Introduction to Deep Learning and Self-Driving Cars from MIT

It doesn’t matter if you are beginner or new to machine learning or advanced researcher in the field of deep learning methods and their application, everybody can benefit of Lex Fridman’s course on Deep Learning for Self-Driving Cars. If you are interested in this course, you can go to  http://selfdrivingcars.mit.edu/  and Register an account on the site to stay up-to-date. The material for the course is free and open to the

Dr. Harry Shum on the future of AI at the 2017 Future Forum

Before computers can solve our greatest challenges, they must understand us. How can we make the leap from computers that perceive and identify objects or just words to a future where they comprehend and interpret the larger context of our world? How will the task-oriented bots of today evolve to agents that connect with us emotionally and improve our lives and our society? Artificial Intelligence will be the pivotal technology –

The Long-Term Future of Artificial Intelligence

Professor Stuart J. Russell (University of California, Berkeley) delivered a public lecture on Friday 15th May 2015 at the Centre for the Study of Existential Risk on the long-term future of Artificial Intelligence. The news media in recent months have been full of dire warnings about the risk that AI poses to the human race, coming from well-known figures such as Stephen Hawking, Elon Musk, and Bill Gates. Should we be

The State of Artificial Intelligence

Professor Jennifer Neville, Associate Professor and Miller Family Chair of Computer Science and Statistics at Purdue University, talked about the history and future of artificial intelligence and about machine dialog systems called “Chatbots.” Professor Neville focuses her research on machine learning and data mining. Professor Neville’s research focuses on data mining and machine learning techniques for relational data. In relational domains such as social network analysis, citation analysis, epidemiology, fraud

Deep Learning Lectures at the University of Oxford by Nando de Freitas

Deep Learning Courses were taught in 2015 at the University of Oxford by Nando de Freitas with great help from Brendan Shillingford. Slides are available at: https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/ Deep Learning Lecture 1: Introduction Deep Learning Lecture 2: linear models Deep Learning Lecture 3: Maximum likelihood and information Deep Learning Lecture 4: Regularization, model complexity and data complexity (part 1) Deep Learning Lecture 5: Regularization, model complexity and data complexity (part 2) Deep

AI will be a driver for our future

Dr. Fei Fei Li, the Director of Stanford’s Artificial Intelligence Lab, took the stage at Innovate! and Celebrate to welcome entrepreneurs, founders, and attendees to the conference with an in-depth discussion of artificial intelligence and explored how AI will be a driver for our future. She discusses what AI technology is already being developed, the challenges scientists are still facing, and the potential consequences for every industry and almost every facet of

What is Machine Learning?

The goals of AI is to create a machine which can mimic a human mind and to do that it needs learning capabilities. But how does machine learning work? Technically speaking, machine learning is all about testing, testing, testing. It starts with training data and then it applies predictions to what you might like. When you make a predicted selection, your answer is so noted. Machine learning is a subfield

Unsupervised Learning :The Next Frontier in AI

Yann LeCun, Director of AI Research at Facebook and Professor of Computer Science at New York University is speaking on the next frontier in Artificial Intelligence. The rapid progress of AI in the last few years are largely the result of advances in deep learning and neural nets, combined with the availability of large datasets and fast GPUs. We now have systems that can recognize images with an accuracy that rivals