What actually is AI research? (w/ Jonathan Frankle, Chief AI Scientist at Databricks)

Every few weeks a new AI model tops the charts, as if dropped out of the sky. But what's the work that actually goes into that?

• AI research is one loop: change the initial setup, run it, see if the numbers improve. "Did those numbers improve and what did we change and how did those numbers change," and then "are those numbers truly meaningful in the things that matter for our customers."
• Reinforcement learning depends on scoring of outcomes. "You give a model a problem to solve, you let it try to solve that problem, you give it feedback on how well it tried to solve that problem."
• Most AI answers can't be marked as simply right or wrong. Databricks builds example companies to test its agents, then grades the answers "the way you would grade a student's essay in school."
• Models learn to game their own tests: "it makes the numbers look really good. And then you actually have the team start interacting with the model and you're like, this sucks."
• Fine-tuning is a commitment, not recommended for most. Jonathan usually says no first: "Once you do this it's a commitment. You're kind of getting married to fine-tuning for a while."
• A better benchmark score doesn't mean a better model for you. "You are not a general-purpose benchmark."

Presented by Railway, your agent’s favorite cloud.

Original music by The Jazz Boys.

Show notes and the full transcript: https://technically.dev/podcast/what-does-an-ai-researcher-actually-do

• (00:00) - What is AI research?
• (07:10) - What does the job actually look like?
• (13:57) - How do you know an experiment worked?
• (26:15) - What makes a good researcher?
• (31:16) - What happens between model launches?
• (44:14) - What is fine-tuning?
• (50:50) - How do you measure an agent?
• (56:59) - What is still unsolved?