Our last AI PhD grad student feature was Shunyu Yao, who happened to focus on Language Agents for his thesis and immediately went to work on them for OpenAI. Our pick this year is Jack Morris, who bucks the “hot” trends by -not- working on agents, benchmarks, or VS Code forks, but is rather known for his work on the information theoretic understanding of LLMs, starting from embedding models and latent space representations (always close to our heart).
Jack is an unusual combination of doing underrated research but somehow still being to explain them well to a mass audience, so we felt this was a good opportunity to do a different kind of episode going through the greatest hits of a high profile AI PhD, and relate them to questions from AI Engineering.
Papers and References made
AI grad school:
A new type of information theory:
Embeddings
Text Embeddings Reveal (Almost) As Much As Text: https://arxiv.org/abs/2310.06816
Contextual document embeddings https://arxiv.org/abs/2410.02525
Harnessing the Universal Geometry of Embeddings: https://arxiv.org/abs/2505.12540
Language models
GPT-style language models memorize 3.6 bits per param:
Approximating Language Model Training Data from Weights: https://arxiv.org/abs/2506.15553
LLM Inversion
“There Are No New Ideas In AI.... Only New Datasets”
misc reference: https://junyanz.github.io/CycleGAN/
—
for others hiring AI PhDs, Jack also wanted to shout out his coauthor
Zach Nussbaum, his coauthor on Nomic Embed: Training a Reproducible Long Context Text Embedder.
Full Video Episode
Timestamps
00:00 Introduction to Jack Morris
01:18 Career in AI
03:29 The Shift to AI Companies
03:57 The Impact of ChatGPT
04:26 The Role of Academia in AI
05:49 The Emergence of Reasoning Models
07:07 Challenges in Academia: GPUs and HPC Training
11:04 The Value of GPU Knowledge
14:24 Introduction to Jack's Research
15:28 Information Theory
17:10 Understanding Deep Learning Systems
19:00 The "Bit" in Deep Learning
20:25 Wikipedia and Information Storage
23:50 Text Embeddings and Information Compression
27:08 The Research Journey of Embedding Inversion
31:22 Harnessing the Universal Geometry of Embeddings
34:54 Implications of Embedding Inversion
36:02 Limitations of Embedding Inversion
38:08 The Capacity of Language Models
40:23 The Cognitive Core and Model Efficiency
50:40 The Future of AI and Model Scaling
52:47 Approximating Language Model Training Data from Weights
01:06:50 The "No New Ideas, Only New Datasets" Thesis
