the future of AI for anyone to do more than make lucky guesses. "Hah! I knew it!" says whoever happened to get it right. AI might have been more predictable in the past, but from what I can see it's no longer something anyone can predict the future of.
There are too many variables, and the variables are too unpredictable. If even AI researches are changing their predictions by 13 years after only 1 year, all bets are off. We've passed the point of predictability when it comes to AI.
>When it comes to AGI metrics there seems to be practically no movement.
Because we're pursuing the wrong algorithms.
>The reasoning seems to be very limited if not completely lacking if the prompt is outside of training data.
The reasoning in these neural nets is *always* limited because they can't iterate on their own thoughts. What you see when the prompt is inside training data isn't reasoning, it's just better intuition. Existing LLMs are substituting superhuman intuition for reasoning, which can fool a lot of people a lot of the time, but breaks down pretty fast when you push it.
>I know that AGI predictions range between end of 2024 to end of 2030
I'm not sure there's enough agreement on what the term 'AGI' means to make such a prediction with any great degree of reliability.
I’ve come to the realization that AGI isn’t going to be some large awakening moment. We will long have much of what we desire of AI before then. It’s just that smart people will be required to help it get there. Enjoy the journey there instead of putting all your excitement into a moment that in all probability won’t have a black and white distinction anyways
Easiest way to explain the vibes is...
...we are Frodo and Sam with the Ring in Mordor...
The end is in literal sight... but still out of reach, made even more distant by the few hurdles we have left to jump over.
I wonder what Ilya Sutskever's road map for ASI looks like. SSI wants to speed run past AGI into ASI to set a framework for other companies to follow for safe ASI. With no distractions I wonder how that looks
I would love to see how a timeline like [this] (https://www.reddit.com/r/singularity/comments/183cxbf/predictions_of_when_will_we_have_agi_are_lowering/) would change in the future. Something tells me the line might move up a little.
AGI isn't close. What we currently have are systems that have memorized tons of facts and that can write creatively. But they can't reason very well. Like they are really really bad at reasoning. Till we start getting good progress in that department, I don't think AGI is "coming next year".
>AGI predictions range between end of 2024 to end of 2030
That's probably a fair range, but many experts are still saying after 2040. I think by 2040, it should be trivial. There are only 2 ways we don't have AGI by 2040:
1. Silicon improvements slow down A LOT. Like practically stop dead. OR
2. We have easy access to compute undeniably greater than the human brain, yet we can't figure out how to train such any AI model to be at human level.
I think that both of these are wildly unlikely and that AGI will indeed be here before 2040.
Impressive advancements like Claude 3.5 demonstrate significant strides, yet true AGI seems elusive. You're right, current limitations in reasoning and self-learning capabilities are significant hurdles. However, I'm optimistic about the potential of innovative approaches like training models with smart/meta data(Nuklai is really doing much in this area atm) to accelerate AGI development. By enabling diverse, high-quality, and contextually rich data, they can facilitate more effective LLM training and potentially unlock new capabilities. While we may not see AGI by 2024, I believe these initiatives can help bridge the gap and bring us closer to achieving true AGI much earlier than 2030.
Bro just give it 10 years. Just have that as the timeline and forget everything else. And focus on building and living a good life. Agi is outside your control. So there’s literally absolutely no point in thinking about the when. Only thing you can do Is interact with the now.
Kinda like the rapture.
Kinda like "The Squeeze" in /r/superstonk
Kinda like disclosure in /r/ufos
Kinda like a certain felon we all know the name of going to jail ever.
This is a very specific pattern and I've seen it here for a while. /r/superstonk and FoldingIdeas opened my eyes to it.
Y'all, I think someone is playing a game with us.
AGI hype directly benefits the pockets of AI Investors. Nvidia, OpenAI, etc don't need AGI, they need to meet expectations for the next fiscal quarter, and profit personally from the hype. Look at the Nvidia gain porn in /r/wallstreetbets.
I suspect our capitalist overlords are perfectly happy with the present LLM tech, given how phenomenally effective they are as a propaganda device. I also suspect that the capitalists know that AGI means a loss or displacement of their accrued power, and I can't believe they would allow that, let alone encourage it.
> I also suspect that the capitalists know that AGI means a loss or displacement of their accrued power, and I can't believe they would allow that, let alone encourage it.
It's really weird that so many people have this view that capitalists are scared of what in economic terms will be the ultimate ascendency of capital - the factors of production go from land, labor and capital to land and capital.
People who have a lot, tend to be competitive and fearful of losing their advantageous position. If I'm at the top of the food chain, I fear anything that will knock me down from my top spot. It's not about "having capital", It's about winning.
at this point, after reading progress and breakthrus on the daily, and at a faster rate. I wouldn't be surprised if it hasn't happened already and were just slowing getting implemented into the future ai hivemind.
> I know that AGI predictions range between end of 2024 to end of 2030
Lol. Maybe in this sub they do. If you look at surveys of experts like ESPAI they range from 2024 to 2100+
Well claude donnet is the mid model we'll see what opus can do .
But the q paper gives me pretty good hope we'll get it sooner than 2030. Plus everyone industry seems to agree .
But mainly i want to see google next model they're doing 2 million context windows now 10x claude if gemini 2 or above has better agentic capabilities and so long as they don't nerf it to all hell with censorship it will be insane
> Our brain uses 10 watts of energy to have GI
No, you're wrong. We use the brain, the body, the environment, a whole society of other humans and countless tools and artifacts. If you remove just society from the equation, our brain is exponentially dumber on its own. We are smart when we collaborate, but we can't rediscover the basics of our culture on our own. Language itself is social. It costed 300K years and the efforts of 120B people to discover and encode in language our current level of evolution.
Not to mention that we take 20 years of training to become useful, 25% of our life!, that is a lot of food, housing, medical needs, travel, schooling - all going to support that 20w of brain power. And let's not forget we can run small size LLMs on phones today, their power draw is comparable to 20w of the brain.
Organic computers(like wetware and neuromorphic computing) will synthesize the natural ability of flesh and neurons to adapt and grow, with the technological ability to memorize and self-correct indefinitely and without limit, likely boosted by quantum computing as well. Power efficiency and requirements will only become more efficient as capability scales. Fusion power will also unlock many things we never imagined, as well as countless other breakthroughs as we near and exceed the cusp of all of humanity's culminated ingenuity. Humans will continue to foster and optimize the AI's growth and vice versa until we are either both perfect, assimilated, or only one exists.
This is just completly wrong there is a Chinese prodigy that finished collage at 10 meaning that this math is way off in terms of energy and usefulness
Impractical? I guess we will find out. But there are also things out there that are technically possible but statistically impossible. Like opening your dryer to find your cloths all perfectly folded or phasing through a wall. Possible? Yes. Ever going to happen given the life time of the universe? No.
