This amount of diversity in transformers is very impressive, but what’s more impressive is that for models like GPT, scaling the models seems much more effective than engineering the models
The analogy in my mind is this: "burning natural oil/gas completely beats figuring out cleaner & more sustainable energy sources"
My point is that "more data" here simply represents the mental effort that has already been exerted in pre-AI/DL era, which we're now capitalizing on while we can. Similar to how fossil fuels represent the energy storage efforts by earlier lifeforms that we're now capitalizing on, again while we can. It's a system way out of equilibrium, progressing while it can on borrowed resources from the prior generations.
In the long run, the AI agents will be less wasteful as they reach the limits of what data or energy is available on the margins to compete within themselves and to reach their goals. It's just we haven't reached that limit yet, and the competition at this stage is on processing more data and scaling the models at any cost.
Not really. It's not simply that modern architectures are not adding additional inductive biases, they are actively throwing away the inductive bias that used to be used by everyone. For example, it was taken for granted that you should use CNNs to give you translation invariance, but apparently now visual transformers can match that performance with the same amount of compute.
Vision transformers outperform CNNs in HUGE data regimes. On small datasets, CNNs still shine.
Also, if you take a CNN with modern tricks, they can be on par with vision transformers, e.g convnext
Transformers really dominate when you scale the amount of data to infinity