“…scale of seven to eight billion … is a sweet spot where you can start to do good interpretability research… you can do zero-shot prompting and get interesting behavior.”

Eric Bigelow, Goodfire · "The Cognitive Revolution" · How Agents Decide: Goodfire's Eric Bigelow on Critical Tokens, Phase Shifts, & In-Context Learning

Play from 49:29
49:29

Audio streams from the publisher’s own feed. Verbatim does not host or copy it.

What was described

Choose model scale of 7-8 billion parameters as a sweet spot for interpretability research, enabling zero-shot prompting and human-like behavior without training a new model

7-8 billion parameters

Said nearby on the same subject

“A 26 billion parameter models can already orchestrate full replication chains ... able to create small $9 billion parameters models that established interface on eight of the nine hardware types”
♫
Said on Leveraging AI
Why it worked 26 B parameters; 9 B-parameter variants; 8/9 hardware types Share this moment
“Haiku's price jumps 5x if you are sending really large prompts. So for those, you may want to choose a different model.”
♫
Said on Leveraging AI
Pricing 5× price increase over 100k tokens Share this moment
“We will license core ai models from really deep resourced company and then train those models on client data ... without having to hire 300 people”
♫
Said on InsTech - insurance & innovation
Business model Share this moment
“Mistral Large 4, aka LeChunk, a trillion parameter model trained in two months on 3,800 GPUs in Mistral's own data centers”
♫
Said on The MAD Podcast: How AI Gets Built - with Matt Turck
How they work two months; 3,800 GPUs Share this moment

Moments like this, every Monday

The strongest new moments on your topics, in the speakers' own words. Free for three topics.

No card. No password. One-click unsubscribe.

Track a competitor?

Pro watches up to 25 names or phrases and emails you the morning after they are said.

See Pro, $29 a month

Verbatim quotes a short extract and links to the episode; the episode belongs to its publisher. Remove a show.