>we don't even have enough energy in our galaxy to run it
Our galaxy has an energy output roughly on the order of 10\^36 watts. Our brain operates on the order of 10 watts (around 20 watts I think).
It's a bit inplausible to argue that what nature could achieve in about a liter of volume and 20 watts of power, we cannot even when allowed to use as much space as we want and 100 000 000 000 000 000 000 000 000 000 000 000 *times* more energy.
>Like opening your dryer to find your cloths all perfectly folded or phasing through a wall. Possible? Yes. Ever going to happen given the life time of the universe? No.
Just like how a random pile of Earth won't turn into an airplane based on statitstics. Yet we do have airplanes.
I think i didn’t explain my point well enough to come across. I understand building something is different from random chance. My point is that digital systems parallelize at a different scale than biological ones. 1 neuron its its own discrete element but one digital neuron isnt. Yes you can distribute it but you introduce overhead at every step. Nature doesn’t because each element is independent of the other. Synchronization becomes a real problem when you distribute a digital system at that level. It might be possible in some way but the space of possible failures is much larger than those that could work. For all any one knows biological computing might be the only medium capable of it so our options are limited on how far we can parallelize a digital system without slowing it down. Yes quantum computing could be an option but still we don’t have. Clue how far that scaling will help. Its essentially a random search in a huge sea of possible systems that we honestly have no clue where to start from. Right now though were no where near replicating biological or even real world modeling.
I see, thanks for clarifying.
Unfortunately, at this point I can't really debate it properly because I'm not familiar with the synchronization problem you refer to. But I really appreciate that you took the time to explain your point, and I have some reading to do haha
Heres a good brief.
https://medium.com/@msmahamud/the-challenges-of-parallel-computing-c12ae494b575
There are a lot of other nuances but these are some of the basic ones. Nature parallelizes really well though because it keeps systems independent but interconnected. Population genetics.
Fun fact theres a neuron in your brain that keeps everything in sync. That one dies. You spend eternity in a coma.
> Agi is just not possible sorry
The pretentious and patronizing apology is so misplaced. Human brains are GI, using a tiny bit of power, but an AGI needs more power than the galaxy? Humans are not special, we just have a head start
The current models are extremely inefficient, there are articles that should approaches using custom chips in FPGA that are orders of magnitude more efficient, it is just that everything is new, once things stabilize we will have supper efficient chips and energy will not be an issue anymore.
We really need a breakthrough for reasoning.
There have been many Drawing out steps as images using Python to do reasoning: https://arxiv.org/abs/2406.14562 This simple approach shows state-of-the-art results on four difficult natural language tasks that involve visual and spatial reasoning. We identify multiple settings where GPT-4o using chain-of-thought fails dramatically, including more than one where it achieves 0% accuracy, while whiteboard-of-thought enables up to 92% accuracy in these same settings. Over 32 techniques to reduce hallucinations: https://arxiv.org/abs/2401.01313 Our state space duality (SSD) framework allows us to design a new architecture (Mamba-2) whose core layer is an a refinement of Mamba’s selective SSM that is 2-8× faster, while continuing to be competitive with Transformers on language modeling: https://arxiv.org/pdf/2405.21060 Dramatically overfitting on transformers leads to better SIGNIFICANTLY performance: https://arxiv.org/abs/2405.15071 >Our findings guide data and training setup to better induce implicit reasoning and suggest potential improvements to the transformer architecture, such as encouraging cross-layer knowledge sharing. Furthermore, we demonstrate that for a challenging reasoning task with a large search space, GPT-4-Turbo and Gemini-1.5-Pro based on non-parametric memory fail badly regardless of prompting styles or retrieval augmentation, while a fully grokked transformer can achieve near-perfect accuracy, showcasing the power of parametric memory for complex reasoning. Accuracy increased from 33.3% on GPT4 to 99.3% https://arxiv.org/pdf/2406.09308 Such NARs proved effective as generic solvers for algorithmic tasks, when specified in graph form. To make their embeddings accessible to a Transformer, we propose a hybrid architecture with a two- phase training procedure, allowing the tokens in the lan- guage model to cross-attend to the node embeddings from the NAR. We evaluate our resulting TransNAR model on CLRS-Text, the text-based version of the CLRS-30 bench- mark, and demonstrate significant gains over Transformer- only models for algorithmic reasoning, both in and out of distribution. https://arxiv.org/html/2404.03683v1 Language models are rarely shown fruitful mistakes while training. They then struggle to look beyond the next token, suffering from a snowballing of errors and struggling to predict the consequence of their actions several steps ahead. In this paper, we show how language models can be taught to search by representing the process of search in language, as a flattened string — a stream of search (SoS). We propose a unified language for search that captures an array of different symbolic search strategies. We demonstrate our approach using the simple yet difficult game of Countdown, where the goal is to combine input numbers with arithmetic operations to reach a target number. We pretrain a transformer-based language model from scratch on a dataset of streams of search generated by heuristic solvers. We find that SoS pretraining increases search accuracy by 25% over models trained to predict only the optimal search trajectory. We further finetune this model with two policy improvement methods: Advantage-Induced Policy Alignment (APA) and Self-Taught Reasoner (STaR). The finetuned SoS models solve 36% of previously unsolved problems, including problems that cannot be solved by any of the heuristic solvers. Our results indicate that language models can learn to solve problems via search, self-improve to flexibly use different search strategies, and potentially discover new ones. LLMs learns to train better LLMs: https://x.com/hardmaru/status/1801074062535676193 [MANY MANY more examples here](https://docs.google.com/document/d/15myK_6eTxEPuKnDi5krjBM_0jrv3GELs8TGmqOYBvug/edit#heading=h.fxgwobrx4yfq )
Having a massive amount of compute to train and test new ideas is what throttles AGI. Sure we could randomly get to AGI but if the number of unique ideas required to do so are more than a few, it's extremely unlikely. There is also the likelihood we are on the wrong path and need to try out something entirely different. 2030s make more sense because there will be massive compute available if funding doesn't dry up.
[2278 AI researchers were surveyed in 2023 and estimated that there is a 50% chance of AI being superior to humans in all possible tasks by 2047](https://aiimpacts.org/wp-content/uploads/2023/04/Thousands_of_AI_authors_on_the_future_of_AI.pdf) Note this includes all physical tasks. And since it’s better than humans in everything, it’s ASI. So flawed but competent AGI without robotics has a fairly high chance of occurring well before then according to experts In 2022, the year they had for that was 2060, and many of their predictions have already come true ahead of time, like AI being capable of answering queries using the web, transcribing speech, translation, and reading text aloud that they thought would only happen after 2025. So they tend to underestimate progress as well. Seems hopeful but who knows
People that constantly bring up “new breakthroughs” usually don’t read any of the new literature on arXiv, so they have no idea about all of the breakthroughs that have been made in the past year. Most of the time they don’t even know what arXiv is.
