I’m also a computer scientist, and it occurred to me that the principles needed to build planetary-scale inference-and-decision-making systems of this kind, blending computer science with statistics, and taking into account human utilities, were nowhere to be found in my education. And it occurred to me that the development of such principles — which will be needed not only in the medical domain but also in domains such as commerce, transportation and education — were at least as important as those of building AI systems that can dazzle us with their game-playing or sensorimotor skills.
When it comes to AGI, or even the success of machine learning in general, several researchers have high hopes for transfer learning. Demis Hassabis of DeepMind, for example, calls transfer learning “the key to general intelligence”. Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second related task. The idea is that with this precedent knowledge learned from the first task, the AI will perform better, train faster and require less labeled data than a new neural network trained from scratch on the second related task. Fundamentally, the hope it that it can help AI be more “general” and hop from task to task and domain to domain, particularly those where labeled data is less readily available (see a good overview here)