Exploring Self-Consistency and Model Evolution in GPT4-Like Models

In episode 116 of The Gradient Podcast, Daniel Bashir speaks to Kate Park.

Kate is the Director of Product at Scale AI. Prior to joining Scale, Kate worked on Tesla Autopilot as the AI team’s first and lead product manager building the industry’s first data engine. She has also published research on spoken natural language processing and a travel memoir.

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Outline:

* (00:00) Intro

* (01:11) Kate’s background

* (03:22) Tesla and cameras vs. Lidar, importance of data

* (05:12) “Data is key”

* (07:35) Data vs. architectural improvements

* (09:36) Effort for data scaling

* (10:55) Transfer of capabilities in self-driving

* (13:44) Data flywheels and edge cases, deployment

* (15:48) Transition to Scale

* (18:52) Perspectives on shifting to transformers and data

* (21:00) Data engines for NLP vs. for vision

* (25:32) Model evaluation for LLMs in data engines

* (27:15) InstructGPT and data for RLHF

* (29:15) Benchmark tasks for assessing potential labelers

* (32:07) Biggest challenges for data engines

* (33:40) Expert AI trainers

* (36:22) Future work in data engines

* (38:25) Need for human labeling when bootstrapping new domains or tasks

* (41:05) Outro

Links:

* Scale Data Engine

* OpenAI case study