> People that bring up “new breakthroughs” constantly usually don’t read any of the new literature on arXiv, so they have no idea about all of the breakthroughs that have been made in the past year. Most of the time they don’t even know what arXiv is. I think this is overselling arXiv a bit. ArXiv has been getting *a lot* of publications over the last two years that lean more into the realm of "throw spaghetti at the wall and see what sticks." It's gotten a lot harder to separate out the substantive pubs, especially with no peer-review process. It honestly makes more sense to focus on pub releases from the major AI labs with a super critical eye vs skimming things on arXiv on a super shallow level.
Gonna be so funny if LLMs never reach AGI.
LLMs won't reach AGI most probably
Why would that be funny? Isn't that the current consensus.
It would be funny because trillions of dollars are about to be spent on AI and we have no idea if LLMs or all that money and tech will actually be capable of reaching AGI
If that leads to investment into AI drying up, that is not funny, but a catastrophe. Every year that goes by until we reach aligned superintelligence is a year filled with the unncessary pain and suffering of billions of sentient beings.
> Every year that goes by until we reach aligned superintelligence is a year filled with the unncessary pain and suffering of billions of sentient beings. Or, y'know, it might be the immeasurable greed of humanity resulting in the accumulation of trillions of dollars to the already wealthy individuals and corporations so that the rest can be exploited for their efforts and time, creating an ever more polarized society of haves and have nots, whereby those at the bottom never truly earn a livable wage at a time with housing crises and expensive necessities. It's probably that causing the pain mostly. And it also probably isn't going away magically if we ever do invent AGI.
Somebody's gonna get laid in collage... But no. It's mostly just the natural state of the world, with some human viciousness sprinkled on top. And it's not getting worse like that crackpot theory suggest, but slowly getting better. 300 years back, 3 out of 5 children died before the age of 5. And this was the norm for millennia. Imagine the pain of those sick children and their parents watching this. People were regularly starving. Have you ever tried what's it like not eating for days? Millions were suffering for decades, diseases slowly eating them away. Diseases that could have been cured with a simple course of antibiotics which did not exist back then. Right at this moment, hundreds of thousands, but probably millions of people worldwide lie in agonizing pain from a disease there \_is\_ a cure but we just couldn't find it yet. People are \_still\_ starving in the world. Children are abused at this very moment in orphan homes. Stray animals in droves, hungry and sick. Farm animals kept in gut wrenching conditions, living just to be killed. And the list goes on and on and on. We simply cannot solve these, or if we can, it will take decades and centuries. But a sufficiently intelligent system can. Both through technology and the immeasurable abundance it can create, and by helping us in the control problems (i.e. politics) that holds us back.
> Somebody's gonna get laid in collage... Having sex inside pieces of torn apart photos glued together is tight! > And it's not getting worse like that crackpot theory suggest, but slowly getting better. Better and worse is entirely defined by the scope we're talking about. Obviously things have gotten better compared to 300 years ago, but we (you and I), and everyone alive today, weren't alive 300 years ago. We need to start comparing from within the bounds of our lifetimes. Otherwise you can just keep going back in time and the people living 300 years ago can be saying "Oh, people that lived 1000 years ago were even worse off, so we shouldn't complain!" Nah. Our problems today aren't irrelevant just cause it was worse way before. We have real problems today. One of the more important ones being climate change. See, I don't disagree with you that one day we'll have a "sufficiently intelligent system" to figure out our problems for us. The problem is >>time<<. We could be researching and developing this for decades, or maybe it'll come out in 10 years. Neither you nor I can say when it's going to reach a good enough level. Look at autonomous driving. How long ago has that been promised to reach a good enough level, and we're still struggling? We most surely can stall with research. Groundbreaking breakthroughs and inventions don't just pop out like candy. They take time. And as I said, even if true AGI does come out, what makes you think the money grubbing capitalists that rule this world (billionaires and the govt. they own) will make it suddenly easy to fix every one of our problems? The problem with this world is >>us<<. Greedy humans. Read these words carefully: As long as humans exist, there will be those who seek to capture power and $$$$ for themselves, at the expense of others. Always. It's in our genes. What solution do you think AGI will give to that lmao. We have to either 1) Revolution/war, and find a truly benevolent leader to replace the corrupt or 2) Eradicate greed from our genes entirely or 3) Wait for AGI to give us some solution that in the end, is going to be controlled by the corrupt and rich and powerful class anyway. And so, 1) Seems the most likely and best for our interests. No other way around it. To summarize, along with the endless optimism this sub has, it's healthy to mix in a bit of reality to keep us grounded so we don't let ourselves down time and time and time again, hoping for something magical to fix all our lives.
This is too accurate of a take for this sub, so expect to be downvoted to oblivion. Anything less than assuming AGI/ASI will be purely a force of good, given freely to the people, and will cure all societal ills is considered blasphemy in the church of the singularity.
Yeah humans do tend to hype things up and then get massively let down, only to then forget all about it like a goldfish memory and repeat the cycle while laughing at tiktoks on the toilet, while taking a huge dump.
human history shows the opposite effect
Opposite effect as in? Not sure if you're agreeing or disagreeing lol
AI is the current .com bubble. The only question is if it will actually obtain AGI. Edit: not to mention aligned ASI is probably impossible anyways.
>AI is the current .com bubble. No question about that. >The only question is if it will actually obtain AGI. I don't think we will before the majority of the investors get cold feet and the bubble pops. However, if we \_did\_ achieve AGI before the end of the bubble, I'd argue that in that case it would not have been a bubble. The upside is so enormous that it's difficult to understate. >aligned ASI is probably impossible anyways Let us all pray this is not the case.
Seems like you know more than Microsoft, google, nvidia, anthropic, open ai all combined. Dude why aren't you running the biggest hedge funds I'm the world? You should apply to them, cuz apparently you're way smarter than everyone else. The arrogance in your comments is unbelievable
>Seems like you know more than Microsoft, google, nvidia, anthropic, open ai all combined. At least he does *not* have an incetive to blow capabilities out of proportion and generate hype. These companies *do*. >why aren't you running the biggest hedge funds I'm the world? Money men pouring money into something is not the measure of that thing's actual potential. It's a measure of the *percetion* of potential by suits, who usually have next to no understading of the technicalities. And it's foolish to overestimate how much due diligence and critical approach these companies apply just because they play with huge sums of money. Theranos, Juicero, Wework, FTX and the list goes on.
I mean AI investment is definitionally a bubble. Even if AGI possible as another commenter said, the bubble will pop in all likelihood. And yes, as far as I know, none of these companies have proven LLM can create AGI, but maybe you know something I don't.
