Attributes of Participatory Culture @TransformSoc (Henry Jenkins) • Affiliations: online communities • Expressions: new creative forms • Collaborations: Problem-solving in teams • Circulations: Shaping media flow Source: Confronting The Challenges Of Participatory Culture, by Henry Jenkins, MIT Press, 2009 31.

John McCarthy coined the term Artificial Intelligence in 1955. Since then, the AI industry at large has seen dramatic ups and downs -- progress and promise mixed with disappointment and disillusion. But now with the convergence of Megatrends on massive data, lightning fast processing speeds, and renewed competitive fever from the American MAFIA (Microsoft, Alphabet, Facebook, IBM, Amazon), AI is poised to cause disruption on a scale that could surpass the Internet itself. As we prepare for a wave of AI first companies (@sundarpichai) and AI natives (Ryan Hoover), every person in the innovation economy will need to understand how AI will (or will not) change their industry and their lives. These titans shape the conversation and have the most ability to move the entire AI industry.

Matrix completion is a basic machine learning problem that has wide applications, especially in collaborative filtering and recommender systems. Simple non-convex optimization algorithms are popular and effective in practice. Despite recent progress in proving various non-convex algorithms converge from a good initial point, it remains unclear why random or arbitrary initialization suffices in practice. We prove that the commonly used non-convex objective function for \textit{positive semidefinite} matrix completion has no spurious local minima --- all local minima must also be global. Therefore, many popular optimization algorithms such as (stochastic) gradient descent can provably solve positive semidefinite matrix completion with \textit{arbitrary} initialization in polynomial time. The result can be generalized to the setting when the observed entries contain noise. We believe that our main proof strategy can be useful for understanding geometric properties of other statistical problems involving partial or noisy observations.

Classrooms are noticeably more hi-tech these days - interactive boards, laptops and online learning plans proliferate, but has the curriculum actually changed or are children simply learning the same thing on different devices? Some argue that the education this generation of children is receiving is little different from that their parents or even their grandparents had. But, in a world where artificial intelligence and robots threaten jobs, the skills that this generation of children need to learn are likely to be radically different to the three Rs that have for so long been the mainstay of education. The BBC went along to the Bett conference in London in search of different ways of teaching and learning. A stone's throw from the Excel, where Bett is held, stands a new school that is, according to its head Geoffrey Fowler, currently little more than a Portakabin.

Recently, I have been working on the Neural Networks for Machine Learning course offered by Coursera and taught by Geoffrey Hinton. Overall, it is a nice course and provides an introduction to some of the modern topics in deep learning. However, there are instances where the student has to do lots of extra work in order to understand the topics covered in full detail. One of the assignments in the course is to study the Neural Probabilistic Language Model (The related article can be downloaded from here). An example dataset, as well as a code written in Octave (equivalently Matlab) are provided for the assignment.

How is Bayes' Theorem used in artificial intelligence and machine learning? Is there any good book that you can recommend? As an high school student I will be writing an essay about it, and I want to use the best sources that I can find. I need a source that explains bayes' theorem, its general use and how it is used in AI or ML?

When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning. These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level. Reinforcement learning has recently become popular for doing all of that and more. Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible. In 2016 we saw Google's AlphaGo beat the world Champion in Go. We saw AIs playing video games like Doom and Super Mario.

They are wise-cracking companions, able to communicate in more than six million languages. Others are bent on enslaving or destroying humanity, deeming themselves better, more rational caretakers of the Earth in light of our irrational behaviors. Pilot or garbage man, soldier or slave, hero or villain--robots have played every role imaginable in popular science fiction for nearly a century. In the 21st century, real-life robots inspired by their fictional counterparts are beginning to take starring roles in everyday life. Several companies, Google among them, are testing autonomous cars (unfortunately, there is no indication that they will be able to travel into the past or future anytime soon).

A short while ago a recent article in Wired described how physicists are about to rule Silicon Valley. The opening of the article resonated strongly with me when I also used to tackle difficult research questions at the world's most renown laboratories for particle physics: CERN in Geneva, Switzerland and the Fermi National Laboratory in Chicago, IL. For more than 10 years I've tried to uncover the origins of our Universe before transitioning to the private sector and joining Blue Yonder, a cloudbased company that delivers automated, machine learning solutions in the Retail space. Why did I make this move? The LHC has yet to discover anything new – even the Higgs boson discovered in 2012 was about the same mass it was previously expected to be.

"Nanodegree" is a registered trademark of Udacity. Udacity is not an accredited university and we don't confer traditional degrees. Udacity Nanodegree programs represent collaborations with our industry partners who help us develop our content and who hire many of our program graduates.