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"There's a great phrase, written in the '70s: "the definition of today's AI is a machine that can make a perfect chess move while the room is on fire." It really speaks to the limitations of AI. In the next wave of AI research, if we want to make more helpful and useful machines, we've got to bring back the contextual understanding. We've got to bring knowledge abstraction and reasoning. These are all the most important steps."

This could be an important step for preventing revenge porn. But it also requires a lot of trust in a platform that has not done much to inspire confidence recently. It also serves as a reminder that human intervention is still very much required for controlling how content spreads online.

Removing bias or statistical facts about the world will make the machine model less accurate. But you can't easily remove bias, so you have to learn how to work with it. We are self-aware, we can decide to do the right thing instead of the prejudiced option. But machines don't have self awareness. An expert human might be able to aid in [the AIs'] decision-making process so the outcome isn't stereotyped or prejudiced for a given task.

"All the other stuff you hear about-machine learning, deep learning, neural networks, what have you-are much more precise names for the various scientific, mathematical, and engineering methods that people employ within the field of AI. George Orwell was on to something when he famously wrote that "the slovenliness of our language makes it easier for us to have foolish thoughts." Using the same science-fictional shorthand to describe anything from self-driving cars to slightly better ad targeting surely inhibits basic comprehension. But its potential for seeding both magical thinking and abject confusion about real economic, social, and political changes also seems bottomless. If the experts don't really know what they talk about when they talk about AI, is it any wonder that you and I don't, either?"

Fernando Pereira (who may have been quoting someone else) told me 20 years ago about the "explorers" and the "settlers".  The explorers see the terrain first (without rigor) and the settlers clean it up (with rigor).  Consider calculus or Fourier analysis.

"while using algorithms to streamline the hiring process is clearly attractive to any firm that wants to save on costs and time spent recruiting, as we have written before, AI can often be biased (see "How to Root Out Hidden Biases in AI"). This isn't the fault of the software, as such-it's only as good as the data it is fed. And we humans have an unfortunate habit of being unable to shake habits like gender and racial bias. If we're to avoid such pitfalls in a world in which AI mediates the hiring process, then, we're going to have to keep a sharp eye out to make sure it does a better job than we have of playing fair."