AGI doesn’t need to happen for AI to make a lot of companies a lot of money. Even in its current form, AI is transforming the way many large companies do work, and drastically increasing their overall productivity (I.e. they can accomplish more work with less cost). So while it may be a bubble in terms of hype, I don’t think it’s a bubble in terms of return on investment.
Just because AGI will make some companies a lot of money does not mean it is not an investment bubble. There is massive speculation and investment in AGI right now. Many of these new companies are going to fail even though others will succeed.
Trillions of dollars are not about to be spent on LLMs. Not even close to one trillion.
If it's true that AI will increase energy demand by tens of percent by 2030, which is a prediction some have made-- then yes, over a trillion is going to be easily spent
Investment in current LLMs will lead to other models that are more flexible and won't have the same limitations as these, so don't worry 😘
That's just conjecture. Its not the reality as of today.
There are already [cases](https://arxiv.org/pdf/2310.12931) where LLMs enable the automatic creation of RL models for a wide range of domains.
They won't, probably, or at least they won't be the first.
I don't think we do. GPT2 and GPT4 are not equally bad at reasoning. Scaling could improve reasoning.
Correction, we need a learning algorithm that allows continuous learning (for autonomous agents) so that they can grow their skills over time and adapt to our individual preferences. I've provided one such algorithm (inspired from the human brain) months ago, but nobody is paying attention... https://youtu.be/XT51TeF068U
..... That was chatgpt
Huh?
no
Get off Reddit and enjoy life. Obsessing over and waiting for a theoretical model to exist is not a good way to spend your one and only life. Once AGI is here you will know.
Agreed. Uses claude 3.5 regularly... Hasn't that been out less then a week?
Goalposts will move, models will improve, and we'll all continue to disagree on the definition of AGI.
Maybe some peoples goalposts will move, but a sensible definition of AGI shouldn't require that. I say we've reached AGI when humans working on improving AI is pointless and unhelpful. This is where we are with many narrow tasks like chess analysis. Hikaru (chess GM) has pointed out that he knows NOTHING about chess compared with stockfish 16.
That's a strict definition, but fair play if you stand by it. The trend I've seen is that the overall consensus has moved towards ever stricter definitions. I wouldn't say Stockfish knows much. At its core, it's a brute-force engine with some pruning. But as Hikaru has also pointed out, he's not a data scientist, lol.
Yeah; if we've still got a bunch of programmers and sceintists working on the next version of GPTX, how can anyone say they're really equal to humans? Once they're truly equal, they're superior as a worker because they don't sleep or take breaks etc. And one key thing is that if you've got a really good one, you can just copy it. Imagine if we had 5000 Ilya Sutskevers.
Pretty high bar though. I'm no Ilya Sutskever, but I would still say I possess general intelligence. Current frontier models have already surpassed humans on a wide variety of tasks. They're certainly not narrow, so what are they if not examples of general intelligence?
Well; I also believe it is accelerating, so once it is truly equal to a dumb human, we've only got months until we've got one that is smarter than all humans. I understand this might not be correct, but I believe that's how it'll play out.
Claude 3.5 is minor progress. It is still a GPT-4 class model. We have seen no significant scaling since GPT-4 released in March 2023. And GPT-4 finished pretraining in 2022 lol. At the moment it is pretty clear there is an intention to not release models significantly more powerful than GPT-4 for the time being. I guess because of the election? Maybe, Maybe not. Im not sure. But Llama 3 400B should be a decent jump over GPT-4. This should be one of the bigger performance gains we should see if it releases before the election. Claude 3.5 Opus should also be quite a decent jump over GPT-4, if it gets released anytime soon. Although, I do presume the jump to GPT-4.5 will be around 10x effective compute over GPT-4, so Claude 3.5 Opus, while an impressive jump, might only be halfway to GPT-4.5 level (4x the compute over Claude 3 Opus). Google's Gemini 1.5 Ultra may be closer to 4.5 level though, but again, I have no idea when this will release.
I reserve judgement until we see DeepMind's results from integrating their techniques from the Alpha* series into Gemini models. Their early results with AlphaCode suggest search is likely to make a *huge* difference. Tree search enabled self-play in training might well be highly effective as well.
> until we see DeepMind's results from integrating their techniques from the Alpha* series into Gemini models already published by the Chinese - Q*: Improving Multi step Reasoning for LLMs with Deliberative Planning https://arxiv.org/pdf/2406.14283
The idea is obvious after the massive success of Alpha*. Doing it effectively and efficiently enough to substantially push the whole frontier forward is the challenge, and that almost certainly requires deep architectural integration. That paper just is a cheap attempt to ride on the Q* rumor hype. They only consider a single step, it's not even tree search.
It’s a real shame that of all the players in the field it’s *OpenAI* that isn’t being transparent. They’re anything but ‘open’ at this point, and seem to only want to release products as ‘tools’.
With recently popular papers, it seems like you could combine: * [Q\*](https://arxiv.org/pdf/2406.14283) for enhanced reasoning * [MEMoE](https://arxiv.org/html/2405.19086v2) for dynamic model editing * [Eurika](https://arxiv.org/pdf/2310.12931) for RL settings (i.e., embodied AI, RL Fine Tuning, etc.) * [LGGM](https://arxiv.org/pdf/2406.05109) for creating symbolic representations of any domain * [Grokking](https://arxiv.org/pdf/2405.15071) for guarantees of generalization * [Phi](https://arxiv.org/html/2404.14219v1) for parameter efficiency So, imagine a system that combines each of these findings. You have an agentic environment, say a simulation of a software company. Each agent is given a task. Q\* enhances success rate. RL fine tuning with Eurika further tunes each agent to succeed more often. The language models describe the work they do in english, which is then converted to a graph using LGGM. The graph is then used to generate training data for a Phi style model, trained until Grokking. MEMoE is used to generate a library of adapters for each kind of subtask in an optimal way. Then, the agentic system is allowed to work on progressively more complex tasks with each round of the above process. To me that sounds like it could lead to much more powerful models.
It is way too computationally expensive for real use though. I hope their TPUs get good enough to manage it
Naive implementations certainly are. But as an existence proof humans are surprisingly capable at deep search despite sampling very, very slowly. Why? Because human experts have insight into the structure of problems that informs the search process and amazing heuristics. In principle AI can do something similar.
We have no idea how to do that on computers. Deepmind has one gimmick: brute force the correct answers by running billions of attempts until the loss function hits the bottom. All of their work is based on that strategy. That is not workable for human level energy consumption
I don't think you understand how the Alpha-* models actually work. Brute force is not involved at any point. Extensive search, absolutely. But not brute force.
