Among techniques for data analysis and automation, machine learning has received strong attention the last few years. This is mainly due to the recent breakthroughs within deep learning, but has quite rightfully renewed interest also in more simple and approachable techniques. In this webinar we introduce you to what machine learning is, focus on some of the fundamental concepts and see examples of how it works in SAS.

The first myth regards the timeline: how long will it take until machines greatly supersede human-level intelligence? A common misconception is that we know the answer with great certainly. One popular myth is that we know we'll get superhuman AI this century. In fact, history is full of technological over-hyping. Where are those fusion power plants and flying cars we were promised we'd have by now?

The following article and talk are by Jessica Taylor and they were originally posted on MIRI. At the EA Global 2016 conference, I gave a talk on "Using Machine Learning to Address AI Risk": It is plausible that future artificial general intelligence systems will share many qualities in common with present-day machine learning systems. If so, how could we ensure that these systems robustly act as intended? We discuss the technical agenda for a new project at MIRI focused on this question. The talk serves as a quick survey (for a general audience) of the kinds of technical problems we're working on under the "Alignment for Advanced ML Systems" research agenda. Included below is a version of the talk in blog post form.1 This talk is about a new research agenda aimed at using machine learning to make AI systems safe even at very high capability levels.

Importance Principle: Advanced AI could represent a profound change in the history of life on Earth, and should be planned for and managed with commensurate care and resources. In the history of human progress, a few events have stood out as especially revolutionary: the intentional use of fire, the invention of agriculture, the industrial revolution, possibly the invention of computers and the Internet. But many anticipate that the creation of advanced artificial intelligence will tower over these achievements. In a popular post, Tim Urban with Wait But Why wrote that artificial intelligence is "by far THE most important topic for our future." Or, as AI professor Roman Yampolskiy told me, "Design of human-level AI will be the most impactful event in the history of humankind. It is impossible to over-prepare for it."

E-mail them to Alain Thériault Your question may become the question of the month!

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Deep learning is becoming an increasingly important part of the artificial intelligence (AI) toolkit, yet it is often misunderstood. Although it supports a developing market and is often touted as an important direction for corporate innovation, it needs to be viewed in context. This is a developing area of technology that is rapidly creating its own domain, becoming richer and increasingly varied. We will soon see new opportunities based specifically on deep learning. The market for deep learning solutions continues to expand.

The stunning advancement of artificial intelligence and machine learning has brought advances in society. These technologies have improved medicine and how quickly doctors can diagnose disease, for example. IBM's AI platform Watson helps reduce water waste in drought stricken areas. AI even entertains us--the more you use Netflix, the more it learns what your viewing preferences are and makes suggestions based on what you like to watch. However, there is a very dark side to AI, and it's worrying many social scientists and some in the tech industry.

Making TVs "smart" is one thing, but being able to use them smartly and to their full potential is another. Smart TVs have come a long way since Samsung first introduced theirs as a "connected-TV" back in 2009. In 2011, Samsung was the first to use the term "Smart TV" that converged the computer and its integrated internet, a conventional TV, and set-top boxes. TVs remain the primary gateway for easy content consumption in the home, despite the fact that it no longer holds a monopoly thanks to the compatibility of PCs, tablets, and smartphones for viewing content. Gimmicky features and other functions that overlapped with other screened devices introduced in earlier launches have disappeared, and TV remains the best device to watch content.

Unsupervised learning, in particular learning general nonlinear representations, is one of the deepest problems in machine learning. Estimating latent quantities in a generative model provides a principled framework, and has been successfully used in the linear case, e.g. with independent component analysis (ICA) and sparse coding. However, extending ICA to the nonlinear case has proven to be extremely difficult: A straight-forward extension is unidentifiable, i.e. it is not possible to recover those latent components that actually generated the data. Here, we show that this problem can be solved by using temporal structure. We formulate two generative models in which the data is an arbitrary but invertible nonlinear transformation of time series (components) which are statistically independent of each other.