AI Salon

AI Salon

About AI Salon

AI Salon is a roughly bi-weekly event on Fridays where the AI Lab gathers to discuss high-level topics in AI and machine learning. The goal is to encourage ourselves to think beyond our individual day-to-day research, and better see how our work fits into the long-term trajectory of scientific progress, and into society as a whole. Each Salon has a different topic for discussion, which is led by two speakers: typically one expert guest, and one AI Lab member. We hold the event in the spirit of Enlightenment-era salons, with no electronics or whiteboard allowed: only free-ranging discussion among attendees, starting with a 10-minute opening statement from the two speakers.

For the 2018-2019 academic year, Andrey Kurenkov (andreyk@stanford.edu) and Michelle Lee (mishlee@stanford.edu) are the organizers of AI Salon.

Time and Place

AI Salon is typically held in Room 219 (2nd floor open area) of the Gates Computer Science building. The event is held from 4-5pm, with refreshments served from 3:45pm.

Attendance Policy

AI Salon is open to all current Stanford students, postdocs and faculty. We particularly welcome those who can bring interdisciplinary perspectives to the event. Due to space limitations, any other attendees must be invited by a current member of the AI Lab.

Occasionally, AI Salon is opened up to the public – for example the AI Salon with Yann LeCun on February 2, 2018. However, this is an exception to the usual format.

Mailing List

AI Salon announcements are automatically sent to the ai-all@cs.stanford.edu mailing list. If you would like to receive notifications about upcoming AI Salon events and are not already on the ai-all list, please join the ai-salon-announce@lists.stanford.edu mailing list.

Note: you can join the ai-salon-announce even without a @stanford.edu email address. Subscribing to the ai-salon-announce list is fully automatic; follow the link and follow the instructions. You do not need to email anyone to get on the ai-salon-announce list.

