The Gradient: Perspectives on AI Linus Lee: At the Boundary of Machine and Mind
Linus Lee's Background
- Linus Lee's background is in web engineering, primarily front-end development focusing on user interfaces.
- He transitioned to independent research exploring knowledge representation and creative work aided by AI.
Language Models and World Knowledge
- Language models, like DALL-E 2, indirectly model human understanding of the world, which proves surprisingly effective.
- They achieve this by encoding knowledge about images explicitly or implicitly through extensive text and image data.
Alien Intelligence of Language Models
- Language models don't experience the world like humans; they lack a sense of time or individual experiences.
- Instead, they access a distribution of all possible experiences within their training data, like an alien intelligence.
In episode 56 of The Gradient Podcast, Daniel Bashir speaks to Linus Lee.
Linus is an independent researcher interested in the future of knowledge representation and creative work aided by machine understanding of language. He builds interfaces and knowledge tools that expand the domain of thoughts we can think and qualia we can feel. Linus has been writing online since 2014–his blog boasts half a million words–and has built well over 100 side projects. He has also spent time as a software engineer at Replit, Hack Club, and Spensa, and was most recently a Researcher in Residence at Betaworks in New York.
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Outline:
* (00:00) Intro
* (02:00) Linus’s background and interests, vision-language models
* (07:45) Embodiment and limits for text-image
* (11:35) Ways of experiencing the world
* (16:55) Origins of the handle “thesephist”, languages
* (25:00) Math notation, reading papers
* (29:20) Operations on ideas
* (32:45) Overview of Linus’s research and current work
* (41:30) The Oak and Ink languages, programming languages
* (49:30) Personal search engines: Monocle and Reverie, what you can learn from personal data
* (55:55) Web browsers as mediums for thought
* (1:01:30) This AI Does Not Exist
* (1:03:05) Knowledge representation and notational intelligence
* Notation vs language
* (1:07:00) What notation can/should be
* (1:16:00) Inventing better notations and expanding human intelligence
* (1:23:30) Better interfaces between humans and LMs to provide precise control, inefficiency prompt engineering
* (1:33:00) Inexpressible experiences
* (1:35:42) Linus’s current work using latent space models
* (1:40:00) Ideas as things you can hold
* (1:44:55) Neural nets and cognitive computing
* (1:49:30) Relation to Hardware Lottery and AI accelerators
* (1:53:00) Taylor Swift Appreciation Session, mastery and virtuosity
* (1:59:30) Mastery/virtuosity and interfaces / learning curves
* (2:03:30) Linus’s stories, the work of fiction
* (2:09:00) Linus’s thoughts on writing
* (2:14:20) A piece of writing should be focused
* (2:16:15) On proving yourself
* (2:28:00) Outro
Links:
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