When to assume neural networks can solve a problem - LessWrong 2.0

Note: the original article has been split into two since I think the two points were only vaguely related, I will leave it as is here, since I'd rather not re-post stuff and I think the audience on LW might see the "link" between the two separate ideas presented here. Let's begin with a gentle introduction in to the field of AI risk - possibly unrelated to the broader topic, but it's what motivated me to write about the matter; it's also a worthwhile perspective to start the discussion from. I hope for this article to be part musing on what we should assume machine learning can do and why we'd make those assumptions, part reference guide for "when not to be amazed that a neural network can do something". I've often had a bone to pick against "AI risk" or, as I've referred to it, "AI alarmism". When evaluating AI risk, there are multiple views on the location of the threat and the perceived warning signs. I would call one of these viewpoints the "Bostromian position", which seems to be mainly promoted by MIRI, philosophers like Nick Bostrom and on forums such as AI Alignment.