It depends... on what you measure outrageousness in... If what you mean is that something is more extravagant if it... involves somehow having more p… - Max Tegmark

There are more things in heaven and earth, Horatio, than are dreamt of in your philosophy. — William Shakespeare, Hamlet, act 1, scene 5

It’s easy for you to tell what it’s a photo of, but to program a function that inputs nothing but the colors of all the pixels of an image and outputs an accurate caption such as “A group of young people playing a game of frisbee” had eluded all the world’s AI researchers for decades. Yet a team at Google led by Ilya Sutskever did precisely that in 2014. Input a different set of pixel colors, and it replies “A herd of elephants walking across a dry grass field,” again correctly. How did they do it? Deep Blue–style, by programming handcrafted algorithms for detecting frisbees, faces and the like? No, by creating a relatively simple neural network with no knowledge whatsoever about the physical world or its contents, and then letting it learn by exposing it to massive amounts of data. AI visionary Jeff Hawkins wrote in 2004 that “no computer can…see as well as a mouse,” but those days are now long gone.

I’m encouraging mine to go into professions that machines are currently bad at, and therefore seem unlikely to get automated in the near future. Recent forecasts for when various jobs will get taken over by machines identify several useful questions to ask about a career before deciding to educate oneself for it. 48 For example: • Does it require interacting with people and using social intelligence? • Does it involve creativity and coming up with clever solutions? • Does it require working in an unpredictable environment?