Silicon Valley has become increasingly concerned that advancements in AI are already slowing. One indication is a lack of progress between new models released.
Generative artificial intelligence has developed so quickly in the past two years that massive breakthroughs seemed more a question of when rather than if. But in recent weeks, Silicon Valley has become increasingly concerned that advancements are slowing.
If progress is plateauing, it would call into question a core assumption that Silicon Valley has treated as religion: scaling laws. The idea is that adding more computing power and more data guarantees better models to an infinite degree. But those recent developments suggest they may be more theory than law.
"AI is an industry which is garbage in, garbage out," Wang said. "So if you feed into these models a lot of AI gobbledygook, then the models are just going to spit out more AI gobbledygook." "Foundation model pre-training scaling is intact and it's continuing," Nvidia CEO Jensen Huang said on the chipmaker'scall. "As you know, this is an empirical law, not a fundamental physical law. But the evidence is that it continues to scale."
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