Andrew Ng, Adjunct Professor & Kian Katanforoosh, Lecturer – Stanford University
https://stanford.io/3eJW8yT

Andrew Ng
Adjunct Professor, Computer Science

Kian Katanforoosh
Lecturer, Computer Science

To follow along with the course schedule and syllabus, visit:
http://cs230.stanford.edu/

Researchers and leaders from academia, hospitals, government and industry gathered for two days at the 2019 Big Data in Precision Health conference at Stanford Medicine to spark collaborations, address challenges, and identify actionable steps for using large-scale data analysis and technology to improve human health.

Jeff Dean, PhD, of Google gave a keynote talk.

For more information, visit https://bigdata.stanford.edu/

Bringing together thought leaders in large-scale data analysis and technology to transform the way we diagnose, treat and prevent disease. Visit our website at http://bigdata.stanford.edu/.

Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. Learn more at: https://stanford.io/2rf9OO3

Professor Christopher Potts & Consulting Assistant Professor Bill MacCartney, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Potts
Professor of Linguistics and, by courtesy, Computer Science
Director, Stanford Center for the Study of Language and Information
http://web.stanford.edu/~cgpotts/

Consulting Assistant Professor Bill MacCartney
Senior Engineering Manager, Apple
https://nlp.stanford.edu/~wcmac/

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Sherry Ruan
Stanford University

In the well-known two sigma problem introduced in 1984, Bloom found that students tutored by a one-on-one expert tutor achieved a learning outcome two standard deviations higher than those taught using traditional classroom methods. Since one-on-one tutoring is too costly to scale up to the majority of students, technology-based solutions have been suggested as promising solutions to simulate one-on-one human tutoring experiences. However, current automated computer-based tutors still primarily consist of learning activities with limited interactivity such as multiple-choice questions, review-and-flip flash cards, and listen-and-repeat practices. These tutors tend to be unengaging and thus their effectiveness typically relies on students’ desire to learn. With recent advances in artificial intelligence (AI), we now have the potential to create conversation-based tutoring systems with the ability to provide personalized feedback to make learning more engaging and effective and eventually help bridge the gap between one-on-one human tutoring and computer-based tutoring.

In this dissertation, I present the design, development, and testing of four AI-based conversational tutoring systems that personalize learning for adults and children. For adult learning, I present two systems: QuizBot for helping college students learn factual knowledge and EnglishBot for tutoring second language learners in speaking English. For child learning, I present two systems embedding conversational tutors into narrative stories to supplement elementary school students’ math learning: the first implemented using Wizard-of-Oz techniques and the second powered by online reinforcement learning algorithms. I conducted human evaluations with over 500 students using these tutoring systems to better understand how humans interact with AI in these educational systems. Our results show that, compared to current learning systems, conversation-based tutoring systems that leverage new natural language processing and reinforcement learning techniques to provide adaptive feedback can engage students more, motivate them to spend more time using tutoring systems, and improve student learning outcomes.

Learn more about Stanford’s Human-Computer Interaction Group: https://hci.stanford.edu
Learn about Stanford’s Graduate Certificate in HCI: https://online.stanford.edu/programs/human-computer-interaction-graduate-certificate

View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMyupDF2O00r19JsmolyXdD&disable_polymer=true”

Sponsored by the Stanford Institute for Human-Centered Artificial Intelligence (HAI), COVID-19 and AI: A Virtual Conference convened experts from Stanford and beyond to advance the understanding of the virus and its impact on society. The speakers and topics engaged the broad research community, government and international organizations and civil society, uniting a global community toward solutions to benefit all of humanity.

To develop equitable and trustworthy technology, we must understand how AI performs in practice, and guide and shape the way AI interacts with humans. Lightning talks from Juliana Bidadanure, Mark Duggan, Jennifer Pan, David Engstrom and Daniel Ho; followed by a panel discussion moderated by James Manyika and featuring Susan Athey, Erik Brynjolfsson, Kate Crawford and Tristan Harris.

The ultimate purpose of AI should be to enhance our humanity, not diminish or replace it. Diving into this subject at our Stanford HumanAI Symposium: Lightning talks from Michael Bernstein, Emma Brunskill, Serena Yeung and Dorsa Sadigh; followed by a panel discussion moderated by Eric Horvitz, Russ Altman, Justine Cassell, Fernanda Viégas and Bob Zhang.

Highlighting our work developing AI technologies inspired by the versatility and depth of human intelligence: Lightning talks by Michael Frank, Surya Ganguli and Percy Liang; followed by a panel discussion moderated by Reid Hoffman and featuring Jeff Dean, Alison Gopnik, Demis Hassabis and Chris Manning.

