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Implications of AI: Spawn of the DAO... Non-Human VC's

Regarding the DAO and so much yet to come with VC's...


The architect of the world wide web Sir Tim Berners-Lee has talked
about some of his concerns for the internet over the coming years,
including a nightmarish scenario where artificial intelligence (AI)
could become the new 'masters of the universe' by creating and running
their own companies.
Masters of the universe is a reference to Tom Wolfe's 1987 novel The
Bonfire of the Vanities, regarding the men (and they were men) who
started racking up multi-million dollar salaries and a great deal of
influence from their finance roles on Wall Street and in London during
the computerised trading boom pre-Black Monday. Berners-Lee said, "So
when AI starts to make decisions such as who gets a mortgage, that's a
big one. Or which companies to acquire and when AI starts creating its
own companies, creating holding companies, generating new versions of
itself to run these companies. So you have survival of the fittest
going on between these AI companies until you reach the point where
you wonder if it becomes possible to understand how to ensure they are
being fair, and how do you describe to a computer what that means


Last year an experimental vehicle, developed by researchers at the
chip maker Nvidia was unlike anything demonstrated by Google, Tesla,
or General Motors. The car didn't follow a single instruction provided
by an engineer or programmer. Instead, it relied entirely on an
algorithm that had taught itself to drive by watching a human do it.
Getting a car to drive this way was an impressive feat. But it's also
a bit unsettling, since it isn't completely clear how the car makes
its decisions, argues an article on MIT Technology Review.
The mysterious mind of this vehicle points to a looming issue with
artificial intelligence. The car's underlying AI technology, known as
deep learning, has proved very powerful at solving problems in recent
years, and it has been widely deployed for tasks like image
captioning, voice recognition, and language translation. There is now
hope that the same techniques will be able to diagnose deadly
diseases, make million-dollar trading decisions, and do countless
other things to transform whole industries. But this won't happen --
or shouldn't happen -- unless we find ways of making techniques like
deep learning more understandable to their creators and accountable to
their users. Otherwise it will be hard to predict when failures might
occur -- and it's inevitable they will. That's one reason Nvidia's car
is still experimental.