Considering what a country can do a in a decade does make sense. But it is still relatively close compared to multiple millennia evolutionary timescales.
I’m not sure what you mean here. If you want to incorporate all of the evolution before that into that multiplier of ‘1.4 billion’, so it’s thousands of times that, that doesn’t make human brains look any more efficient.
Humans produce go professionals as a side product or one mode of answering the question of life. Even quite strict go professionals do stuff like prepare meals, file taxes and watch television.
All of those are costs and disadvantages to the debit of human Go FLOPS budgets; not credits or advantages.
On that “country level” we should also consider for the model hyperparameter tuning and such.
Sure, but that is a fixed cost which is now in the past, and need never be done again. The MuZero code is written, and the hyperparameters are done. They are amortized over every year that the MuZero trained model exists, so as humans turn over at the same cost every era, the DL R&D cost approaches zero and becomes irrelevant. (Not that it was ever all that large, since the total compute budget for such research tends to be more like 10-100x the final training run, and can be <1x in scaling research where one pilots tiny models before the final training run: T5 or GPT-3 did that. So, irrelevant compared to the factors we are talking about like >>10,000x.)
“Do go really well and a passable job at stereoscopic 3d vision” is a different task than just “Do go really well”.
But not one that anyone has set, or paid for, or cares even the slightest about whether Lee Sedol can see stereoscopic 3D images.
Humans being able to do ImageNet classifications without knowing to prepare for that specific task is quite a lot more than just having the capability.
I think you are greatly overrating human knowledge of the 117 dog breeds in ImageNet, and in any case, zero-shot ImageNet is pretty good these days.
In contrast most models get an environment or data that is very pointedly shaped/helpful for their target task.
Again, a machine advantage and a human disadvantage.
Human filtering is also pretty much calibrated on human ability levels ie a good painter is a good human painter. Thus the “miss rate” based on trying to gather the cream of the cream doesn’t really tell that it would be a generally unreliable method.
I don’t know what you mean by this. The machines either do or do not pass the thresholds that varying numbers of humans fail to pass; of course you can have floor effects where the tasks are so easy that every human and machine can do it, and so there is no human penalty multiplier, but there are many tasks of considerable interest where that is obviously not the case and the human inefficiency is truly exorbitant and left out of your analysis. Chess, Go, Shogi, poetry, painting, these are all tasks that exist, and there are more, and will be more.
I’m not sure what you mean here. If you want to incorporate all of the evolution before that into that multiplier of ‘1.4 billion’, so it’s thousands of times that, that doesn’t make human brains look any more efficient.
All of those are costs and disadvantages to the debit of human Go FLOPS budgets; not credits or advantages.
Sure, but that is a fixed cost which is now in the past, and need never be done again. The MuZero code is written, and the hyperparameters are done. They are amortized over every year that the MuZero trained model exists, so as humans turn over at the same cost every era, the DL R&D cost approaches zero and becomes irrelevant. (Not that it was ever all that large, since the total compute budget for such research tends to be more like 10-100x the final training run, and can be <1x in scaling research where one pilots tiny models before the final training run: T5 or GPT-3 did that. So, irrelevant compared to the factors we are talking about like >>10,000x.)
But not one that anyone has set, or paid for, or cares even the slightest about whether Lee Sedol can see stereoscopic 3D images.
I think you are greatly overrating human knowledge of the 117 dog breeds in ImageNet, and in any case, zero-shot ImageNet is pretty good these days.
Again, a machine advantage and a human disadvantage.
I don’t know what you mean by this. The machines either do or do not pass the thresholds that varying numbers of humans fail to pass; of course you can have floor effects where the tasks are so easy that every human and machine can do it, and so there is no human penalty multiplier, but there are many tasks of considerable interest where that is obviously not the case and the human inefficiency is truly exorbitant and left out of your analysis. Chess, Go, Shogi, poetry, painting, these are all tasks that exist, and there are more, and will be more.