Discusses the recent conquering of GO with AI. She notes how this success was more than ten years sooner than many expected.
Policy is an Alphago neural network trained to make reasonable moves based upon supervised learning of 100,000 games.
Value network is an Alphago neural network built from tens of millions of games to be able to determine what winning positions are.
The value scoring of positions was previously believed to be impossible.
Alphago used policy to reduce the breadth of search
Alphago used value network to reduce the depth of the search for good moves.
Alphago also uses fast rollouts to play a few thousand games to determine statistics for which moves are good.
The Alphago program is improving by several GO ranking levels every few months.
Julia gives more examples of the AI lawyer that helps get out of parking and other traffic tickets.
Discusses the self driving cars.
AI uses for finding disease cures.
Still it will not be a utopia.
10 million truck driving jobs will go away.
Julia discusses a post-work society.
If you cannot sell time anymore, then you only have ownership and rent or gains from ownership