ViewTube

ViewTube
Sign inSign upSubscriptions
Filters

Upload date

Type

Duration

Sort by

Features

Reset

4,172 results

Institute for Pure & Applied Mathematics (IPAM)
Max Tegmark - Neural network interpretability: symmetry, geometry and formal verification

Recorded 01 September 2026. Max Tegmark of the Massachusetts Institute of Technology presents "Neural network ...

51:58
Max Tegmark - Neural network interpretability: symmetry, geometry and formal verification

25,697 views

1mo ago

Institute for Pure & Applied Mathematics (IPAM)
Paul Riechers - The shape of beliefs and abstraction in neural networks - IPAM at UCLA

Recorded 04 September 2026. Paul Riechers of Simplex presents "The shape of beliefs and abstraction in neural networks" at ...

54:34
Paul Riechers - The shape of beliefs and abstraction in neural networks - IPAM at UCLA

7,513 views

1mo ago

EuroPython Conference
Hacking LLMs: An Introduction to Mechanistic Interpretability — Jenny Vega

EuroPython 2025 — South Hall 2B on 2025-07-17] *Hacking LLMs: An Introduction to Mechanistic Interpretability by Jenny ...

30:28
Hacking LLMs: An Introduction to Mechanistic Interpretability — Jenny Vega

5,850 views

11mo ago

Papers Are Wonderful
A Window  Into LLMs | Sparse Autoencoders Explained

This has been my favorite video so far to make! I think interpretability is so important both in terms of ensuring safe AI and also ...

5:27
A Window Into LLMs | Sparse Autoencoders Explained

21,594 views

1y ago

Institute for Pure & Applied Mathematics (IPAM)
Josh Batson - Interpretability: From Art Towards Science - IPAM at UCLA

Recorded 31 August 2026. Josh Batson of Anthropic presents "Interpretability: From Art Towards Science" at IPAM's Foundations ...

43:57
Josh Batson - Interpretability: From Art Towards Science - IPAM at UCLA

3,719 views

1mo ago

Institute for Pure & Applied Mathematics (IPAM)
Koji Hashimoto - On the interpretability of neural network solutions of PDEs in theoretical physics

Recorded 18 September 2026. Koji Hashimoto of Kyoto University presents "On the interpretability of neural network solutions of ...

33:58
Koji Hashimoto - On the interpretability of neural network solutions of PDEs in theoretical physics

18,136 views

3w ago

UofU Data Science
UUtah CS 6966 Interpretability of LLMs | Spring 2026 | Transcoders

Notes:

49:55
UUtah CS 6966 Interpretability of LLMs | Spring 2026 | Transcoders

349 views

8mo ago

Institute for Pure & Applied Mathematics (IPAM)
Terence Tao - Reflections on the Foundations of Interpretability workshop - IPAM at UCLA

Recorded 04 September 2026. Terence Tao of the University of California, Los Angeles, presents "Reflections on the Foundations ...

34:13
Terence Tao - Reflections on the Foundations of Interpretability workshop - IPAM at UCLA

37,505 views

1mo ago

Chris Potts
Assessing skeptical views of interpretability research

Stanford AI Lab Faculty Lunch, November 7, 2025. Updated version of https://web.stanford.edu/~cgpotts/blog/interp/ 0:59 ...

37:23
Assessing skeptical views of interpretability research

6,540 views

11mo ago

Chris Potts
Reassessing stimulus poverty arguments using causal interpretability methods

Practice run of a talk given at the First Workshop on CogInterp: Interpreting Cognition in Deep Learning Models, December 7, ...

28:02
Reassessing stimulus poverty arguments using causal interpretability methods

1,031 views

10mo ago

Institute for Pure & Applied Mathematics (IPAM)
Gitta Kutyniok - From Mathematical Guarantees to Computational Limits of AI Interpretability

Recorded 03 September 2026. Gitta Kutyniok of Ludwig-Maximilians-Universität München presents "From Mathematical ...

