For example, using DeMorgan's theorem, we can build any logic circuit out of all NAND or NOR gates:
https://www.electronics-tutorials.ws/boolean/demorgan.html
https://en.wikipedia.org/wiki/NAND_logic
https://en.wikipedia.org/wiki/NOR_logic
Dall-E 2's level of associative comprehension is so far beyond the old psychology bots in the console pretending to be people, that I can't help but wonder if it's reached a level where it can make any association.
For example, I went to an AI talk about 5 years ago where the guy said that any of a dozen algorithms like K-Nearest Neighbor, K-Means Clustering, Simulated Annealing, Neural Nets, Genetic Algorithms, etc can all be adapted to any use case. They just have different strengths and weaknesses. At that time, all that really mattered was how the data was prepared.
I guess fundamentally my question is, when will AGI start to become prevalent, rather than these special-purpose tools like GPT-3 and Dall-E 2? Personally I give it less than 10 years of actual work, maybe less. I just mean that to me, Dall-E 2 is already orders of magnitude more complex than what's required to run a basic automaton to free humans from labor. So how can we adapt these AI experiments to get real work done?