Date
Title
Speaker
03/21/2014 Has IBM’s Watson driven forward research or was it primarily an engineering accomplishment?
04/11/2014 Are we overfitting to single-query datasets? (Gabor Angeli & Olga Russakovsky)
04/25/2014 Artificial Intelligence & Intelligence Augmentation (John Markoff & Arun Chaganty)
05/16/2014 Is interaction with the physical world necessary to acquire intelligence? (David Held & Oussama Khatib)
05/30/2014 AI & Neuroscience (Michelle Green & Ben Poole)
06/13/2014 AI in Movies (Andrej Karpathy & Vivek Srikumar)
09/26/2014 Machine Learning vs. Statistics (Will Fithian & Percy Liang)
10/10/2014 What should theory look like to be useful for robotics? (Leslie Kaelbling & Arun Chaganty)
10/17/2014 Phonemes and parts of speech: Are categories and symbolic structures a fundamental fact of language or fantasy of linguists? (Chris Manning & Andrew Ng)
11/07/2014 The Language of Food (Dan Jurafsky)
11/21/2014 The Ethics of Autonomous Driving (Silvio Savarese & Christian Gerdes)
12/05/2014 The End of Determinism (Stefano Ermon & Alex Ratner)
01/23/2015 Using neuroscience to build an AI (Jeff Hawkins and Surya Ganguli)
02/06/2015 The Turing Test (Will Hamilton and Fei-Fei Li)
02/20/2015 Robotics in the Air and Space (Mykel Kochenderfer & Marco Pavone)
03/06/2015 Robotics in the Air: Part II (Mykel Kochenderfer)
03/20/2015 AI100 Initiative and the Future of AI (Eric Horvitz)
04/07/2015 Dangers of AI (attendance by invitation only) (Elon Musk)
04/17/2015 Horizontal vs. Vertical Progress in AI (Matt Ginsberg & Jacob Steinhardt)
04/24/2015 Our Filtered Lives (Grace Muzny & Timnit Gebru)
05/08/2015 The History of AI (Ed Feigenbaum & Angel Chang)
05/15/2015 The Future of Human Labor (Michael Webb & Tudor Achim)
10/02/2015 AI’s Role in Human Disconnection (Timnit Gebru & Serena Yeung)
10/16/2015 Trust in AI Techniques/Algorithms (Maneesh Agarwala & Tum Chaturapruek)
10/30/2015 How to Build a Society in a World of Creative Destruction (Nils Nilsson & Russell Stewart)
11/06/2015 What’s Right About Machine Learning? (Sham Kakade & Yash Deshphande)
11/13/2015 AI and Health Care (David Sontag & Volodymyr Kuleshov)
12/04/2015 Artificial Intelligence — Boom or Menace? (Jerry Kaplan & John Markoff)
01/08/2016 AI & Genomics (Gill Bejerano & Irene Kaplow)
01/22/2016 AI and the Legal System (Tino Cuellar & Pratiksha Thaker)
01/29/2016 Gap Between Pattern Recognition and Intelligence (Michael Black & Amir Zamir)
03/11/2016 Pattern Recognition for Symbolic Reasoning: Deep Learning and Beyond (Tudor Achim & Jacob Steinhardt)
04/01/2016 The Role of Formal Logic in Semantics (Ray Mooney & Chris Manning)
04/15/2016 What Can AI Learn From How Babies Learn? (Jitendra Malik & Zayd Enam)
04/29/2016 The Master Algorithm for AI (Pedro Domingos & Volodymyr Kuleshov)
05/13/2016 AI and Accessibility (Joshua Miele & Timnit Gebru)
05/20/2016 Lessons from the Neocortex (Ray Kurzweil & Bharath Ramsundar)
05/27/2016 Diversity in AI (Serena Yeung & Arun Chaganty)
09/30/2016 Ai and the Economy (Kevin Leyton-Brown & Jacob Steinhardt)
10/07/2016 Software Engineering for Machine Learning (Peter Norvig & Aditya Grover)
11/04/2016 The Interaction of Future AI Algorithms and Hardware (Jen-Hsun Huang & Jim Fan)
11/18/2016 Challenges in Whole-Brain Simulation (Rosa Cao & Will Allen)
12/02/2016 Will Today’s AI Technology Be Able to Transform Healthcare? (Vijay S. Pande & Bharath Ramsundar)
01/20/2017 How Can We Make ML Interpretable (Anshul Kundaje & Avanti Shrikumar)
02/03/2017 AI That Understands Emotion (Noah Goodman & Sasha Sax)
02/17/2017 AI & Internet of Things (Frank Chen & Monica Lam)
03/03/2017 Adversarial Machine Learning (Ian Goodfellow & TBD)
03/17/2017 AI and Education (Emma Brunskill & Chris Piech)
04/21/2017 Media Portrayals of AI (Abigail See & Urvashi Khandelwal)
05/12/2017 Safe Reinforcement Learning (Animesh Garg & Andreas Krause)
05/26/2017 Issues as We Democratize AI (Pietro Perona & Phil Thomas)
10/13/2017 AI and Great Power Politics (Allan Dafoe & Ashwin Paranjape)
11/03/2017 Challenges of Human-Centered Assistive Robotics (Maja Matarić & Allison Okamura)
11/17/2017 Is General AI the Right Goal? (Aditya Grover & Steve Mussmann)
12/01/2017 AI and the Brain (Liqun Luo & Fei-Fei Li)
01/12/2018 AI in the Cloud (Ruchir Puri & Fei-Fei Li)
02/02/2018 What innate priors should we build into the architecture of deep learning systems? (Yann LeCun & Chris Manning)
03/02/2018 AI, Employment and Inequality (Erik Brynjolfsson & Trevor Standley)
03/16/2018 Should AI researchers divert efforts away from competitions toward controlled experiments? (Pat Langley & Andrey Kurenkov)
04/06/2018 How can we make AI more accessible? (Rachel Thomas & Mark Whiting)
05/11/2018 How can AI cure cancer? (Marty Tenenbaum & Kim Branson)
05/25/2018 Learning to learn (Ilya Sutskever & Amir Zamir)
10/19/2018 Ethics in ML Systems (Ben Recht & Emma Brunskill)
11/02/2018 Autonomous Driving – how close are we? (Lex Frideman & TBD)
11/09/2018 Best Practices for Ethical AI Research (Timnit Gebru & Margaret Mitchell)
11/15/2018 Deep RL for real world systems (Sergey Levine & Mykel Kochenderfer)