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The Gradient: Perspectives on AI
Linus Lee: At the Boundary of Machine and Mind
Jan 19, 2023
0
02:28:46
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Episode notes
1.
Introduction
2min
2.
What's Your Deal With AI?
3min
3.
How Much Can Text Tell Us About the World?
3min
4.
Is There a Limit to Embodiment?
2min
5.
What Is the Complementary Nature of Artificial Intelligence?
4min
6.
Is There a Kind of Intelligence?
3min
7.
The Taste of Intelligence
2min
8.
All Possible Human Languages
2min
9.
The Natural Languages That Push the Limits
2min
10.
How Do You Speak Like Colloquial Chinese?
4min
11.
Is There a Specific Set of Tools That You Can Use in ML?
3min
12.
The Concept of Like an Operation on an Idea
2min
13.
What's Your Journey to Doing Independent Research?
6min
14.
Is Public Discourse Really Helpful for Learning?
2min
15.
Is There a Balance Between Productivity and Self-Implied Deadlines?
4min
16.
Is There a Taste for Programming Languages?
4min
17.
Is There a Personal Search Engine?
2min
18.
The Data That We Don't Create
2min
19.
Is There a Difference in Intentionality?
2min
20.
The Web Browser as a Tool of Thought
3min
21.
I Think the Web Browser Is a Very Multimodal Very Dynamic Interactive
3min
22.
Is There a Way to Generate a ML Model That Doesn't Exist
2min
23.
What's the Difference Between Notation and Language?
3min
24.
What Is Good Notation?
5min
25.
Is Notation a Tool for Expression?
5min
26.
How Do We Use Language Models to Improve Computers?
3min
27.
The Language Models - Agents vs Tools
5min
28.
Is There a Difference Between Direct and Indirect Prompting?
3min
29.
AI Interface Design
5min
30.
Can You Teach a Model of Language?
3min
31.
Using Latent Space Models in Prototyping Objects
4min
32.
The Right Way to Work With Ideas
2min
33.
The Space of Ideas and the Concept of Language Models and General Models
3min
34.
Neural Nets Are a Different Kind of Tool From Computers
5min
35.
Is There a Space for Machine Learning and Hardware?
3min
36.
Is There a Future for Apple's Chips?
3min
37.
Midnight's Is a Polarizing Album, I'm Curious How You Responded to It
3min
38.
The Learning Curve Is a Sign of Good Interfaces
3min
39.
The Learning Curve in Vim Is a Great Example
2min
40.
The Great Library
3min
41.
The Work of Fiction Is a Really Interesting Medium to Explore Beyond the Limits of Where We Can Actually Build
3min
42.
What Is Your Workflow for Writing Short Sentence?
3min
43.
The Process of Writing
2min
44.
Writing Is a Work in Progress and I've Learned to Take a Different View of Writing
2min
45.
Is There a Dissonance in Your Work?
5min
46.
The Right Way to Get to the Top of Hacker News
2min
47.
Open-Endingness
2min
48.
Is There a Disaggregation?
4min
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.
Have suggestions for future podcast guests (or other feedback)? Let us know here!
Subscribe to The Gradient Podcast: Apple Podcasts | Spotify | Pocket Casts | RSSFollow The Gradient on Twitter
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:
Get full access to The Gradient at thegradientpub.substack.com/subscribe
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