047 Interpretable Machine Learning - Christoph Molnar
Christoph Molnar is one of the main people to know in the space of interpretable ML. In 2018 he released the first version of his incredible online book, interpretable machine learning. Interpretabili...
14 Maalis 20211h 40min
#046 The Great ML Stagnation (Mark Saroufim and Dr. Mathew Salvaris)
Academics think of themselves as trailblazers, explorers — seekers of the truth. Any fundamental discovery involves a significant degree of risk. If an idea is guaranteed to work then it moves from th...
6 Maalis 20211h 39min
#045 Microsoft's Platform for Reinforcement Learning (Bonsai)
Microsoft has an interesting strategy with their new “autonomous systems” technology also known as Project Bonsai. They want to create an interface to abstract away the complexity and esoterica of dee...
28 Helmi 20212h 30min
#044 - Data-efficient Image Transformers (Hugo Touvron)
Today we are going to talk about the *Data-efficient image Transformers paper or (DeiT) which Hugo is the primary author of. One of the recipes of success for vision models since the DL revolution beg...
25 Helmi 202152min
#043 Prof J. Mark Bishop - Artificial Intelligence Is Stupid and Causal Reasoning won't fix it.
Professor Mark Bishop does not think that computers can be conscious or have phenomenological states of consciousness unless we are willing to accept panpsychism which is idea that mentality is fundam...
19 Helmi 20211h 35min
#042 - Pedro Domingos - Ethics and Cancel Culture
Today we have professor Pedro Domingos and we are going to talk about activism in machine learning, cancel culture, AI ethics and kernels. In Pedro's book the master algorithm, he segmented the AI com...
11 Helmi 20211h 33min
#041 - Biologically Plausible Neural Networks - Dr. Simon Stringer
Dr. Simon Stringer. Obtained his Ph.D in mathematical state space control theory and has been a Senior Research Fellow at Oxford University for over 27 years. Simon is the director of the the Oxford C...
3 Helmi 20211h 26min
#040 - Adversarial Examples (Dr. Nicholas Carlini, Dr. Wieland Brendel, Florian Tramèr)
Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. there's good reason to believe neural networks look...
31 Tammi 20211h 36min
