GAI is not limited by computing power, but theory. Yes we will be able to make better inference by feeding more data into our models, supervised learning will b

GAI is not limited by computing power, but theory. Yes we will be able to make better inference by feeding more data into our models, supervised learning will be able to work with thousands of parameters, etc. This will lead to all sorts of new discoveries in many fields, including biochemistry, astrophysics, neurology, etc. It will also bring us a bunch of new fun toys to play with including better and more capable robots, and it will also lead to better engineered social systems (including traffic engineering; but also public transit, and—yes—self driving vehicles—but we already have self driving trains, so it is not as big of a deal as you think it is). However this misses my point about AGI. It is not about computing capabilities but theory. AGI will have to evaluate situations that has never arisen before without having data that directly directs it. I don’t see how statistical inference can do that, and as such we will need an entirely new methods of training it. Like I said, we haven’t even conceived how an AGI technology works, there are no algorithms which will work in theory even with unlimited computing power. Contrast this, for example, with quantum computing, which has a bunch of algorithms demonstrating it’s capabilities. With AGI all we have is “perhaps our cars become better at driving them selves”. I’m not buying it.