Machine Learning Street Talk (MLST)
Podcast készítő Machine Learning Street Talk (MLST)

Kategóriák:
217 Epizód
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#56 - Dr. Walid Saba, Gadi Singer, Prof. J. Mark Bishop (Panel discussion)
Közzétéve: 2021. 07. 08. -
#55 Self-Supervised Vision Models (Dr. Ishan Misra - FAIR).
Közzétéve: 2021. 06. 21. -
#54 Gary Marcus and Luis Lamb - Neurosymbolic models
Közzétéve: 2021. 06. 04. -
#53 Quantum Natural Language Processing - Prof. Bob Coecke (Oxford)
Közzétéve: 2021. 05. 19. -
#52 - Unadversarial Examples (Hadi Salman, MIT)
Közzétéve: 2021. 05. 01. -
#51 Francois Chollet - Intelligence and Generalisation
Közzétéve: 2021. 04. 16. -
#50 Christian Szegedy - Formal Reasoning, Program Synthesis
Közzétéve: 2021. 04. 04. -
#49 - Meta-Gradients in RL - Dr. Tom Zahavy (DeepMind)
Közzétéve: 2021. 03. 23. -
#48 Machine Learning Security - Andy Smith
Közzétéve: 2021. 03. 16. -
047 Interpretable Machine Learning - Christoph Molnar
Közzétéve: 2021. 03. 14. -
#046 The Great ML Stagnation (Mark Saroufim and Dr. Mathew Salvaris)
Közzétéve: 2021. 03. 06. -
#045 Microsoft's Platform for Reinforcement Learning (Bonsai)
Közzétéve: 2021. 02. 28. -
#044 - Data-efficient Image Transformers (Hugo Touvron)
Közzétéve: 2021. 02. 25. -
#043 Prof J. Mark Bishop - Artificial Intelligence Is Stupid and Causal Reasoning won't fix it.
Közzétéve: 2021. 02. 19. -
#042 - Pedro Domingos - Ethics and Cancel Culture
Közzétéve: 2021. 02. 11. -
#041 - Biologically Plausible Neural Networks - Dr. Simon Stringer
Közzétéve: 2021. 02. 03. -
#040 - Adversarial Examples (Dr. Nicholas Carlini, Dr. Wieland Brendel, Florian Tramèr)
Közzétéve: 2021. 01. 31. -
#039 - Lena Voita - NLP
Közzétéve: 2021. 01. 23. -
#038 - Professor Kenneth Stanley - Why Greatness Cannot Be Planned
Közzétéve: 2021. 01. 20. -
#037 - Tour De Bayesian with Connor Tann
Közzétéve: 2021. 01. 11.
Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).