I’m not sure I buy ’2013 algorithms are literally enough’, but it does seem very likely to me that in practice you get AGI very quickly (<2 years) if you give out GPUs which have (say) 10^50 FLOPS. (These GPUS are physically impossible, but I’m just supposing this to make the hypothetical easier. In particular, 2013 algorithms don’t parallelize very well and I’m just supposing this away.)
And, I think 2023 algorithms are literally enough with this amount of FLOP (perhaps with 90% probability).
For a concrete story of how this could happen, let’s imagine training a model with around 10^50 FLOP to predict all human data ever produced (say represented as uncompressed bytes and doing next token prediction) and simultaneously training with RL to play every game ever. We’ll use the largest model we can get with this flop budget, probably well over 10^25 parameters. Then, you RL on various tasks, prompt the AI, or finetune on some data (as needed).
This can be done with either 2013 or 2023 algorithms. I’m not sure if it’s enough with 2013 algorithms (in particular, I’d be worried that the AI would be extremely smart but the elicitation technology wasn’t there to get the AI to do anything useful). I’d put success with 2013 algos and this exact plan at 50%. It seems likely enough with 2023 algorithms (perhaps 80% chance of success).
In 2013 this would look like training an LSTM. Deep RL was barely developed, but did exist.
In 2023 this looks similar to GPT4 but scaled way up and trained on all source of data and trained to play games etc.
Let me replay my understanding to you, to see if I understand. You are predicting that...
IF:
we gathered all files stored on hard drives
...decompressed them into streams of bytes
...trained a monstrous model to predict the next chunk in each stream
...and also trained it to play every winnable computer game ever made
THEN:
You are 50% confident we’d get AGI* using 2013 algos
You are 80% confident we’d get AGI* using 2023 algos
WHERE:
*AGI means AI that is general; i.e., able to generalize to all sorts of data way outside its training distribution. Meaning:
It avoids overfitting on the data despite its massive parameter count. E.g., not just memorizing every file or brute forcing all the exploitable speedrunning bugs in a game that don’t generalize to real-world understanding.
It can learn skills and tasks that are barely represented in the computer dataset but that real-life humans are nonetheless able to quickly understand and learn due to their general world models
It can made to develop planning, reasoning, and strategy skills not well represented by next-token prediction (e.g., it would learn to how write a draft, reflect on it, and edit it, even though it’s never been trained to do that and has only been optimized to append single tokens in sequence)
It simultaneously avoids underfitting due to any regularization techniques used to avoid the above overfitting problems
ASSUMING:
We don’t train on data not stored on computers
We don’t train on non-computer games (but not a big crux if you want to posit high fidelity basketball simulations, for example)
We don’t train on games without win conditions (but not a big crux, as most have them)
Is this a correct restatement of your prediction?
And are your confidence levels for this resulting in AGI on the first try? Within ten tries? Within a year of trial and error? Within a decade of trial and error?
(Rounding to the nearest tenth of a percent, I personally am 0.0% confident we’d get AGI on our first try with a system like this, even with 10^50 FLOPS.)
This seems like a pretty good description of this prediction.
Your description misses needing a finishing step of doing some RL, prompting, and generally finetuning on the task of interest (similar to GPT4). But this isn’t doing much of the work, so it’s not a big deal. Additionally, this sort of finishing step wasn’t really developed in 2013, so it seems less applicable to that version.
I’m also assuming some iteration on hyperparameters and data manipulation etc. in keeping with the techniques used in the respective time periods. So, ‘first try’ isn’t doing that much work here because you’ll be iterating a bit in the same way that people generally iterate a bit (but you won’t be doing novel research).
My probabilities are for the ‘first shot’ but after you do some preliminary experiments to verify hyper-params etc. And with some iteration on the finetuning. There might be a non-trivial amount of work on the finetuning step also, I don’t have a strong view here.
It’s worth noting that I think that GPT5 (with finetuning and scaffolding, etc.) is perhaps around 2% likely to be AGI. Of course, you’d need serious robotic infrastructure and much larger pool of GPUs to automate all labor.
My general view is ‘if the compute is there, the AGI will come’. I’m going out on more of a limb with this exact plan and I’m much less confident in the plan than in this general principle.
Here are some examples reasons why I think my high probabilities are plausible:
The training proposal I gave is pretty close to how models like GPT4 are trained. These models are pretty general and are quite strategic etc. Adding more FLOP makes a pretty big qualitative difference.
It doesn’t seem to me like you have to generalize very far for this to succeed. I think existing data trains you to do basically everything humans can do. (See GPT4 and prompting)
Even if this proposal is massively inefficient, we’re throwing an absurd amount of FLOP at it.
It seems like the story for why humans are intelligent looks reasonably similar to this story: have big, highly functional brains, learn to predict what you see, train to achieve various goals, generalize far. Perhaps you think humans intelligence is very unlikely ex-ante (<0.04% likely).
