I found this article a bit fluffy and lacking on technical substance. If we want to entertain the limits of AGI, we need to look at concepts like Solomonoff ind

I found this article a bit fluffy and lacking on technical substance. If we want to entertain the limits of AGI, we need to look at concepts like Solomonoff induction, which were conceived of and researched in the 1960s (and apparently mostly forgotten about since then). Given an input sequence of bits, what is the best (i.e., “oracle”) posterior distribution possible given a domain prior and the universal prior? How would a resource-limited approximation widen this posterior distribution? What are the practical limits to achieving the shortest Turing machines that reproduce the input sequence? These are interesting questions that should be looked into if we want to discuss AGI in the epistemological limit. Also, I tend to get frustrated with predictions by “AI experts” about what a future with AGI will look like. We don’t have any world experts on AGI currently, for the simple reason that it doesn’t exist yet. We have experts (or I’d prefer to say “first movers”) on particular types of machine learning based upon neural networks, but that’s about it. A true AGI would almost be unpredictable by definition, so human attempts to forecast the future in this regard are mostly sensationalism or a projection of some kind of personal agenda (cough cough Marc Andreessen).