Why could the same AI rebuild a 16,000-line program without seeing its code, yet fail to customize a library for METR? The answer shapes what you hand it.
Watch now (21 mins) | Are we developing a more general intelligence, or getting much better at producing extraordinary abilities that transfer unevenly?
TL;DR
- We tend to assume AI progress in one difficult area signifies deeper, general intelligence that should help in others.
- OpenAI's release of mathematical research manuscripts from an internal model highlights narrow superintelligence – extraordinary performance in specific tasks.
- It's possible AI can improve dramatically in many things, but its most exceptional abilities may advance much faster, creating unevenness.
- This uneven progress could result in astonishing AI successes alongside failures that are hard to explain, making generalized 'smartness' less predictive.
- Training feedback concentration in areas with cheap and plentiful data may lead to disproportionately faster improvements in those specific abilities.