1 link tagged with all of: deep-learning + ai-research + recursive-self-improvement + scaling-laws
Click any tag below to further narrow down your results
Links
Three AI researchers from frontier labs discuss why transformative AI might fail to emerge despite current progress—exploring technical bottlenecks like the sim-to-real gap, the possibility that current methods hit diminishing returns, and whether scaling alone can discover the next paradigm shift needed for AGI.
- Current AI progress follows repeating cycles where new models seem revolutionary but reveal limitations within months, potentially continuing indefinitely without reaching true generalization or self-improvement loops.
- The transformer + RL paradigm may require fundamental discontinuities to advance further, similar to how scaling laws in deep learning repeatedly hit walls that required new innovations (pre-training, then RL) to overcome—and future breakthroughs might not be discoverable by scaling current methods alone.
- Even if AI systems become better than humans at research, the jump to explosive recursive self-improvement isn't guaranteed; there could be persistent gaps between what works in simulation and real-world deployment that prevent the takeoff scenario.