“You can take some sort of document and back translate it into, you know, question and answer pairs, for example. That's a very concrete example.”

Unsupervised Learning with Jacob Effron · Ep 94: Applied Compute CEO on the Limits of RL, the New AI Hyperscaler & Why Post-Training Wins Inference

Play from 40:14
40:14

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

What was described

Generated synthetic QA pairs by back-translating documents into question-answer format for training data

Said nearby on the same subject

“Synthetic data generation techniques… take some amount of tokens and explode it into something much larger and pull learning signal out of that”
♫
Said on Unsupervised Learning with Jacob Effron
Why it worked Share this moment
“We trained the final model on the SpaceX cluster ... a few weeks of pre-training, then synthetic data NRL and then shipping it”
ML
Misha Laskin Beam
No Priors: Artificial Intelligence | Technology | Startups ·
How they work Share this moment
“The model can also generate videos… the model can also generate its own training data so that becomes a self-fulfilling flywheel”
♫
Said on Latent Space: The AI Engineer Podcast
Automation Share this moment
“In the Sudan case, experts used a machine learning algorithm trained on over a million examples of synthetic speech to prove, almost without a shadow of a doubt, that it was authentic.”
♫
Said on TED Tech
How they work over a million examples 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.