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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 Aufrufe • vor 1 Jahr •via X (Twitter)

Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

10 Kommentare

Profilbild von Tsarathustra

Tsarathustravor 1 Jahr

Source (thanks to @curiousgangsta):

Profilbild von Bunagaya

Bunagayavor 1 Jahr

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

Profilbild von Rob Leclerc

Rob Leclercvor 1 Jahr

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.

Profilbild von Narasimha R N 🤗 AI - Disciple @69

Narasimha R N 🤗 AI - Disciple @69vor 1 Jahr

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

Profilbild von Chandan Ganwani

Chandan Ganwanivor 1 Jahr

Hallucinations squared and compounded exponentially. This is brilliant!

Profilbild von Rob Parker

Rob Parkervor 1 Jahr

Human limitations

Profilbild von 𝕏one - exo/acc 🧙🏽‍♂️🎨

𝕏one - exo/acc 🧙🏽‍♂️🎨vor 1 Jahr

Self play is the correct way forward.

Profilbild von Shawn

Shawnvor 1 Jahr

Sounds like Meta is closing up OAIs moat

Profilbild von Tsarathustra

Tsarathustravor 1 Jahr

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

Profilbild von Timi Okoya

Timi Okoyavor 1 Jahr

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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