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المحتوى المقدم من Machine Learning Street Talk (MLST). يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Machine Learning Street Talk (MLST) أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
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How Machines Learn to Ignore the Noise (Kevin Ellis + Zenna Tavares)

1:16:55
 
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Manage episode 475912602 series 2803422
المحتوى المقدم من Machine Learning Street Talk (MLST). يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Machine Learning Street Talk (MLST) أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

Prof. Kevin Ellis and Dr. Zenna Tavares talk about making AI smarter, like humans. They want AI to learn from just a little bit of information by actively trying things out, not just by looking at tons of data.

They discuss two main ways AI can "think": one way is like following specific rules or steps (like a computer program), and the other is more intuitive, like guessing based on patterns (like modern AI often does). They found combining both methods works well for solving complex puzzles like ARC.

A key idea is "compositionality" - building big ideas from small ones, like LEGOs. This is powerful but can also be overwhelming. Another important idea is "abstraction" - understanding things simply, without getting lost in details, and knowing there are different levels of understanding.

Ultimately, they believe the best AI will need to explore, experiment, and build models of the world, much like humans do when learning something new.

SPONSOR MESSAGES:

***

Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich.

Goto https://tufalabs.ai/

***

TRANSCRIPT:

https://www.dropbox.com/scl/fi/3ngggvhb3tnemw879er5y/BASIS.pdf?rlkey=lr2zbj3317mex1q5l0c2rsk0h&dl=0

Zenna Tavares:

http://www.zenna.org/

Kevin Ellis:

https://www.cs.cornell.edu/~ellisk/

TOC:

1. Compositionality and Learning Foundations

[00:00:00] 1.1 Compositional Search and Learning Challenges

[00:03:55] 1.2 Bayesian Learning and World Models

[00:12:05] 1.3 Programming Languages and Compositionality Trade-offs

[00:15:35] 1.4 Inductive vs Transductive Approaches in AI Systems

2. Neural-Symbolic Program Synthesis

[00:27:20] 2.1 Integration of LLMs with Traditional Programming and Meta-Programming

[00:30:43] 2.2 Wake-Sleep Learning and DreamCoder Architecture

[00:38:26] 2.3 Program Synthesis from Interactions and Hidden State Inference

[00:41:36] 2.4 Abstraction Mechanisms and Resource Rationality

[00:48:38] 2.5 Inductive Biases and Causal Abstraction in AI Systems

3. Abstract Reasoning Systems

[00:52:10] 3.1 Abstract Concepts and Grid-Based Transformations in ARC

[00:56:08] 3.2 Induction vs Transduction Approaches in Abstract Reasoning

[00:59:12] 3.3 ARC Limitations and Interactive Learning Extensions

[01:06:30] 3.4 Wake-Sleep Program Learning and Hybrid Approaches

[01:11:37] 3.5 Project MARA and Future Research Directions

REFS:

[00:00:25] DreamCoder, Kevin Ellis et al.

https://arxiv.org/abs/2006.08381

[00:01:10] Mind Your Step, Ryan Liu et al.

https://arxiv.org/abs/2410.21333

[00:06:05] Bayesian inference, Griffiths, T. L., Kemp, C., & Tenenbaum, J. B.

https://psycnet.apa.org/record/2008-06911-003

[00:13:00] Induction and Transduction, Wen-Ding Li, Zenna Tavares, Yewen Pu, Kevin Ellis

https://arxiv.org/abs/2411.02272

[00:23:15] Neurosymbolic AI, Garcez, Artur d'Avila et al.

https://arxiv.org/abs/2012.05876

[00:33:50] Induction and Transduction (II), Wen-Ding Li, Kevin Ellis et al.

https://arxiv.org/abs/2411.02272

[00:38:35] ARC, François Chollet

https://arxiv.org/abs/1911.01547

[00:39:20] Causal Reactive Programs, Ria Das, Joshua B. Tenenbaum, Armando Solar-Lezama, Zenna Tavares

http://www.zenna.org/publications/autumn2022.pdf

[00:42:50] MuZero, Julian Schrittwieser et al.

http://arxiv.org/pdf/1911.08265

[00:43:20] VisualPredicator, Yichao Liang

https://arxiv.org/abs/2410.23156

[00:48:55] Bayesian models of cognition, Joshua B. Tenenbaum

https://mitpress.mit.edu/9780262049412/bayesian-models-of-cognition/

[00:49:30] The Bitter Lesson, Rich Sutton

http://www.incompleteideas.net/IncIdeas/BitterLesson.html

[01:06:35] Program induction, Kevin Ellis, Wen-Ding Li

https://arxiv.org/pdf/2411.02272

[01:06:50] DreamCoder (II), Kevin Ellis et al.

https://arxiv.org/abs/2006.08381

[01:11:55] Project MARA, Zenna Tavares, Kevin Ellis

https://www.basis.ai/blog/mara/

  continue reading

232 حلقات

Artwork
iconمشاركة
 
Manage episode 475912602 series 2803422
المحتوى المقدم من Machine Learning Street Talk (MLST). يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Machine Learning Street Talk (MLST) أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

Prof. Kevin Ellis and Dr. Zenna Tavares talk about making AI smarter, like humans. They want AI to learn from just a little bit of information by actively trying things out, not just by looking at tons of data.

