In 2001, when Jamie was diagnosed with chronic myelogenous leukemia (CML), a cancer that starts inside the bone marrow, the disease had few effective cures. Fourteen years later, thanks to advances in cancer treatment, she is able to manage the disease and live a full life. Jamie is profiled in the PhRMA.org Yet many patients and their doctors wait for years before promising treatments become available. All too often, unforeseen side effects send researchers back to the drawing board, just when they thought they were close to bringing a new medication to market.

I just noticed that Justin Domke has a blog -- He's one of the strongest researchers in the field of graphical models. I first came across his dissertation when looking for a way to improve loopy-Belief Propagation based training. His thesis gives one such idea -- instead of maximizing the fit of an intractable model, and using BP as intermediate step, maximize the fit of BP marginals directly. This makes sense since approximate (BP-based) marginals are what you ultimately use. If you run BP for k steps, then likelihood of the BP-approximated model is tractable to minimize -- calculation of gradient is very similar to k steps of loopy BP.

What does it mean to be intelligent? Why do humans perceive their environment the way they do? These are all questions that artificial intelligence will, over time, help scientists answer about the human experience. And Richard Sutton, a professor of computer sciences at the University of Alberta, has been a significant contributor to the discussion around the limits and promise of artificial intelligence. He is a respected voice on "reinforcement learning," or the fundamental process that allows AI software to respond to (and ultimately learn from) its environment.

Artificial intelligence is no longer the stuff of the future. It is becoming an increasingly common part of our everyday lives, from the watches we wear that monitor our heart rate to the robots that vacuum our floors without any direction from us. Yet we have traditionally thought of artificial intelligence as similar to a robot that has a mind of its own and can act independently. While these robots would certainly qualify, they are not the only examples. Google - the leader in almost all things Internet-related - has introduced RankBrain, which is a machine learning algorithm. It is an example of ANI, just like the spam filters in your email or the "recommended products" feature on Amazon.

The story starts in Paris in 1989, when I benchmarked neural networks against kernel methods, but the real invention of SVMs happened when Bernhard decided to implement Vladimir Vapnik algorithm.

I'm really fond of Bayesian methods, so I decided to spend some time wrapping my head around modern Bayesian ideas, especially combined with Deep Learning. I'm mostly interested in Variational Inference at the moment, so my reading list is I hope to write a blogpost (or maybe a series of?) summarizing all these works and putting them in a common context. BTW, if you know interesting papers that marry Bayesian methods with Deep Learning -- I'd be interested to hear about them.

Google is hiring and there are lots of opportunities to do Machine Learning-related work here. Kevin Murphy is applying Bayesian methods to video recommendation, Andrew Ng is working on a neural network that can run on millions of cores, and that's just the tip of the iceberg that I've discovered working here for last 3 months. There is machine learning work in both "researcher" and "engineer" positions, and the focus on applied research makes the distinction somewhat blurry.

As card not present (CNP) transactions grow, online fraud is growing too, and with EMV migration in the US it is expected to grow even more. No wonder fraud is so high on merchants' list of concerns! Next to omnichannel, big data and cross-border, fraud was also one of the main keywords of the CNP EXPO 2016 edition. Amongst the top challenges in fighting fraud is the immense volumes and velocity of data generated by the big brands. This poses new, unique problems but it also creates opportunities to transform business and create competitive advantage.

Physical beauty is subjective and often difficult to define. But for the robot jury of Beauty.AI, an online competition billed as "the first international beauty contest judged by artificial intelligence," beauty is calculated by a set of complex algorithms that measure parameters like participants' facial symmetry and skin quality. The contest, launched in December, is an experiment by Youth Laboratories, an international team of data scientists and biogerontologists interested in developing anti-aging technologies. Its aim is to test and demonstrate how computers can learn to assess human attractiveness. The robot jury uses algorithms to analyze and rate participants' selfies submitted through the Beauty.AI app.

Many people think that the healthcare sector will greatly benefit from the Fourth Industrial Revolution. Professor Klaus Schwab, founder and executive Chairman of the World Economic Forum, describes this revolution as "a fusion of technologies that is blurring the lines between the physical, digital and biological spheres." Many of the discussions around technology and healthcare have, in broad terms, focused on the internet of things, telemedicine, personalized medicine, and robotics. But how exactly are these technologies going to be transformative? As the Chair of the Global Agenda Council on Artificial Intelligence and Robotics, I've been part of many discussions on the impacts of these technologies, both good and bad.