The Download: shaking up neural networks, and the rise of weight-loss drugs

Networks programmed directly into computer chip hardware can identify images faster, and use much less energy, than the traditional neural networks that underpin most modern AI systems. That's according to work presented at a leading machine learning conference in Vancouver last week. Neural networks, from GPT-4 to Stable Diffusion, are built by wiring together perceptrons, which are highly simplified simulations of the neurons in our brains. In very large numbers, perceptrons are powerful, but they also consume enormous volumes of energy. Part of the trouble is that perceptrons are just software abstractions--running a perceptron network on a GPU requires translating that network into the language of hardware, which takes time and energy.