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Deep Learning Is Hitting a Wall
As we successfully apply simpler, narrow versions of intelligence that benefit from faster computers and lots of data, we are not making incremental progress, but rather picking low-hanging fruit. The jump to general “common sense” is completely different, and there’s no known path from the one to the other.
Erik J. Larson • The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do
- What a modern LLM does during training is, essentially, very very quickly skim the textbook, the words just flying by , not spending much brain power on it.
- Rather, when you or I read that math textbook, we read a couple pages slowly; then have an internal monologue about the material in our heads and talk about it with a few study-buddies; read an
SITUATIONAL AWARENESS - The Decade Ahead • I. From GPT-4 to AGI: Counting the OOMs
Clune also stresses the importance of thinking about the ethics of the new technology from the start. There is a good chance that AI-designed neural networks and algorithms will be even harder to understand than today’s already opaque black-box systems.