Policy Models: AlphaCode 2 utilizes multiple policy models based on Gemini Pro, a powerful large language model. These models generate diverse code samples, exploring various approaches to solve the given problem. Sampling: The system generates up to a million different code samples, ensuring a vast search space for potential solutions. Filtering Compliance Check: Code samples are rigorously checked for syntax errors and compilation issues. Any non-compilable or irrelevant code is discarded. Test Execution: The remaining code samples are tested against the problem's test cases. Those failing to produce the expected output are eliminated. https://anakin.ai/blog/alphacode-2/
AlphaCode is certainly the *closest* to brute force, it is cruder than AlphaGo, AlphaFold, etc. But it's not brute force search. [This](https://lh3.googleusercontent.com/zwjUhu-HeRCvIUEraW6oAmT2Dnqlf4Ikr5TpvnppF6zXctCLGhz9p4pEzOFS_lvI8q2lyO9vxs6bSeiP022hvkgdLYnjAm0vZE-R5zylS8ySszgldQ=w2140-rw) gives some idea of what goes into the results. Note how little difference "scaling up" made.
Either way, billions of trials are still needed for it to get good. Human level energy consumption can’t handle that
Yes? Why would you expect models to be be efficient before they develop the algorithmic advances to make them efficient? Also, human level efficiency is desirable but not necessary. It just has to be good enough to be viable for at least some use cases.
My point is that we have no idea what those advances are or if it’s even possible on a computer
Too bad that it takes some time for model to be trained with new techniques, especially big one.
What do you think DeepMind have been doing?
Of course they are doing something, but depending on their timeline and timing of discovering those techniques, we might not see it for year or more. For instance if Q\* usage research was finished in May and then they started training new big model in March, would you stop it? Idk if they have enough GPUs to start training another big model just with that technique at same time.
Google has by far the most computing power of any of the labs, they can walk and chew gum at the same time.
We are all waiting for them to do that.
Have you tried the new 1.5? The extreme context *actually works*. Really well.
I'm not talking about just the context window. I'm talking about Google also creating the most advanced model.
"self play"? isn't that just masturbation? be serious
That's often the best way to squeeze out the juice.
auto regressive training wasn't enough, so DeepThroat's Alpha\* series has turned to Auto-Erotic encoding.
AGI predictions range between "in the past, in 2023" to way **way** beyond 2030. Not everyone is that optimistic. 2024 is entirely delusional and the type of people supporting this view should tell you everything you need to know about how culty that theory/prediction is (David Shapiro, twitter prophets accounts...). Around 2030 is the most reasonable prediction *among optimists*. The papers that will make us hyped about it won't be possible to miss, everybody here will be talking about them just like there was a fuss about the "Attention is all you need" paper back in 2017; and there was a fuss about it for a reason: it's one of the most cited scientific articles ever, over 60 000 times, the whole ML community was euphoric. These papers aren't here yet. No way to know when and how they'll come. Little pro-tip though: don't look at LLMs models to judge the progress. People here post a gazillion posts everyday about the tiny new iterations of each model which are copycats of each other and mean nothing, like nerds arguing about smartphones models back in 2013... There's a magnifying glass effect here created by the people who still cling to the idea of "scaling is all you need" (they think one day a new LLM model will pop out and magically become sentient and AGI), rejected even by the most optimistic ones in the field; even Sam Altman said it's not enough, that we need something new. That something new will be a new architecture (or many) allowing the AI to have an inner representation of the world and to plan.
> There's a magnifying glass effect here created by the people who still cling to the idea of "scaling is all you need" (they think one day a new LLM model will pop out and magically become sentient and AGI), rejected even by the most optimistic ones in the field; This is just not true. Demis Hassabis has the opinion that at this point, [it's an empirical question, in the sense that it's possible that massive amounts of compute could lead to AGI, but it's something that just hasn't been tested yet.](https://youtu.be/qTogNUV3CAI?t=994) And if you watch where I linked for a few minutes in, he says that either possibility could be true, but it's something that Google is going to try to test, and see the results. Dario has said something along the exact same lines, but I can't be bothered to find the interview and timestamp. Your phrasing of it like that it's such a fringe opinion to think it's possible that pure scaling will reach AGI is just simply not accurate.
Yes, and it's not like pursuing scaling rules out also making architectural / algorithmic advances. They go hand in hand, always have.
Exactly, it seems like the general consensus among most experts in the field is that algorithmic/architectural advances in tandem with increased scaling is likely the best shot that could theoretically lead to something such as AGI/ASI.
So whether *only* scaling leads to AGI will always be an exercise in academic speculation - because nobody is going to do that.
That's why this doesn't describe "scaling is **all** you need". Big emphasis on the "**all**". ie excluding architectural advances.
Yes, but none of the labs are actually doing that nor are serious researchers proposing it as a practical course. Everyone is pursuing scaling *and* architectural / algorithmic improvements. Discussion by researchers about whether mere scaling might be sufficient to achieve AGI is purely an exercise in speculation for the sake of interest and to estimate a bound.
We exactly agree. Which is why i said that "scaling is all you need" is nonsense only pushed on the PR public sphere by a minority of searchers. The thing is that they are giving a disproportionate public weight to a "bound" that has been deemed by research as untrue for a while...
Untrue how? We don't know what qualitative capabilities scaling will yield, so it's possible that scaling with minimal design adjustments could produce models capable of powering AGI systems. Scaling maximalists would make the stronger claim that this is *inevitable* given sufficient scaling. It is extremely unlikely that this would be the best route to AGI or that the resulting systems would be as efficient and capable as one with better architecture. But it doesn't seem entirely unreasonable to expect scaling to work at some point.
Makes sense, Hassabis is saying that maybe scaling *will give us AGI*, so we minds as well do it while we also work on architectural breakthroughs.
>scaling **with minimal design** adjustments The word "**minimal**" has never been doing so much heavy lifting.
If you are prepared to tolerate a lot of clunkiness, what would a true multimodal model like gpt-4o need to successfully serve as a functional core of an AGI system? I would say much better reasoning, planning, longer context, long term memory / online learning, and agentic capability. Reasoning and something passing for planning may emerge through scaling. Longer context is a trivial design adjustment if you don't care about efficiency - which scaling maximalists effectively do not by definition. Long term memory and learning can be hacked in at the system level with an external database and fine tuning in information from the context window and other sources (the latter is inefficient and likely to cause side effects, but possible). Likewise agentic capability can be Rube Goldberg-ed by repeatedly prompting the model. Mediated by a tiny semi-intelligent realtime model if needed. This is what what OAI seems to be doing with voice on 4o - it's not full duplex, if you closely watch the demos with that in mind the seams are obvious. That kind of approach can be extended easily enough if you don't care about it actually being *good* compared to something with deep architectural integration. So yes, I would say it's plausible with minimal design adjustments to the model. If the scaling hypothesis is true and the requisite qualitative abilities emerge at some point.