48:05
Gitta Kutyniok - From Mathematical Guarantees to Computational Limits of AI Interpretability

1,813 views

1mo ago

UofU Data Science
UUtah CS 6966 Interpretability of LLMs | Spring 2026 | Sparse autoencoders: Basics

Notes: https://drive.google.com/file/d/1GTIqXS-vEiDz2rAPfdeB_5G5IjBfNkxF/view?usp=sharing.

19:39
UUtah CS 6966 Interpretability of LLMs | Spring 2026 | Sparse autoencoders: Basics

334 views

9mo ago

Institute for Pure & Applied Mathematics (IPAM)
Been Kim - Changing Shapes: Interpretability, AI, and the Future of Human Agency - IPAM at UCLA

Recorded 31 August 2026. Been Kim of Google DeepMind presents "Changing Shapes: Interpretability, AI, and the Future of ...

40:09
Been Kim - Changing Shapes: Interpretability, AI, and the Future of Human Agency - IPAM at UCLA

4,222 views

1mo ago

Institute for Pure & Applied Mathematics (IPAM)
Mahdi Soltanolkotabi - Interpreting Generative Models for Better Steering: Generation to Verifiable

Recorded 03 September 2026. Mahdi Soltanolkotabi of the University of Southern California presents "Interpreting Generative ...

42:30
Mahdi Soltanolkotabi - Interpreting Generative Models for Better Steering: Generation to Verifiable

1,116 views

1mo ago

UofU Data Science
UUtah CS 6966 Interpretability of LLMs | Spring 2026 | Model diffing with crosscoders

Notes: TBD.

44:02
UUtah CS 6966 Interpretability of LLMs | Spring 2026 | Model diffing with crosscoders

199 views

7mo ago

UofU Data Science
UUtah CS 6966 Interpretability of LLMs | Spring 2026 | Review of language model reasoning

Slides: https://docs.google.com/presentation/d/1AGNYS_nAcO_SDzYwqRlXUbMvrNzqZE1oGT3ikxKZVyI/edit?usp=sharing.

1:19:28
UUtah CS 6966 Interpretability of LLMs | Spring 2026 | Review of language model reasoning

87 views

5mo ago

Institute for Pure & Applied Mathematics (IPAM)
Elias Bareinboim - Towards Causal AI: From Mechanism to Understanding - IPAM at UCLA

Recorded 02 September 2026. Elias Bareinboim of Columbia University presents "Towards Causal AI: From Mechanism to ...

54:38
Elias Bareinboim - Towards Causal AI: From Mechanism to Understanding - IPAM at UCLA

6,647 views

1mo ago

UofU Data Science
UUtah CS 6966 Interpretability of LLMs | Spring 2026 | Evaluating SAE feature descriptions

Notes: https://drive.google.com/file/d/1iV7H3W7t3o-n6BRyYxleCvPVGO-I_hdQ/view?usp=sharing.

1:12:08
UUtah CS 6966 Interpretability of LLMs | Spring 2026 | Evaluating SAE feature descriptions

258 views

8mo ago

UofU Data Science
UUtah CS 6966 Interpretability of LLMs | Spring 2026 | Generating SAE feature descriptions

Notes: https://drive.google.com/file/d/125hZQEWbhwXbD5Pym9v-JBfXq6WnKajt/view?usp=sharing.

1:11:40
UUtah CS 6966 Interpretability of LLMs | Spring 2026 | Generating SAE feature descriptions

276 views

8mo ago

Institute for Pure & Applied Mathematics (IPAM)
Bin Yu - Neural network interpretability: symmetry, geometry and formal verification

Recorded 01 September 2026. Bin Yu of the University of California, Berkeley, presents "Neural network interpretability: symmetry ...

46:20
Bin Yu - Neural network interpretability: symmetry, geometry and formal verification

4,127 views

1mo ago

Show more