They discuss two main ways AI can "think": one way is like following specific rules or steps (like a computer program), and the other is more intuitive, like guessing based on patterns (like modern AI often does). They found combining both methods works well for solving complex puzzles like ARC.

A key idea is "compositionality" - building big ideas from small ones, like LEGOs. This is powerful but can also be overwhelming. Another important idea is "abstraction" - understanding things simply, without getting lost in details, and knowing there are different levels of understanding.

Ultimately, they believe the best AI will need to explore, experiment, and build models of the world, much like humans do when learning something new.

SPONSOR MESSAGES:

***

Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich.

Goto https://tufalabs.ai/

***

TRANSCRIPT:

https://www.dropbox.com/scl/fi/3ngggvhb3tnemw879er5y/BASIS.pdf?rlkey=lr2zbj3317mex1q5l0c2rsk0h&dl=0

Zenna Tavares:

http://www.zenna.org/

Kevin Ellis:

https://www.cs.cornell.edu/~ellisk/

TOC:

1. Compositionality and Learning Foundations

[00:00:00] 1.1 Compositional Search and Learning Challenges

[00:03:55] 1.2 Bayesian Learning and World Models

[00:12:05] 1.3 Programming Languages and Compositionality Trade-offs

[00:15:35] 1.4 Inductive vs Transductive Approaches in AI Systems

2. Neural-Symbolic Program Synthesis

[00:27:20] 2.1 Integration of LLMs with Traditional Programming and Meta-Programming

[00:30:43] 2.2 Wake-Sleep Learning and DreamCoder Architecture

[00:38:26] 2.3 Program Synthesis from Interactions and Hidden State Inference

[00:41:36] 2.4 Abstraction Mechanisms and Resource Rationality

[00:48:38] 2.5 Inductive Biases and Causal Abstraction in AI Systems

3. Abstract Reasoning Systems

[00:52:10] 3.1 Abstract Concepts and Grid-Based Transformations in ARC

[00:56:08] 3.2 Induction vs Transduction Approaches in Abstract Reasoning

[00:59:12] 3.3 ARC Limitations and Interactive Learning Extensions

[01:06:30] 3.4 Wake-Sleep Program Learning and Hybrid Approaches

[01:11:37] 3.5 Project MARA and Future Research Directions

REFS:

[00:00:25] DreamCoder, Kevin Ellis et al.

https://arxiv.org/abs/2006.08381

[00:01:10] Mind Your Step, Ryan Liu et al.

https://arxiv.org/abs/2410.21333

[00:06:05] Bayesian inference, Griffiths, T. L., Kemp, C., & Tenenbaum, J. B.

https://psycnet.apa.org/record/2008-06911-003

[00:13:00] Induction and Transduction, Wen-Ding Li, Zenna Tavares, Yewen Pu, Kevin Ellis

https://arxiv.org/abs/2411.02272

[00:23:15] Neurosymbolic AI, Garcez, Artur d'Avila et al.

https://arxiv.org/abs/2012.05876

[00:33:50] Induction and Transduction (II), Wen-Ding Li, Kevin Ellis et al.

https://arxiv.org/abs/2411.02272

[00:38:35] ARC, François Chollet

https://arxiv.org/abs/1911.01547

[00:39:20] Causal Reactive Programs, Ria Das, Joshua B. Tenenbaum, Armando Solar-Lezama, Zenna Tavares

http://www.zenna.org/publications/autumn2022.pdf

[00:42:50] MuZero, Julian Schrittwieser et al.

http://arxiv.org/pdf/1911.08265

[00:43:20] VisualPredicator, Yichao Liang

https://arxiv.org/abs/2410.23156

[00:48:55] Bayesian models of cognition, Joshua B. Tenenbaum

https://mitpress.mit.edu/9780262049412/bayesian-models-of-cognition/

[00:49:30] The Bitter Lesson, Rich Sutton

http://www.incompleteideas.net/IncIdeas/BitterLesson.html

[01:06:35] Program induction, Kevin Ellis, Wen-Ding Li

https://arxiv.org/pdf/2411.02272

[01:06:50] DreamCoder (II), Kevin Ellis et al.

https://arxiv.org/abs/2006.08381

[01:11:55] Project MARA, Zenna Tavares, Kevin Ellis

https://www.basis.ai/blog/mara/

  continue reading

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