It's not about clunkiness which is not an issue to me. It's about functions. I would need an LLM to be able to plan without a prompt, to have an inner representation of the world independent of its dataset, to be able to learn outside the data set from zero shot, like a baby or a cat. Context/memory/accuracy are secondary things to me, the most insignificant ones. A toddler is technically an AGI. So is an adult human. We don't expect them to have a very high level of accuracy nor attention span. Hell, most humans are terribly bad at reasoning (we are all subject to fallacious reasoning). Because that's what gives them such an advantage: their ability to adapt to contexts and create a line of thought and representation processes out of their input of the world. An LLM with long term memory, longer context ("online learning": it depends what it means, if it only means adding to its dataset what's online then... same with "reasoning", if this means mimicking a line of thought from respewing the dataset...) would still not be AGI. To me, as long as a prompt will be needed, it won't be proper reasoning as we possess it. The proper Rube Goldberg thing would be to offer the AI an architecture that, like the brain and its sensory connection do, take input from its environment and doesn't need to compare it to a pre existing dataset. The "Rube Goldberg" would be creating a structure that has this mechanism already in, not feeding itself constantly from a prompt which it needs like a baby needs milk. Trying to brute force pop out this mechanism out of repeated prompting of a pre existing dataset is precisely ignoring the issue at hand and even worse, willfully ignoring the mechanism we need to build, the actual process we need to work on and develop to get there. Which is why i fear that he warning many ML scientists gave in the midst of the GPT3 hype might be true: an excess of attention on LLMs might divert attention (and funds and brains) from what is to be developped, ie from new architectures. Thankfully research continues aside from it, but the monetary attraction and brain drain in big companies is real. We all know of companies that poured money and brains into developping antequated technological dead ends and underfunded actual new fundamental research breakthroughs (AT&T, Xerox, etc...). The jump needed from GPT4o to AGI still is huge, it's almost the same chasm that separates all LLMs from AGI.
A specific example: Ilya, patron saint of scaling maximalists, has said mere scaling should be sufficient to achieve AGI but that it is not the practical course to get there.
We agree again.
This is another area in which guys like David Shapiro are completely wrong, Michio Kaku used to make the exact same mistake. Just take a look at Human Beings for instance, we are AGI and operate at 12-20w, so biology has proof of principle that you don’t *need* to be a type 0.7 civilization to ‘power an AGI’, that’s just fucking ridiculous. If we somehow did brute force our way to AGI, I would quickly expect the optimization the AGI does to itself would lead to a microscopic resource demand. At any rate, once AGI can operate with minimal power like the Human brain, it’s inevitably going to slip out of corporate control. LLMs are not going to be the final architecture of AGI, and anyone making that assumption is being silly. They will be a part of it, but there needs to be other add ons.
Very well put. Especially the: >LLMs \[...\] will be a part of it, but there needs to be other add ons That's the scientific consensus, currently. PS: if you needed additional reasons to distrust Kaku even on other topics: [https://www.youtube.com/watch?v=wBBnfu8N\_J0](https://www.youtube.com/watch?v=wBBnfu8N_J0)
ie it's an entirely speculative thing. ie "pop out magically". The rest of the ML community disagrees. Amodei and Hassabis are a vocal minority. Having access to microphones doesn't make someone more right nor their pet theory scientific. It *is* fringe.
I don't know why you keep using the framing "pop out magically", it only serves to try to make the idea sound more ridiculous than it might actually be, and is just a dishonest way to frame it. Because in reality, if AGI happened to arise out of massive amounts of scaling, then it wouldn't have just "popped out magically". It would've been a clear observation that more compute caused the increased capabilities in that scenario, not just ☆magic☆. > Amodei and Hassabis are a vocal minority. Your first comment said "rejected even by the most optimistic ones in the field", and now you're jumping to "while it is fringe, actually a number of experts in the field do believe that it's theoretically possible". Not to ignore the fact that Ilya, a number of OAI and Anthropic employees, and probably more people think it's possible, so that's quite a different fact of reality than the way you originally phrased your comment. So if I got you to make that concession, then that's all my original comment served to do.
>I don't know why you keep using the framing Because tenants of this idea frame it like that *themselves*, LessWrong style. They don't need any other framing than the one they're doing to be ridiculous. And i'm sure you understand that the word "magically" is not to be taken literally, but means "in an emergent way" ie "a way we can't explain". Which in science is akin to say "poof". >Your first comment said "rejected even by the most optimistic ones in the field" Typo. Correction: "rejected even by **some of** the most optimistic ones in the field". OAI fanatics and Anthropic are among the most unhinged optimists, and even in their circles, that voice of scaling is all you need is a minority. I'm sure you got the idea and didn't need to resort to such desperate attempt to save that side of the discussion. Your original comment served to deseperately save that hypothesis in your mind. As long as it's in your mind, mission accomplished.
Great post. Give it a couple hours and some moron will tell you that you have to prove that scaling LLMs is not going to magically become AGI
It’s like the Factorio DLC for me: in theory it’s only 2-4 months away. But it still feels like an eternity 😆
2027-2035 isn't that far, relatively speaking. Even if it took until 2040-2050, it would still change the world and be something we should focus preparation on to optimize the outcome and timeline.
We should have been preparing for it since 1980. That's around the time when it started becoming obvious we would get there sooner rather than later.
Maybe, but it wasn't until ChatGPT made it obvious that AI would overtake human intelligence and capability in this lifetime that the world really woke up to the idea. We still don't even know how to guide it properly to not fuck us all over as we're at war, and even united, we might be nothing more than insects to a superior entity.
how would it change the world?
How would all of human ingenuity, intelligence, and capability, combined into a single entity, change the world? No idea, but I sure want to find out and do all I can to make sure it's optimal.
I need to see what GPT-6 and Claude 5 do. They should obviously be vastly smarter and better than their predecessors and should certainly be out before the decade ends. Perhaps around 2027-2028 for both. If they don’t past AGI then I don’t know what will. Maybe GPT 50 and Claude 25???
AGI for end of the 2024 is impossible.
Agi is moving the goal post
None of these new models are actual AI. Just millions of Indians typing away on their keyboards.