Panelists include Susan Athey, Erik Brynjolfsson, Kate Crawford, and Tristan Harris.

Dr. Sven Beiker
Silicon Valley Mobility, LLD

Dr. Maarten Sierhuis
Silicon Valley, Nissan North America, Inc.

October 17, 2019
Sven Beiker is a Lecturer in Management at the GSB, and the Managing Director of Silicon Valley Mobility, an independent consulting & advisory firm. He covers the electrification, automation, connectivity, and sharing of automobiles through the lens of new technologies and business models. This is reflected in his teaching at the GSB as well as in his professional engagements. Prior to his independent consulting work, he served as an Expert Consultant for mobility topics at McKinsey & Company for 2.5 years.

Dr. Beiker is also the former Executive Director of the Center for Automotive Research at Stanford, an industry affiliates program that he launched in 2008 together with Stanford Professors Gerdes, Nass, and Thrun. Before coming to Stanford, Dr. Beiker worked at the BMW Group for more than 13 years. Between 1995 and 2008 he pursued responsibilities in technology scouting, innovation management, systems design, and series development. He primarily applied his expertise to chassis and powertrain projects, which also provided him with profound insights into the industry’s processes and best practices. In addition, he worked in three major automotive and technology locations: Germany, Silicon Valley, and Detroit.

Dr. Beiker received his MS (1995) and PhD (1999) degrees in Mechanical Engineering from the Technical University in Braunschweig, Germany. He published various technical papers and holds several patents in the fields of vehicle dynamics and powertrain technology.

In this role Sierhuis has oversight of all technical innovation with responsibility to connect Silicon Valley research outcomes to all Alliance research & advanced engineering functions. Previously, as the founding Director of Nissan Research Center Silicon Valley, Sierhuis created the NRC-SV research portfolio and lead a team of researchers in Artificial Intelligence (AI) technologies for autonomous vehicles, connected vehicles and Human-Machine Interaction and Interfaces (HMI²) to help shape the future of intelligent cars capable of driving themselves.

Prior to Nissan, Sierhuis spent more than 12 years at NASA Ames Research Center, where he researched the development of human and autonomy systems for space exploration. He developed an autonomous multi-agent system for human and robotic planetary exploration that was used to automate the work of flight controllers in NASA’s Mission Control for the International Space Station. Sierhuis is one of the creators of the Brahms agent language.
Sierhuis joined Nissan in 2013. In a career in research and software engineering spanning 25 years, Sierhuis has also worked at Xerox Palo Alto Research Center, NYNEX Science & Technology, IBM Corporation, and as a founder of a startup Ejenta.

Sierhuis earned a bachelor degree in Computer Science from The Hague University and a Ph.D. in Artificial Intelligence & Cognitive Science from the University of Amsterdam, The Netherlands. He lives in San Francisco, California.

3:30 Deep Learning: Machine Learning via Large-scale Brain Simulations

51:03 Q&A

Best finals project ever? Stanford students in the ‘Introduction to Mechatronics’ course build robots to do battle, sumo wrestler-style, to display their mastery of combining mechanical, electrical and computer engineering skills.

This year’s competition, dubbed the ‘Fiscal Cliff Face-Off’, in which student-built robots, each representing a different political party, squared off on a picnic-table size platform and tried to push each other over the edge, otherwise known as the Fiscal Cliff, drew a large and enthusiastic crowd.

Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. Learn more at: https://stanford.io/3bhmLce

Andrew Ng
Adjunct Professor of Computer Science
https://www.andrewng.org/

To follow along with the course schedule and syllabus, visit:
http://cs229.stanford.edu/syllabus-autumn2018.html

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit:
http://learn.stanford.edu/AI.html

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Fei-Fei Li in conversation with Yuval Harari moderated by Nicholas Thompson

This video covers Stanford CoreNLP Example.

GitHub link for example: https://github.com/TechPrimers/core-nlp-example

Stanford Core NLP: https://stanfordnlp.github.io/CoreNLP/
Stanford API example: https://stanfordnlp.github.io/CoreNLP/api.html

Slack Community: https://techprimers.slack.com

Twitter: https://twitter.com/TechPrimers

Facebook: http://fb.me/TechPrimers

GitHub: https://github.com/TechPrimers or https://techprimers.github.io/

Video Editing: iMovie

Intro Music: A Way for me (www.hooksounds.com)

#CoreNLP #TechPrimers

Abstract:
Artificial intelligence has begun to impact healthcare in areas including electronic health records, medical images, and genomics. But one aspect of healthcare that has been largely left behind thus far is the physical environments in which healthcare delivery takes place: hospitals, clinics, and assisted living facilities, among others. In this talk I will discuss our work on endowing healthcare spaces with ambient intelligence, using computer vision-based human activity understanding in the healthcare environment to assist clinicians with complex care. I will first present pilot implementations of AI-assisted healthcare spaces where we have equipped the environment with visual sensors. I will then discuss our work on human activity understanding, a core problem in computer vision. I will present deep learning methods for dense and detailed recognition of activities, and efficient action detection, important requirements for ambient intelligence, and I will discuss these in the context of several clinical applications. Finally, I will present work and future directions for integrating this new source of healthcare data into the broader clinical data ecosystem.