I’m not sure I buy ’2013 algorithms are literally enough’, but it does seem very likely to me that in practice you get AGI very quickly (<2 years) if you give out GPUs which have (say) 10^50 FLOPS. (These GPUS are physically impossible, but I’m just supposing this to make the hypothetical easier. In particular, 2013 algorithms don’t parallelize very well and I’m just supposing this away.)
And, I think 2023 algorithms are literally enough with this amount of FLOP (perhaps with 90% probability).
For a concrete story of how this could happen, let’s imagine training a model with around 10^50 FLOP to predict all human data ever produced (say represented as uncompressed bytes and doing next token prediction) and simultaneously training with RL to play every game ever. We’ll use the largest model we can get with this flop budget, probably well over 10^25 parameters. Then, you RL on various tasks, prompt the AI, or finetune on some data (as needed).
This can be done with either 2013 or 2023 algorithms. I’m not sure if it’s enough with 2013 algorithms (in particular, I’d be worried that the AI would be extremely smart but the elicitation technology wasn’t there to get the AI to do anything useful). I’d put success with 2013 algos and this exact plan at 50%. It seems likely enough with 2023 algorithms (perhaps 80% chance of success).
In 2013 this would look like training an LSTM. Deep RL was barely developed, but did exist.
In 2023 this looks similar to GPT4 but scaled way up and trained on all source of data and trained to play games etc.
Let me replay my understanding to you, to see if I understand. You are predicting that...
IF:
we gathered all files stored on hard drives
...decompressed them into streams of bytes
...trained a monstrous model to predict the next chunk in each stream
...and also trained it to play every winnable computer game ever made
THEN:
You are 50% confident we’d get AGI* using 2013 algos
You are 80% confident we’d get AGI* using 2023 algos
WHERE:
*AGI means AI that is general; i.e., able to generalize to all sorts of data way outside its training distribution. Meaning:
It avoids overfitting on the data despite its massive parameter count. E.g., not just memorizing every file or brute forcing all the exploitable speedrunning bugs in a game that don’t generalize to real-world understanding.
It can learn skills and tasks that are barely represented in the computer dataset but that real-life humans are nonetheless able to quickly understand and learn due to their general world models
It can made to develop planning, reasoning, and strategy skills not well represented by next-token prediction (e.g., it would learn to how write a draft, reflect on it, and edit it, even though it’s never been trained to do that and has only been optimized to append single tokens in sequence)
It simultaneously avoids underfitting due to any regularization techniques used to avoid the above overfitting problems
ASSUMING:
We don’t train on data not stored on computers
We don’t train on non-computer games (but not a big crux if you want to posit high fidelity basketball simulations, for example)
We don’t train on games without win conditions (but not a big crux, as most have them)
Is this a correct restatement of your prediction?
And are your confidence levels for this resulting in AGI on the first try? Within ten tries? Within a year of trial and error? Within a decade of trial and error?
(Rounding to the nearest tenth of a percent, I personally am 0.0% confident we’d get AGI on our first try with a system like this, even with 10^50 FLOPS.)
This seems like a pretty good description of this prediction.
Your description misses needing a finishing step of doing some RL, prompting, and generally finetuning on the task of interest (similar to GPT4). But this isn’t doing much of the work, so it’s not a big deal. Additionally, this sort of finishing step wasn’t really developed in 2013, so it seems less applicable to that version.
I’m also assuming some iteration on hyperparameters and data manipulation etc. in keeping with the techniques used in the respective time periods. So, ‘first try’ isn’t doing that much work here because you’ll be iterating a bit in the same way that people generally iterate a bit (but you won’t be doing novel research).
My probabilities are for the ‘first shot’ but after you do some preliminary experiments to verify hyper-params etc. And with some iteration on the finetuning. There might be a non-trivial amount of work on the finetuning step also, I don’t have a strong view here.
It’s worth noting that I think that GPT5 (with finetuning and scaffolding, etc.) is perhaps around 2% likely to be AGI. Of course, you’d need serious robotic infrastructure and much larger pool of GPUs to automate all labor.
My general view is ‘if the compute is there, the AGI will come’. I’m going out on more of a limb with this exact plan and I’m much less confident in the plan than in this general principle.
Here are some examples reasons why I think my high probabilities are plausible:
The training proposal I gave is pretty close to how models like GPT4 are trained. These models are pretty general and are quite strategic etc. Adding more FLOP makes a pretty big qualitative difference.
It doesn’t seem to me like you have to generalize very far for this to succeed. I think existing data trains you to do basically everything humans can do. (See GPT4 and prompting)
Even if this proposal is massively inefficient, we’re throwing an absurd amount of FLOP at it.
It seems like the story for why humans are intelligent looks reasonably similar to this story: have big, highly functional brains, learn to predict what you see, train to achieve various goals, generalize far. Perhaps you think humans intelligence is very unlikely ex-ante (<0.04% likely).