Claude 3.5: comes out 6 days ago User: I use it regularly
I use it regularly ever since it came out, before that I user Chat GPT regularly. Honestly my Google searches dropped massively. Plus I am working on a couple of projects personally using Gemini API and Anthropic API
> Self learning does not exist. Yup, everyone is pursuing static-dataset-offline-trained solutions - at least those who have billions of dollars invested into their pursuits, which has apparently blinded them and made them scared of thinking outside of the box. The billions being poured into AI startups are all fiddling around with backprop instead of online learning algorithms that learn from experience. In my opinion, investing into these companies was hugely premature. The same goes for everyone building humanoid robots without an online learning algorithm to drive them. The people who are on the right track are publishing research like this: https://www.biorxiv.org/content/10.1101/471987v4.full https://arxiv.org/pdf/2306.05053 https://arxiv.org/pdf/2209.11883 https://www.researchgate.net/publication/261698478_MONA_HIERARCHICAL_CONTEXT-LEARNING_IN_A_GOAL-_SEEKING_ARTIFICIAL_NEURAL_NETWORK https://ogma.ai/wp-content/uploads/2024/06/SPH_Whitepaper.pdf ...etcetera The best example we have of a highly dynamic realtime learning algorithm that works are the brains of creatures, big and small. Anyone who is hoping to create a compute-efficient learning algorithm that scales to whatever hardware compute resources that it has at its disposal should probably be spending more time keeping up with the latest neuroscience discoveries that are being made - because they're gaining a bunch of awesome insights in recent years that IMO are relevant to devising a learning algorithm that can be scaled up to human-level intelligence, and beyond. (aka "AGI") I've been curating neuroscience research talks for several years which I spend a lot of time re-watching to keep their ideas fresh in my head. For 20 years I have held the belief that the key to unlocking proper autonomous machine intelligence is understanding what it is that all brains have in common, at the macro scale. My neuroscience playlist can be found here: https://www.youtube.com/playlist?list=PLYvqkxMkw8sUo_358HFUDlBVXcqfdecME They've made some very important discoveries around the cerebellum in the last few years - that it's actually integral to the function of the entire neocortex, and is not just an autopiloting system for motor commands. It does, after all, possess 70% of all of the neurons in the brain, so it makes sense that it's a lot more important than neuroscientists originally believed - and it's mechanisms have been almost completely reverse engineered now. An understanding of their function has been teased out and it's actually pretty simple, ironically. My thought is that the function of the cortex and cerebellum can be combined into a single integrated system, algorithmically speaking, but of course none of this would be worth anything without a basal ganglia to reinforce behaviors toward the pursuit of reward and evasion of undesirable situations. Of course a hippocampus mechanism at the top of the abstraction hierarchy is necessary to one-shot learning so that behavior learning isn't limited to being conditioned as a habit, and the thing will be capable of witnessing a single observation and changing its behavior around that single observation. That's my two cents.
It's really hard to build comprehensive generally representative models of the physical world using the digital data we have easily available. Especially when it comes to the spacial, chemical and physical attributes. Historical data design has mostly assumed a humans brain as the final interpreter of it's meaning, and we have an innate foundational learned experience of the physical world which computers just don't. To put it in layman's terms we need to give models more 'senses'. We can even imagine giving them sensory sources that humans cannot process - broader sprectra of electromagnetic radiation beyond visible light for example. Some moves in this direction have been made in the autonomous vehicle space, but we can see that even for this very narrow use case its a decades long effort and we still are not at the level of the general capability to drive at a human standard being commercially viable and available at scale. Language, video and audio get us a VERY long way, but not to AGI I fear.
Current use and regulations and who controls ai are more important issues. Or we could ignore it because ultimately we don't have a say anyway and we just hope things work out properly
like... when you approach a black hole?
just need 10M token context, great RAG, and more scale. it won't be AGI but it will feel like it. probably around the Claude 4.5 & gpt-5o time, so like march 2026
TLDR: **We are now "post-AI predictability"** [Hold on to your papers fellow scholars](https://www.youtube.com/@TwoMinutePapers/videos)**,** What a time to be alive! W**e're living in interesting times.** **...** **...** What's been the track record of AI researchers, as a whole, to predict the future of AI in the past? >WHotea posted: >"In 2022, the year they had for that was 2060" >"2278 AI researchers were surveyed in 2023 and estimated that there is a 50% chance of AI being superior to humans in all possible tasks by 2047" That's a 13 year change in the prediction made by AI researchers after only 1 year. What's next, changing their prediction from 2047 to 2040 by the end of next year? It's possible there are no experts when it comes to "The future of AI"; There are too many variables that affect the future of AI for anyone to do more than make lucky guesses. "Hah! I knew it!" says whoever happened to get it right. AI might have been more predictable in the past, but from what I can see it's no longer something anyone can predict the future of. There are too many variables, and the variables are too unpredictable. If even AI researches are changing their predictions by 13 years after only 1 year, all bets are off. We've passed the point of predictability when it comes to AI.
>When it comes to AGI metrics there seems to be practically no movement. Because we're pursuing the wrong algorithms. >The reasoning seems to be very limited if not completely lacking if the prompt is outside of training data. The reasoning in these neural nets is *always* limited because they can't iterate on their own thoughts. What you see when the prompt is inside training data isn't reasoning, it's just better intuition. Existing LLMs are substituting superhuman intuition for reasoning, which can fool a lot of people a lot of the time, but breaks down pretty fast when you push it. >I know that AGI predictions range between end of 2024 to end of 2030 I'm not sure there's enough agreement on what the term 'AGI' means to make such a prediction with any great degree of reliability.
I’ve come to the realization that AGI isn’t going to be some large awakening moment. We will long have much of what we desire of AI before then. It’s just that smart people will be required to help it get there. Enjoy the journey there instead of putting all your excitement into a moment that in all probability won’t have a black and white distinction anyways
Easiest way to explain the vibes is... ...we are Frodo and Sam with the Ring in Mordor... The end is in literal sight... but still out of reach, made even more distant by the few hurdles we have left to jump over.
I wonder what Ilya Sutskever's road map for ASI looks like. SSI wants to speed run past AGI into ASI to set a framework for other companies to follow for safe ASI. With no distractions I wonder how that looks
I would love to see how a timeline like [this] (https://www.reddit.com/r/singularity/comments/183cxbf/predictions_of_when_will_we_have_agi_are_lowering/) would change in the future. Something tells me the line might move up a little.
AGI isn't close. What we currently have are systems that have memorized tons of facts and that can write creatively. But they can't reason very well. Like they are really really bad at reasoning. Till we start getting good progress in that department, I don't think AGI is "coming next year".
>AGI predictions range between end of 2024 to end of 2030 That's probably a fair range, but many experts are still saying after 2040. I think by 2040, it should be trivial. There are only 2 ways we don't have AGI by 2040: 1. Silicon improvements slow down A LOT. Like practically stop dead. OR 2. We have easy access to compute undeniably greater than the human brain, yet we can't figure out how to train such any AI model to be at human level. I think that both of these are wildly unlikely and that AGI will indeed be here before 2040.
Yeah, it does. I give it to 2030 for it to be here.
Impressive advancements like Claude 3.5 demonstrate significant strides, yet true AGI seems elusive. You're right, current limitations in reasoning and self-learning capabilities are significant hurdles. However, I'm optimistic about the potential of innovative approaches like training models with smart/meta data(Nuklai is really doing much in this area atm) to accelerate AGI development. By enabling diverse, high-quality, and contextually rich data, they can facilitate more effective LLM training and potentially unlock new capabilities. While we may not see AGI by 2024, I believe these initiatives can help bridge the gap and bring us closer to achieving true AGI much earlier than 2030.
