Some sibling comments either are not familiar with the book or are mixing up today's AI activities with the goal of general AI. Bostrom is talking about the re

Some sibling comments either are not familiar with the book or are mixing up today's AI activities with the goal of general AI. Bostrom is talking about the results of efforts to create super intelligence / general artificial intelligence (GAI). I see AI being referred to four things these days: 1. Things that professionals would never refer to as AI. I.e. hyped gadgets and software. The word "smart" has been overused this way for a long time. 2. Today's machine learning applied to business or artistic problems. Often using third party tensor flow and gradient optimization libraries. 3. Next year's machine learning. I.e. improvements in library algorithms or creative uses of them: such as improved tensor flow, gradient optimization, novel data, layer or target definitions, etc. 4. Progress toward GAI, which on a day to day basis often looks a lot like 2 or 3, but where the leadership and budget are there for sustained progress wherever it leads. GAI isn't going to suddenly pop out of a bottle today. But now is the time to be accessing the risks because in the time frame of decades we are going to cross that line (lots of lines really). Some risks are existential so best overmanaged and overthought than under. Despite the understandable (in the short term) reaction of some that threats will always be fearmongering or hype.