What has led you to the conclusion there is too little diversity in the AI field?
We have seen enormous advances in the ability of AI, and in particular deep learning, to predict, classify, and generate what we might think of as the surface features of the world. These successes are marked by two fundamental features that don't always hold: having a measurement of what matters, and being able to define what counts as success. Deep learning can do amazing things, but what worries me is that it crowds everything else out.
We have struggled to come up with AI systems that can discover the underlying structure of the world, things that show up in the data but are not defined by them. So one reason that we are struggling with developing more trustworthy and value-centered AI is because trust and values fundamentally are not things that we know how to give numerical expressions for.
Can you give an example?
It is difficult to figure out what counts as success for a self-driving car. Sure, we want to drive safely, but what counts as driving safely is very context-dependent. It depends on social norms, it depends on the weather, it depends on suddenly occurring situations on the road. As soon as there is an unusual context, self-driving cars can't reason their way out like a human driver can.
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