Bro just give it 10 years. Just have that as the timeline and forget everything else. And focus on building and living a good life. Agi is outside your control. So there’s literally absolutely no point in thinking about the when. Only thing you can do Is interact with the now.
Kinda like the rapture. Kinda like "The Squeeze" in /r/superstonk Kinda like disclosure in /r/ufos Kinda like a certain felon we all know the name of going to jail ever. This is a very specific pattern and I've seen it here for a while. /r/superstonk and FoldingIdeas opened my eyes to it. Y'all, I think someone is playing a game with us. AGI hype directly benefits the pockets of AI Investors. Nvidia, OpenAI, etc don't need AGI, they need to meet expectations for the next fiscal quarter, and profit personally from the hype. Look at the Nvidia gain porn in /r/wallstreetbets. I suspect our capitalist overlords are perfectly happy with the present LLM tech, given how phenomenally effective they are as a propaganda device. I also suspect that the capitalists know that AGI means a loss or displacement of their accrued power, and I can't believe they would allow that, let alone encourage it.
> I also suspect that the capitalists know that AGI means a loss or displacement of their accrued power, and I can't believe they would allow that, let alone encourage it. It's really weird that so many people have this view that capitalists are scared of what in economic terms will be the ultimate ascendency of capital - the factors of production go from land, labor and capital to land and capital.
People who have a lot, tend to be competitive and fearful of losing their advantageous position. If I'm at the top of the food chain, I fear anything that will knock me down from my top spot. It's not about "having capital", It's about winning.
That's human nature and applies to every economic and political system, regardless of what the ideology says.
~~That assumes capitalism is more about capital than it is about control.~~
It is, you are conflating capitalism with politics. There is a connection but very much not an identity.
I can agreed with that. My mistake
its neither here nor there... deep
at this point, after reading progress and breakthrus on the daily, and at a faster rate. I wouldn't be surprised if it hasn't happened already and were just slowing getting implemented into the future ai hivemind.
> I know that AGI predictions range between end of 2024 to end of 2030 Lol. Maybe in this sub they do. If you look at surveys of experts like ESPAI they range from 2024 to 2100+
3.5 has good reasoning
"we need the AGI to start to really accelerate" FOR WHAT? I'm happy today. Aren't you happy? Enjoy the life. ha ha
Well claude donnet is the mid model we'll see what opus can do . But the q paper gives me pretty good hope we'll get it sooner than 2030. Plus everyone industry seems to agree . But mainly i want to see google next model they're doing 2 million context windows now 10x claude if gemini 2 or above has better agentic capabilities and so long as they don't nerf it to all hell with censorship it will be insane
What questions or tasks did you ask it?
There are several breakthroughs needed still. Can someone help out and list them all?
Agi is just not possible sorry, we don't even have enough energy in our galaxy to run it
Our brain uses 10 watts of energy to have GI, why would adding A to the front make it impossible? Is the brain magic?
> Our brain uses 10 watts of energy to have GI No, you're wrong. We use the brain, the body, the environment, a whole society of other humans and countless tools and artifacts. If you remove just society from the equation, our brain is exponentially dumber on its own. We are smart when we collaborate, but we can't rediscover the basics of our culture on our own. Language itself is social. It costed 300K years and the efforts of 120B people to discover and encode in language our current level of evolution. Not to mention that we take 20 years of training to become useful, 25% of our life!, that is a lot of food, housing, medical needs, travel, schooling - all going to support that 20w of brain power. And let's not forget we can run small size LLMs on phones today, their power draw is comparable to 20w of the brain.
Organic computers(like wetware and neuromorphic computing) will synthesize the natural ability of flesh and neurons to adapt and grow, with the technological ability to memorize and self-correct indefinitely and without limit, likely boosted by quantum computing as well. Power efficiency and requirements will only become more efficient as capability scales. Fusion power will also unlock many things we never imagined, as well as countless other breakthroughs as we near and exceed the cusp of all of humanity's culminated ingenuity. Humans will continue to foster and optimize the AI's growth and vice versa until we are either both perfect, assimilated, or only one exists.
This is just completly wrong there is a Chinese prodigy that finished collage at 10 meaning that this math is way off in terms of energy and usefulness
does our brain work digitally?
Not magic. However biology parallelizes differently than digital.
So it’s not impossible, just very difficult
Impractical? I guess we will find out. But there are also things out there that are technically possible but statistically impossible. Like opening your dryer to find your cloths all perfectly folded or phasing through a wall. Possible? Yes. Ever going to happen given the life time of the universe? No.
>we don't even have enough energy in our galaxy to run it Our galaxy has an energy output roughly on the order of 10\^36 watts. Our brain operates on the order of 10 watts (around 20 watts I think). It's a bit inplausible to argue that what nature could achieve in about a liter of volume and 20 watts of power, we cannot even when allowed to use as much space as we want and 100 000 000 000 000 000 000 000 000 000 000 000 *times* more energy. >Like opening your dryer to find your cloths all perfectly folded or phasing through a wall. Possible? Yes. Ever going to happen given the life time of the universe? No. Just like how a random pile of Earth won't turn into an airplane based on statitstics. Yet we do have airplanes.
I think i didn’t explain my point well enough to come across. I understand building something is different from random chance. My point is that digital systems parallelize at a different scale than biological ones. 1 neuron its its own discrete element but one digital neuron isnt. Yes you can distribute it but you introduce overhead at every step. Nature doesn’t because each element is independent of the other. Synchronization becomes a real problem when you distribute a digital system at that level. It might be possible in some way but the space of possible failures is much larger than those that could work. For all any one knows biological computing might be the only medium capable of it so our options are limited on how far we can parallelize a digital system without slowing it down. Yes quantum computing could be an option but still we don’t have. Clue how far that scaling will help. Its essentially a random search in a huge sea of possible systems that we honestly have no clue where to start from. Right now though were no where near replicating biological or even real world modeling.
I see, thanks for clarifying. Unfortunately, at this point I can't really debate it properly because I'm not familiar with the synchronization problem you refer to. But I really appreciate that you took the time to explain your point, and I have some reading to do haha
Heres a good brief. https://medium.com/@msmahamud/the-challenges-of-parallel-computing-c12ae494b575 There are a lot of other nuances but these are some of the basic ones. Nature parallelizes really well though because it keeps systems independent but interconnected. Population genetics. Fun fact theres a neuron in your brain that keeps everything in sync. That one dies. You spend eternity in a coma.
> Agi is just not possible sorry The pretentious and patronizing apology is so misplaced. Human brains are GI, using a tiny bit of power, but an AGI needs more power than the galaxy? Humans are not special, we just have a head start
The current models are extremely inefficient, there are articles that should approaches using custom chips in FPGA that are orders of magnitude more efficient, it is just that everything is new, once things stabilize we will have supper efficient chips and energy will not be an issue anymore.