March 31, 2015
Harvard University

The Future of Life with AI, Nuclear Weapons, and Other Powerful Technologies

Prof. Max Tegmark will explore how we humans have repeatedly underestimated not only the size of our cosmos (and hence our future opportunities), but also the power of our humans minds to understand it and develop technologies with the power to enrich or extinguish humanity.

Known as “Mad Max” for his unorthodox ideas and passion for adventure, Max Tegmark’s scientific interests range from precision cosmology to the ultimate nature of reality, all explored in his new popular book “Our Mathematical Universe.”

He is an MIT physics professor with more than two hundred technical papers and has featured in dozens of science documentaries. His work with the SDSS collaboration on galaxy clustering shared the first prize in Science magazine’s “Breakthrough of the Year: 2003.” He is also the president of the Future of Life Institute, which is launching a major research program supported by Elon Musk aimed at keeping artificial intelligence beneficial.

Human Computer Integration versus Powerful Tools
Umer Farooq, Jonathan Grudin, Ben Shneiderman, Pattie Maes, Xiangshi Ren

CHI ’17: ACM CHI Conference on Human Factors in Computing Systems
Session: Human Computer Integration versus Powerful Tools

Abstract
In 1960, JCR Licklider forecast three phases for how humans relate to machines: human-computer interaction, human-computer symbiosis, and ultra-intelligent machines. Have we moved from interaction to symbiosis or integration, should we focus on this or on other aspects of human augmentation via powerful tools, and how will such decisions affect us as designers, researchers, and members of society? This panel will raise uneasy and disruptive HCI notions. For example, we will debate whether integration is a necessary and desirable next phase, or whether it could undermine human self-efficacy and control and lessen the predictability of machine actions.

DOI:: http://dx.doi.org/10.1145/3027063.3051137
WEB:: https://chi2017.acm.org/

Recorded at the ACM CHI Conference on Human Factors in Computing Systems in Denver, CO, USA May 6-11, 2017

Statement by Anya Daneez Khan, age 10, on the occasion of the International Day of Women and Girls in Science (11 February).
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February 11th marks the International Day of Women and Girls in Science. The theme for 2019 is “Investment in Women and Girls for Inclusive Green Growth,” and a two-day event began today (11 Feb) at UN Headquarters in New York, bringing together global experts and leaders to evaluate the economic and social impact of women’s participation in science-based sustainable development programmes.

The event featured a high-level panel focusing on the public-sector financing of science for green growth, investment to attract and retain high calibre women in science, and financing to ensure gender equality in science.

Anya Daneez Khan, a girl in the field of science, said “The reason we celebrate this Day is to make sure it becomes not a story about exceptional women but a norm that girls belong and succeed in science and technology.”

The United Nations General Assembly in 2015 declared 11 February as the International Day of Women and Girls in Science in order to achieve full and equal access to and participation in science for women and girls, and further achieve their empowerment as well as gender equality.

This video provides a list of YouTube videos and playlists, which you can use to improve your knowledge in the areas of machine learning and deep learning. Below are the links to each video/playlist:

David Silver’s Videos on Reinforcement Learning:
https://www.youtube.com/playlist?list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT

SysML 18: Michael Jordan, Perspectives and Challenges:
https://www.youtube.com/watch?v=4inIBmY8dQI

Deep Probabilistic Methods with PyTorch – Chris Ormandy:
https://www.youtube.com/watch?v=HNKlytVD1Zg&list=PLUeNEwyehBMmpTRJxhmJWDowHXU7W3ZkU&index=15&t=0s

Geoffrey Hinton talk “What is wrong with convolutional neural nets ?”:
https://www.youtube.com/watch?v=rTawFwUvnLE&t=77s

NIPS 2016 – Generative Adversarial Networks – Ian Goodfellow:
https://www.youtube.com/watch?v=AJVyzd0rqdc

Heroes of Deep Learning: Andrew Ng interviews Geoffrey Hinton:
https://www.youtube.com/watch?v=-eyhCTvrEtE&list=PLfsVAYSMwsksjfpy8P2t_I52mugGeA5gR

Yann LeCun – Power & Limits of Deep Learning:
https://www.youtube.com/watch?v=0tEhw5t6rhc

DeepMind’s Richard Sutton – The Long-term of AI & Temporal-Difference Learning:
https://www.youtube.com/watch?v=EeMCEQa85tw

The Future of Robotics and Artificial Intelligence (Andrew Ng, Stanford University, STAN 2011):
https://www.youtube.com/watch?v=AY4ajbu_G3k&feature=youtu.be

Using Python to Code by Voice:
https://www.youtube.com/watch?v=8SkdfdXWYaI

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