Chinchilla
product on 6 shows · 5 statements across 4 episodes · said 57 times in 23 episodes since 2023
Latent Space 14 
the a16z Podcast 10 
No Priors 6 
TBPN 4 
20VC 4 
BG2 Pod 2
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Latent Space 14the a16z Podcast 10No Priors 620VC 4TBPN 4BG2 Pod 2
2026 2 mentions in 2 episodes 1 per episode
2025 7 mentions in 4 episodes 2 per episode
2024 14 mentions in 4 episodes 4 per episode
- Training Llama 2, 3 & 4: The Path to Open Source AGI — with Thomas Scialom of Meta AI
- Open Source AI is AI we can Trust — with Soumith Chintala of Meta AI
- Aravind Srinivas:Will Foundation Models Commoditise & Diminishing Returns in Model Performance|E1161 · 20VC with Harry Stebbings
- Ep8. AI Models, Data Scaling, Enterprise & Personal AI | BG2 with Bill Gurley & Brad Gerstner · Bg2 Pod
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2023 17 mentions in 8 episodes 2 per episode
- Safety in Numbers: Keeping AI Open
- No Priors Ep. 40 | With Arthur Mensch, CEO Mistral AI
- The Ultimate AI Roundtable: What Happens Now in AI, Why Google are Vulnerable | E1085 · 20VC with Harry Stebbings
- No Priors Ep. 33 | With Replit's CEO & Co-Founder Amjad Masad
- No Priors Ep. 27 | With Sarah Guo & Elad Gil
- No Priors Ep. 17 | With Karan Singhal
- Emad Mostaque: These 5 Companies Will Win the AI War; Why We Need National Data Sets | E1015 · 20VC with Harry Stebbings
- No Priors Ep. 3 | With Stability AI’s Emad Mostaque
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5 statements about Chinchilla, every show
Morcos: Kaplan and Chinchilla scaling laws incorrectly assume all data is equal
“And even if you go and you look at the scaling laws work from Kaplan and Chinchilla and all these other things, they all assume IID data which is insane. We know that all data are not created equal, that garbage in garbage out is like the oldest adage in compu…”
Scialom: Llama 1 and 2 flagship size was chosen to reproduce Chinchilla
“Lama two, maybe I would say it's like Lama one. We had a flagship model, which was seven TB. It's also because the project was taking some routes to reproducing a chinchilla, which was a seven TB.”
Scialom: Overtrain models beyond Chinchilla optimal to minimize inference costs
“And so, to be compute efficient at inference time, it's much better to train it much longer training time, even if it's an effort, an additional effort, than to have a bigger model. That's what I call, like, I refer to the chinchilla trap, Not that Chinchilla …”
Mensch: AI industry stopped publishing open research after GPT-3
“And all of a sudden in with GPT-free, this tide kind of reversed and companies started to be more opaque about what they were doing because they realized there was actually a very big market. And all of a sudden in 20, 22, on the important aspects of AI and on…”