content: AI already are truly impressive. To give just one example most people didn't realize the relevance of Watson crushing humanity's best at Jeopardy. They just figured it was a matter of looking up facts. Just like a human looking up facts on google, but really really fast - not a big deal. They were wrong. The thing is Jeopardy makes excessive use of word play, innuendo, and so on. You need to understand these subtleties in order to even know what you need to look up. An AI being able to do this is huge. And not exactly surprisingly Watson is now already outperforming human doctors at diagnosing cancer.. It's now partaking in the diagnostic and treatment strategy process at MD Anderson - a preeminent hospital for cancer treatment. How did it learn how to do this? It 'read' massive amounts of medical journals and information on medical trials. Millions of pages worth. And it's only getting smarter - at an incredibly rapid rate.  The reason it's not all in your face is because to date AIs are stuck in the digital world. A big part of the reason for that is the lack of vision. Things that are incredibly difficult for us, like consuming millions of pages of medical journals and applying them with perfect 'understanding' and flawless recall, is trivial for an advanced AI system. On the other hand things that are incredibly easy for us, like identifying each object in a room and understanding how they physically interact (there's a cat that must have just jumped off a table and is flying through the air towards the other table, probably to go get the fish laying on the table), is incredibly difficult for AIs. But AI has made huge strides in computer vision. AIs being able to 'digitize' the physical world means they're eventually going to be able to bring their intelligence in physical form into our world. Imagine something that's smarter than you, stronger than you, faster than you, never needs to sleep, can 'eat' sunlight, and can automatically repair itself or other of its kind that are injured beyond being able to do that. That thing will likely be created within your lifetime. It should have interesting implications on our labor based socioeconomic system.  This article in general is talking about the huge jump between deep learning and human developed systems. A deep learning system is a system that's programmed to create a solution, rather than a human programmer creating a solution himself. For instance imagine I want to write a program to detect the sound of the word "Hello" then I might focus on the frequency of the pitches and go letter by letter doing my best to compensate for the variations possible. A deep learning system on the other hand would instead be given a sample of lots of different versions of Hello and use internal algorithms to create a way it internally understands as recognizing hello. And if well designed this deep learning algorithm could be applied to any word. Given enough samples of speech it in theory could learn to 'understand' and represent any word in any language from any person. Another cool feature is that deep learning can also improve and refine itself. When a human writes an algorithm to solve a problem - that's it, it's done. What you see is what you get. Deep learning on the other hand can always improve itself further not only with code rewrites, but by giving the AI system more training and practice.