AI pioneer Yoshua Bengio explores paths forward to human-level artificial intelligence at the January 2017 Asilomar conference organized by the Future of Life .

Yoshua Bengio, Yann LeCun, Demis Hassabis, Anca Dragan, Oren Etzioni, Guru Banavar, Jurgen Schmidhuber, and Tom Gruber discuss how and when we .

A revolution in AI is occurring thanks to progress in deep learning. How far are we towards the goal of achieving human-level AI? What are some of the main .

AI pioneer Yoshua Bengio explores paths forward to human-level artificial intelligence at the January 2017 Asilomar conference organized by the Future of Life .

In January 2017, AI pioneer Yoshua Bengio explores paths forward to human-level artificial intelligence and talks about breakthroughs in creating human level .

AI pioneer Yoshua Bengio explores paths forward to human-level artificial intelligence at the January 2017 Asilomar conference organized by the Future of Life .

When Microsoft acquired deep learning startup Maluuba in January, Maluuba's highly respected advisor, the deep learning pioneer Yoshua Bengio, agreed to .

Elon Musk, Stuart Russell, Ray Kurzweil, Demis Hassabis, Sam Harris, Nick Bostrom, David Chalmers, Bart Selman, and Jaan Tallinn discuss with Max Tegmark .

During the mid-80s, Yoshua Bengio began his research in neural networks. This became the gateway to his career in artificial intelligence (AI) and deep learning (DL). In his presentation, Yoshua takes us through the evolution of DL: How it started in supervised learning, its progression from speech recognition to computer vision, till it reached human level processing and where we can expect it to go from here. At its core, of AI needs knowledge. Early models of AI failed as human knowledge, such as intuition, is implicit. DL was born as a way to let machines help intelligent decisions on its own. Empowered by increasing computational power and improved algorithms, DL and deep neural networks has advanced rapidly speech recognition, computer vision and natural language processing and visual question answering.

Yoshua Bengio (PhD in CS, McGill University, 1991), post-docs at M.I.T. (Michael Jordan) and AT&T Bell Labs (Yann LeCun), CS professor at Université de Montréal, Canada Research Chair in Statistical Learning Algorithms, NSERC Chair, CIFAR Fellow, member of NIPS foundation board and former program/general chair, co-created ICLR conference, authored two books and over 300 publications, the most cited being in the areas of deep learning, recurrent networks, probabilistic learning, natural language and manifold learning. He is among the most cited Canadian computer scientists and is or has been associate editor of the top journals in machine learning and neural networks.

This presentation took place at RE•WORK Deep Learning Summit in Boston, May 2016. View more videos from RE•WORK Summits here: http://videos.re-work.co/

RE•WORK events bring together breakthrough technology, cutting-edge science and entrepreneurship to re-work the future, finding solutions to challenges in business and society.

More about our events: https://re-work.co/events
More on this event: https://re-work.co/events/deep-learning-boston-2016

AI pioneer Yoshua Bengio explores paths forward to human-level artificial intelligence at the January 2017 Asilomar conference organized by the Future of Life Institute.

The Beneficial AI 2017 Conference: In our sequel to the 2015 Puerto Rico AI conference, we brought together an amazing group of AI researchers from academia and industry, and thought leaders in economics, law, ethics, and philosophy for five days dedicated to beneficial AI. We hosted a two-day workshop for our grant recipients and followed that with a 2.5-day conference, in which people from various AI-related fields hashed out opportunities and challenges related to the future of AI and steps we can take to ensure that the technology is beneficial.

For more information on the BAI ‘17 Conference:

https://futureoflife.org/ai-principles/

https://futureoflife.org/bai-2017/

https://futureoflife.org/2017/01/17/principled-ai-discussion-asilomar/

Presented at Activate 2018

Slides: https://www.slideshare.net/lucidworks/deep-learning-for-ai-yoshua-bengio-mila

There has been much progress in AI thanks to advances in deep learning in recent years, especially in areas such as computer vision, speech recognition, natural language processing, playing games, robotics, machine translation, etc. This presentation aims at introducing some of the core concepts and motivations behind deep learning and representation learning. Deep learning builds on many of the ideas introduced decades earlier with the connectionist approach to machine learning, inspired by the brain. These essential early contributions include the notion of distributed representation and the back-propagation algorithm for training multi-layer neural networks, but also the architecture of recurrent neural networks and convolutional neural networks. In addition to the substantial increase in computing power and dataset sizes, many modern additions have contributed to the recent successes. Thanks to soft-attention mechanisms neural nets have moved from pattern recognition devices working on vectors to general-purpose differentiable modular machines which can handle arbitrary data structures. The talk will end with a discussion of some major open problems for AI which are at the forefront of research in deep learning and reinforcement learning.

Learn more: https://activate-conf.com/

Yoshua Bengio, along with Geoffrey Hinton and Yann Lecun, is considered one of the three people most responsible for the advancement of deep learning during the 1990s, 2000s, and now. Cited 139,000 times, he has been integral to some of the biggest breakthroughs in AI over the past 3 decades.

This conversation is part of MIT 6.S099: Artificial General Intelligence. This class is free and open to everyone. Our goal is to take an engineering approach to exploring possible paths toward building human-level intelligence for a better world. Audio podcast version is available on https://lexfridman.com/ai/

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A revolution in AI is occurring thanks to progress in deep learning. How far are we towards the goal of achieving human-level AI? What are some of the main challenges ahead?

Yoshua Bengio believes that understanding the basics of AI is within every citizen’s reach. That democratizing these issues is important so that our societies can make the best collective decisions regarding the major changes AI will bring, thus making these changes beneficial and advantageous for all.

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Yoshua Bengio is one of the pioneers of Deep Learning. He is the head of the Montreal Institute for Learning Algorithms (MILA), Professor at the Université de Montréal, member of the NIPS board and co-founder of Element AI. With a PhD from McGill University (1991, Computer Science) and postdocs at MIT and AT&T Bell Labs, he holds the Canada Research Chair in Statistical Learning Algorithms, is a Senior Fellow of the Canadian Institute for Advanced Research and co-directs its program focused on deep learning. He is best known for his contributions to deep learning, recurrent nets, neural language models, neural machine translation and biologically inspired machine learning.

https://mila.umontreal.ca/en/
https://www.elementai.com/

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For more information visit http://www.tedxmontreal.com

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

Yoshua Bengio talks about artificial intelligence through deep learning at TedXMontreal. http://bit.ly/2BDTHcs

For the entire video The Rise of Artificial Intelligence through Deep Learning | Yoshua Bengio | TEDxMontreal click here: https://www.youtube.com/watch?v=uawLjkSI7Mo