- 2010 isn’t 2012 (and I don’t think that it’s true Elon even said that at the time - at best he may have said 2016?) - FSD is one example, but the improvement

- 2010 isn’t 2012 (and I don’t think that it’s true Elon even said that at the time - at best he may have said 2016?) - FSD is one example, but the improvement of computer vision since 2015 has been massive and deep learning approaches to general problem solving too. This wasn’t something people were predicting in 2010. - The deep blue and stock fish style approaches vs. the alpha go or alpha zero approaches are categorically different - the latter being a lot more interesting and closer to general learning vs. the older approach which is more like brute force. - GOFAI was a bad approach and the optimism in the 60s was wrong. Today’s looks more promising. Being wrong in the 60s doesn’t necessarily mean people are wrong now. It’s hard to know: https://intelligence.org/2017/10/13/fire-alarm/ https://intelligence.org/2017/10/13/fire-alarm/ For the AGI bit I’d recommend reading some of Eliezer Yudkowsky’s writing or Bostrom’s book (though I find Bostrom’s writing style tedious). There’s a lot of good writing about take offs and AGI/goal alignment that’s worth reading to get a base level understanding of the concepts people have thought through. AGI doesn’t need to be human like to be dangerous - it can be good at general problem solving with poorly aligned goals and just act much faster. Brains exist everywhere in nature, simpler than human brains. A lot of that computation in training could be an analog of the genetic “pre-training” of evolution for humans that gets our baseline which could be one reasons humans don’t seem to require so much. There was a massive amount of “computation” over time via natural selection to get to our current state.