#54 Prof. GARY MARCUS + Prof. LUIS LAMB - Neurosymbolic models
Professor Gary Marcus is a scientist, best-selling author, and entrepreneur. He is Founder and CEO of Robust.AI, and was Founder and CEO of Geometric Intelligence, a machine learning company acquired by Uber in 2016. Gary said in his recent next decade paper that — without us, or other creatures like us, the world would continue to exist, but it would not be described, distilled, or understood. Human lives are filled with abstraction and causal description. This is so powerful. Francois Chollet the other week said that intelligence is literally sensitivity to abstract analogies, and that is all there is to it. It's almost as if one of the most important features of intelligence is to be able to abstract knowledge, this drives the generalisation which will allow you to mine previous experience to make sense of many future novel situations.
Also joining us today is Professor Luis Lamb — Secretary of Innovation for Science and Technology of the State of Rio Grande do Sul, Brazil. His Research Interests are Machine Learning and Reasoning, Neuro-Symbolic Computing, Logic in Computation and Artificial Intelligence, Cognitive and Neural Computation and also AI Ethics and Social Computing. Luis released his new paper Neurosymbolic AI: the third wave at the end of last year. It beautifully articulated the key ingredients needed in the next generation of AI systems, integrating type 1 and type 2 approaches to AI and it summarises all the of the achievements of the last 20 years of research.
We cover a lot of ground in today's show. Explaining the limitations of deep learning, Rich Sutton's the bitter lesson and "reward is enough", and the semantic foundation which is required for us to build robust AI.
Pod: https://anchor.fm/machinelearningstreettalk/episodes/54-Gary-Marcus-and-Luis-Lamb---Neurosymbolic-models-e125495
Tim Epic Intro [00:00:00]
Main Intro [00:38:05]
Gary introduces the field [00:42:12]
Luis introduces his thoughts on Neurosymbolic methods [00:47:56]
On the history of achieving a logical foundation and mathematical foundation for semantics [00:54:12]
Will emulating discrete reasoning break optimizability?
Buzzwords without basis [01:04:34]
We have known for decades about the statistical regularities in language [01:07:02]
Intension vs extension [01:09:14]
Easy to demand abstraction, but what is a workable definition? [01:13:33]
Abstraction is a "terrorist attack on neural networks" [01:20:38]
To succeed we need both, we are the moderates [01:30:14]
What would the future world look like with better semantics? [01:31:32]
Promising current approaches to discrete reasoning systems [01:39:58]
The challenge of machine knowledge acquisition [01:47:32]
Prof. Lamb's more on relational learning [01:53:06]
The role of vector embeddings and neural symbolics [02:02:30]
Humans seem both good and bad at reasoning, what's going on? [02:09:06]
Is reasoning a first-class citizen in the human brain? [02:15:06]
Does reasoning happen on the same substrate as system 1? [02:17:08]
GM papers:
The Next Decade in AI
https://arxiv.org/abs/2002.06177
Innateness, AlphaZero, and Artificial Intelligence
https://arxiv.org/abs/1801.05667
Deep Learning: A Critical Appraisal
https://arxiv.org/abs/1801.00631
Rule learning by seven-month-old infants
https://www.researchgate.net/publication/13415195_Rule_learning_by_seven-month-old_infants
Rethinking Eliminative Connectionism
https://nyuscholars.nyu.edu/en/publications/rethinking-eliminative-connectionism
GM YB Debate
The Best Way Forward For AI
https://montrealartificialintelligence.com/aidebate/
GM:
Rebooting AI
https://www.amazon.com/Rebooting-AI-Building-Artificial-Intelligence-ebook/dp/B07MYLGQLB
Kluge: The Haphazard Evolution of the Human Mind
https://www.amazon.com/Kluge-Haphazard-Evolution-Human-Mind-ebook/dp/B003JTHWQ4
The Birth of The Mind
https://www.amazon.com/Birth-Mind-Creates-Complexities-Thought/dp/0465044069
The Algebraic Mind
https://www.amazon.com/Algebraic-Mind-Integrating-Connectionism-Development/dp/0262632683
LL:
Neurosymbolic AI: The 3rd Wave
https://arxiv.org/pdf/2012.05876.pdf
Understanding Boolean Function Learnability on Deep Neural Networks
https://arxiv.org/pdf/2009.05908.pdf
Graph Neural Networks Meet Neural-Symbolic Computing
https://arxiv.org/abs/2003.00330
Discrete and Continuous Deep Residual Learning Over Graphs
https://arxiv.org/pdf/1911.09554.pdf
Learning to Solve NP-Complete Problems
https://arxiv.org/abs/1809.02721
Neural-symbolic Computing
https://arxiv.org/pdf/1905.06088.pdf
Neural-symbolic learning and reasoning
https://arxiv.org/abs/1711.03902
Neural-symbolic learning and reasoning
https://openaccess.city.ac.uk/id/eprint/11838/
LL books:
Neural-Symbolic Cognitive Reasoning
https://www.amazon.com/Neural-Symbolic-Cognitive-Reasoning-Technologies/dp/3642092292
A Uniform Presentation of Non-Classical Logics
https://www.amazon.com/Compiled-Labelled-Deductive-Systems-Non-Classical/dp/0863802966
Видео #54 Prof. GARY MARCUS + Prof. LUIS LAMB - Neurosymbolic models канала Machine Learning Street Talk
Also joining us today is Professor Luis Lamb — Secretary of Innovation for Science and Technology of the State of Rio Grande do Sul, Brazil. His Research Interests are Machine Learning and Reasoning, Neuro-Symbolic Computing, Logic in Computation and Artificial Intelligence, Cognitive and Neural Computation and also AI Ethics and Social Computing. Luis released his new paper Neurosymbolic AI: the third wave at the end of last year. It beautifully articulated the key ingredients needed in the next generation of AI systems, integrating type 1 and type 2 approaches to AI and it summarises all the of the achievements of the last 20 years of research.
