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Meta's Joe Spisak explains how AI models can train themselves by generating images, asking itself questions about them, and choosing the best answers, in order to move beyond human data and human fine-tuning, and teach itself from synthetic data

Tsarathustra

83,522 subscribers

42,390 views • 1 year ago •via X (Twitter)

Science & Technology

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10 Comments

Tsarathustra1 year ago

Source (thanks to @curiousgangsta):

Bunagaya1 year ago

doesn't feel like that much time left before recursive self improvement

Rob Leclerc1 year ago

We do this as well, but we don’t get far without testing our hypotheses against reality. AI can’t side step this epistemological constraint no more than we can.

Narasimha R N 🤗 AI - Disciple @691 year ago

AI self-training is revolutionary. Synthetic data bypasses human limits, speeding progress. However, guardrails are crucial to ensure ethical outcomes and prevent unintended consequences.

Chandan Ganwani1 year ago

Hallucinations squared and compounded exponentially. This is brilliant!

Rob Parker1 year ago

Human limitations

𝕏one - exo/acc 🧙🏽‍♂️🎨1 year ago

Self play is the correct way forward.

Shawn1 year ago

Sounds like Meta is closing up OAIs moat

Tsarathustra1 year ago

have you tried Meta AI's voice model? It's pretty good. I think it's only a 1B/3B model based on Llama 3.2

Timi Okoya1 year ago

This allows AI to explore beyond the limitations of human-labeled data, increasing its ability to learn, adapt, & improve autonomously. It opens up possibilities for faster, more scalable training, enabling AI systems to tackle complex tasks with minimal human intervention.

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