The standard reading is : "hardware wins". But there is a second part. "The second general point to be learned from the bitter lesson is that the actual conte

The standard reading is : "hardware wins". But there is a second part. "The second general point to be learned from the bitter lesson is that the actual contents of minds are tremendously, irredeemably complex; we should stop trying to find simple ways to think about the contents of minds, such as simple ways to think about space, objects, multiple agents, or symmetries. All these are part of the arbitrary, intrinsically-complex, outside world. They are not what should be built in, as their complexity is endless; instead we should build in only the meta-methods that can find and capture this arbitrary complexity. Essential to these methods is that they can find good approximations, but the search for them should be by our methods, not by us. We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done." This is proving quite tendentious at the moment. Do we want agents that discover like we can? The clattering impact of rogue models, even when they are being used just to make pretty pictures - as opposed to engineering diagrams, patient care plans, fraud investigations.... is making me, and many others wonder. In fact some of the projects that are supporting the discovery of precision like alphafold might be the most promising?