We cover a lot of ground in today's show. Explaining the limitations of deep learning, Rich Sutton's the bitter lesson and "reward is enough", and the semantic foundation which is required for us to build robust AI.
Pod: https://anchor.fm/machinelearningstreettalk/episodes/54-Gary-Marcus-and-Luis-Lamb---Neurosymbolic-models-e125495
Tim Epic Intro [00:00:00]
Main Intro [00:38:05]
Gary introduces the field [00:42:12]
Luis introduces his thoughts on Neurosymbolic methods [00:47:56]
On the history of achieving a logical foundation and mathematical foundation for semantics [00:54:12]
Will emulating discrete reasoning break optimizability?
Buzzwords without basis [01:04:34]
We have known for decades about the statistical regularities in language [01:07:02]
Intension vs extension [01:09:14]
Easy to demand abstraction, but what is a workable definition? [01:13:33]
Abstraction is a "terrorist attack on neural networks" [01:20:38]
To succeed we need both, we are the moderates [01:30:14]
What would the future world look like with better semantics? [01:31:32]
Promising current approaches to discrete reasoning systems [01:39:58]
The challenge of machine knowledge acquisition [01:47:32]
Prof. Lamb's more on relational learning [01:53:06]
The role of vector embeddings and neural symbolics [02:02:30]
Humans seem both good and bad at reasoning, what's going on? [02:09:06]
Is reasoning a first-class citizen in the human brain? [02:15:06]
Does reasoning happen on the same substrate as system 1? [02:17:08]
GM papers:
The Next Decade in AI
https://arxiv.org/abs/2002.06177
Innateness, AlphaZero, and Artificial Intelligence
https://arxiv.org/abs/1801.05667
Deep Learning: A Critical Appraisal
https://arxiv.org/abs/1801.00631
Rule learning by seven-month-old infants
https://www.researchgate.net/publication/13415195_Rule_learning_by_seven-month-old_infants
Rethinking Eliminative Connectionism
https://nyuscholars.nyu.edu/en/publications/rethinking-eliminative-connectionism
GM YB Debate
The Best Way Forward For AI
https://montrealartificialintelligence.com/aidebate/
GM:
Rebooting AI
https://www.amazon.com/Rebooting-AI-Building-Artificial-Intelligence-ebook/dp/B07MYLGQLB
Kluge: The Haphazard Evolution of the Human Mind
https://www.amazon.com/Kluge-Haphazard-Evolution-Human-Mind-ebook/dp/B003JTHWQ4
The Birth of The Mind
https://www.amazon.com/Birth-Mind-Creates-Complexities-Thought/dp/0465044069
The Algebraic Mind
https://www.amazon.com/Algebraic-Mind-Integrating-Connectionism-Development/dp/0262632683
LL:
Neurosymbolic AI: The 3rd Wave
https://arxiv.org/pdf/2012.05876.pdf
Understanding Boolean Function Learnability on Deep Neural Networks
https://arxiv.org/pdf/2009.05908.pdf
Graph Neural Networks Meet Neural-Symbolic Computing
https://arxiv.org/abs/2003.00330
Discrete and Continuous Deep Residual Learning Over Graphs
https://arxiv.org/pdf/1911.09554.pdf
Learning to Solve NP-Complete Problems
https://arxiv.org/abs/1809.02721
Neural-symbolic Computing
https://arxiv.org/pdf/1905.06088.pdf
Neural-symbolic learning and reasoning
https://arxiv.org/abs/1711.03902
Neural-symbolic learning and reasoning
https://openaccess.city.ac.uk/id/eprint/11838/
LL books:
Neural-Symbolic Cognitive Reasoning
https://www.amazon.com/Neural-Symbolic-Cognitive-Reasoning-Technologies/dp/3642092292
A Uniform Presentation of Non-Classical Logics
https://www.amazon.com/Compiled-Labelled-Deductive-Systems-Non-Classical/dp/0863802966
Видео #54 Prof. GARY MARCUS + Prof. LUIS LAMB - Neurosymbolic models канала Machine Learning Street Talk
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Getting ready for Dr Thomas Parr interview, watch it first on Patreon!#035 Christmas Community Edition!Prof. Simon Prince on factor graphsJordan Edwards: ML Engineering and DevOps on AzureMLCapsule Networks and EducationLuciano Floridi on the ramifications of working in AI #machineleaning #artificialintelligence#94 - ALAN CHAN - AI Alignment and Governance #NEURIPS#62 Dr. GUY EMERSON [Unplugged]Riddhi Jain Pitliya on virtual agents and memes #aiDr. THOMAS PARR - Active InferenceProf. Sepp Hochreiter: A Pioneer in Deep Learning#83 Dr. ANDREW LAMPINEN (Deepmind) - Natural Language, Symbols and Grounding [NEURIPS2022 UNPLUGGED]#52 - Dr. HADI SALMAN - Adversarial Examples Beyond Security [MIT]#063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness & CausalityLeetcode Challenge with DeepMind & Mila Scientists!SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments (Mathilde Caron)#71 - ZAK JOST (Graph Neural Networks + Geometric DL) [UNPLUGGED]#85 Dr. Petar Veličković (Deepmind) - Categories, Graphs, Reasoning [NEURIPS22 UNPLUGGED]#036 - Max Welling: Quantum, Manifolds & Symmetries in MLDr. Minqi Jiang on curriculum learningThe Lottery Ticket Hypothesis with Jonathan Frankle