Bio:
Fei-Fei Li is a Professor in the Computer Science Department at Stanford, and the Director of the Stanford Artificial Intelligence Lab. In 2017, she also joined Google Cloud as Chief Scientist of AI and Machine Learning. Dr. Li’s main research areas are in machine learning, deep learning, computer vision and cognitive and computational neuroscience. She has published almost 200 scientific articles in top-tier journals and conferences, including Nature, PNAS, Journal of Neuroscience, New England Journal of Medicine, CVPR, ICCV, NIPS, ECCV, IJCV, and IEEE-PAMI. Dr. Li obtained her B.A. degree in physics from Princeton in 1999 with High Honors, and her PhD degree in electrical engineering from California Institute of Technology (Caltech) in 2005. She joined Stanford in 2009 as an assistant professor, and was promoted to associate professor with tenure in 2012. Prior to that, she was on faculty at Princeton University (2007-2009) and University of Illinois Urbana-Champaign (2005-2006).

Dr. Li is the inventor of ImageNet and the ImageNet Challenge, a critical large-scale dataset and benchmarking effort that has contributed to the latest developments in computer vision and deep learning in AI. In addition to her technical contributions, she is a national leading voice for advocating diversity in STEM and AI. She is a co-founder of Stanford’s renowned SAILORS outreach program for high school girls and the national non-profit AI4ALL. For her work in AI, Dr. Li has been a keynote speaker at the Grace Hopper Conference in 2017 and TED2015 main conference. She is a recipient of the 2017 Athena Academic Leadership Award, IAPR 2016 J.K. Aggarwal Prize, the 2016 NVIDIA Pioneer in AI Award, 2014 IBM Faculty Fellow Award, 2011 Alfred Sloan Faculty Award, 2012 Yahoo Labs FREP award, 2009 NSF CAREER award, the 2006 Microsoft Research New Faculty Fellowship, and a number of Google Research awards. Work from Dr. Li’s lab have been featured in a variety of popular press magazines and newspapers including New York Times, Wall Street Journal, Fortune Magazine, Science, Wired Magazine, MIT Technology Review, Financial Times, and more. She was selected as a 2017 Women in Tech by the ELLE Magazine, a 2017 Awesome Women Award by Good Housekeeping, a Global Thinker of 2015 by Foreign Policy, and one of the “Great Immigrants: The Pride of America” in 2016 by the Carnegie Foundation (past winners include Albert Einstein, Yo-Yo Ma, Sergey Brin and more).

#TuringSeminars

Bringing together thought leaders in large-scale data analysis and technology to transform the way we diagnose, treat and prevent disease. Visit our website at http://bigdata.stanford.edu/.

A common goal for the brightest minds from Stanford and beyond: putting humanity at the center of AI. Watch the Stanford HumanAI Symposium, featuring Bill Gates, Kate Crawford, Reid Hoffman, California Governor Gavin Newsom and many more gathered to discuss our shared dream of a better future.

Panelists include Jeff Dean, Alison Gopnik, Demis Hassabis, and Chris Manning.

Co-chair of the Gates Foundation, Bill Gates sits down with AI4ALL alumni for a special conversation.

Fei-Fei Li came to the U.S. from China at 16 with a love for science and she never looked back. Educated at Princeton and Caltech, her early work in robotics revolutionized machine learning and AI. Her focus on inclusion in tech careers and diversity in what we teach machines suggests that tomorrow’s robots won’t be sexist.

Professor Christopher Manning, Stanford University & Margaret Mitchell, Google AI
http://onlinehub.stanford.edu/

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224n/index.html#schedule

To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html

To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu

Bringing together thought leaders in large-scale data analysis and technology to transform the way we diagnose, treat and prevent disease. Visit our website at http://bigdata.stanford.edu/.

Fei-Fei Li came to the U.S. from China at 16 with a love for science and she never looked back. Educated at Princeton and Caltech, her early work in robotics revolutionized machine learning and AI. Her focus on inclusion in tech careers and diversity in what we teach machines suggests that tomorrow’s robots won’t be sexist.

makers.com/techmakers

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