Yeah, I think we should expect much more powerful open source AIs than we have now. I’ve been working on a blog post about this, maybe I’ll get it out soon. Here are what seem like the dominant arguments to me:
Scaling curves show strongly diminishing returns to $ spend: A $100m model might not be that far behind a $1b model, performance wise.
There are numerous (maybe 7) actors in the open source world who are at least moderately competent and want to open source powerful models. There is a niche in the market for powerful open source models, and they hurt your closed-source competitors.
I expect there is still tons of low-hanging fruit available in LLM capabilities land. You could call this “algorithmic progress” if you want. This will decrease the compute cost necessary to get a given level of performance, thus raising the AI capability level accessible to less-resourced open-source AI projects. [edit: but not exclusively open-source projects (this will benefit closed developers too). This argument is about the absolute level of capabilities available to the public, not about the gap between open and closed source.]
I expect there is still tons of low-hanging fruit available in LLM capabilities land. You could call this “algorithmic progress” if you want. This will decrease the compute cost necessary to get a given level of performance, thus raising the AI capability level accessible to less-resourced open-source AI projects.
Don’t you expect many of those improvements to remain closed-source from here on out, benefitting the teams that developed them at great (average) expense? And even the ones that are published freely will benefit the leaders just as much as their open-source chasers.
Um, looking at the scaling curves and seeing diminishing returns? I think this pattern is very clear for metrics like general text prediction (cross-entropy loss on large texts), less clear for standard capability benchmarks, and to-be-determined for complex tasks which may be economically valuable.
Capability benchmarks: see epoch post, the ~4th figure here
Complex tasks: See GDM dangerous capability evals (Fig 9, which indicates Ultra is not much better than Pro, despite likely being trained on >5x the compute, though training details not public)
To be clear, I’m not saying that a $100m model will be very close to a $1b model. I’m saying that the trends indicate they will be much closer than you would think if you only thought about how big a 10x difference in training compute is, without being aware of the empirical trends of diminishing returns. The empirical trends indicate this will be a relatively small difference, but we don’t have nearly enough data for economically valuable tasks / complex tasks to be confident about this.
Diminishing returns in loss are not diminishing returns in capabilities. And benchmarks tend to saturate, so diminishing returns are baked in if you look at those.
I am not saying that there aren’t diminishing returns to scale, but I just haven’t seen anything definitive yet.
I agree with the premise, but not the conclusion of your last point. Any OpenSource development, that will significantly lower the resource requirements can also be used by closed models to just increased their model/training size for the same cost, thus keeping the gap.
Yeah, these developments benefit close-sourced actors too. I think my wording was not precise, and I’ll edit it. This argument about algorithmic improvement is an argument that we will have powerful open source models (and powerful closed-source models), not that the gap between these will necessarily shrink. I think both the gap and the absolute level of capabilities which are open-source are important facts to be modeling. And this argument is mainly about the latter.
Unless there is a ‘peak-capabilities wall’ that gets hit by current architectures that doesn’t get overcome by the combined effects of the compute-efficiency-improving algorithmic improvements. In that case, the gap would close because any big companies that tried to get ahead by just naively increasing compute and having just a few hidden algorithmic advantages would be unable to get very far ahead because of the ‘peak-capabilities wall’. It would get cheaper to get to the wall, but once there, extra money/compute/data would be wasted. Thus, a shrinking-gap world.
I’m not sure if there will be a ‘peak-capabilities wall’ in this way, or if the algorithmic advancements will be creative enough to get around it. The shape of the future in this regard seems highly uncertain to me. I do think it’s theoretically possible to get substantial improvements in peak capabilities and also in training/inference efficiencies. Will such improvements keep arriving relatively gradually as they have been? Will there be a sudden glut at some point when the models hit a threshold where they can be used to seek and find algorithmic improvements? Very unclear.
Yeah, I think we should expect much more powerful open source AIs than we have now. I’ve been working on a blog post about this, maybe I’ll get it out soon. Here are what seem like the dominant arguments to me:
Scaling curves show strongly diminishing returns to $ spend: A $100m model might not be that far behind a $1b model, performance wise.
There are numerous (maybe 7) actors in the open source world who are at least moderately competent and want to open source powerful models. There is a niche in the market for powerful open source models, and they hurt your closed-source competitors.
I expect there is still tons of low-hanging fruit available in LLM capabilities land. You could call this “algorithmic progress” if you want. This will decrease the compute cost necessary to get a given level of performance, thus raising the AI capability level accessible to less-resourced open-source AI projects. [edit: but not exclusively open-source projects (this will benefit closed developers too). This argument is about the absolute level of capabilities available to the public, not about the gap between open and closed source.]
What’s your argument for that?
Don’t you expect many of those improvements to remain closed-source from here on out, benefitting the teams that developed them at great (average) expense? And even the ones that are published freely will benefit the leaders just as much as their open-source chasers.
Um, looking at the scaling curves and seeing diminishing returns? I think this pattern is very clear for metrics like general text prediction (cross-entropy loss on large texts), less clear for standard capability benchmarks, and to-be-determined for complex tasks which may be economically valuable.
General text prediction: see Chinchilla, Fig 1 of the GPT-4 technical report
Capability benchmarks: see epoch post, the ~4th figure here
Complex tasks: See GDM dangerous capability evals (Fig 9, which indicates Ultra is not much better than Pro, despite likely being trained on >5x the compute, though training details not public)
To be clear, I’m not saying that a $100m model will be very close to a $1b model. I’m saying that the trends indicate they will be much closer than you would think if you only thought about how big a 10x difference in training compute is, without being aware of the empirical trends of diminishing returns. The empirical trends indicate this will be a relatively small difference, but we don’t have nearly enough data for economically valuable tasks / complex tasks to be confident about this.
Diminishing returns in loss are not diminishing returns in capabilities. And benchmarks tend to saturate, so diminishing returns are baked in if you look at those.
I am not saying that there aren’t diminishing returns to scale, but I just haven’t seen anything definitive yet.
I agree with the premise, but not the conclusion of your last point. Any OpenSource development, that will significantly lower the resource requirements can also be used by closed models to just increased their model/training size for the same cost, thus keeping the gap.
Yeah, these developments benefit close-sourced actors too. I think my wording was not precise, and I’ll edit it. This argument about algorithmic improvement is an argument that we will have powerful open source models (and powerful closed-source models), not that the gap between these will necessarily shrink. I think both the gap and the absolute level of capabilities which are open-source are important facts to be modeling. And this argument is mainly about the latter.
Unless there is a ‘peak-capabilities wall’ that gets hit by current architectures that doesn’t get overcome by the combined effects of the compute-efficiency-improving algorithmic improvements. In that case, the gap would close because any big companies that tried to get ahead by just naively increasing compute and having just a few hidden algorithmic advantages would be unable to get very far ahead because of the ‘peak-capabilities wall’. It would get cheaper to get to the wall, but once there, extra money/compute/data would be wasted. Thus, a shrinking-gap world.
I’m not sure if there will be a ‘peak-capabilities wall’ in this way, or if the algorithmic advancements will be creative enough to get around it. The shape of the future in this regard seems highly uncertain to me. I do think it’s theoretically possible to get substantial improvements in peak capabilities and also in training/inference efficiencies. Will such improvements keep arriving relatively gradually as they have been? Will there be a sudden glut at some point when the models hit a threshold where they can be used to seek and find algorithmic improvements? Very unclear.