Machine learning applied to SAFE

I recently read an article about George Hotz who built his own self driving car in less than a month using machine learning as described in this excerpt:

The second advance is deep learning, an AI technology that has taken off over the past few years. It allows researchers to assign a task to computers and then sit back as the machines in essence teach themselves how to accomplish and finally master the job. In the past, for example, it was thought that the only way for a computer to identify a chair in a photo would be to create a really precise definition of a chair—you would tell the computer to look for something with four legs, a flat seat, and so on. In recent years, though, computers have become much more powerful, while memory has become cheap and plentiful. This has paved the way for more of a brute-force technique, in which researchers can bombard computers with a flood of information and let the systems make sense of the data. “You show a computer 1 million images with chairs and 1 million without them,” Hotz says. “Eventually, the computer is able to describe a chair in a way so much better than a human ever could.”

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Is this something we can use to create our get/put economy? Optimal safecoin output? Any number of things that need to be optimized with dynamic algorithms for the full functioning system to work as intended.

I’d love to hear your thoughts.


I can foresee this, and David has (I speculate) alluded to it in comments about the update mechanism.

I certainly ponder using generic algorithms and other low level computational selection techniques to optimise the behaviour of the network (eg vaults). These are still hard-ish problems, but they’re getting easier fast, and with the computation power available to SAFEnetwork, this and many other things become possible.