Google, Amazon, and Facebook are just a few companies investing heavily in machine learning – the branch of AI that allows the tech companies' computers to learn information on their own that they weren't programmed to know. Google, for example, uses its own TensorFlow machine learning systems for its Google Translate speech recognition app, Google Photos, Gmail, and its Web searches. And as these companies dive further into machine learning, they're building their own complex computers using graphics processing units (GPUs) to power them – and that could be particularly beneficial for NVIDIA. NVIDIA makes some of the most popular GPUs for gaming, but the hardware is increasingly finding its way into supercomputers. Facebook already uses NVIDIA's Tesla M40 GPU accelerators to help power its Big Sur machine learning computers.

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code.

By 2020, more than five million jobs are expected to be lost to robots and artificial intelligence. And in the next two decades, graduates will be going into jobs that don't yet exist. Anticipating this future, businesses and employers are overhauling their recruitment strategies. Job hopping has replaced the one job, one-employer career, and hybrid jobs are on the rise. Employers want recruits who have strong technical and soft skills such as empathy and flexibility.

Los Angeles, California – Indie developer and computer vision engineer, Mustafa Jaber, is pleased to announce the release of Capture Caption Lite, an AI-based app developed for iOS and Android devices. With the Capture Caption app, users can snap a photo with their smartphone or iPad and artificial intelligence will generate a word cloud using cutting-edge computer technology. These word clouds can be then downloaded to the user's image library and shared across multiple social media platforms. The brainchild of electrical engineer and image processing expert Mustafa Jaber, the app uses an artificial intelligence platform that derives information from images. This program understands the content of any image by using powerful machine-learning models, which can quickly classify images into thousands of categories.

This week's top Machine Learning stories, including robots to drive your car, diagnose your medical images, pick up your mess, and more! Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning!

A student in Ashok Goel's class last semester had a question: How long could the computer programs, or "agents," they were building take to solve problems? Since it was an online course, the student posted the question to the group discussion board. One teaching assistant replied, pointing to a portion of the assignment that set a 15 minute limit. The student clarified that their agent was running a little slow, and could take a bit longer. "It's fine if your agent takes a few minutes to run," she wrote.

Sadly, I have to admit that I counted mathematics towards the most boring and tedious subjects in High School. I was never really bad at it, but I only participated at a minimum level just to keep my grades in the "okay" range. I think the reason behind it was that I was simply missing the context. I knew that the concepts were somewhat important, but just solving equations for the purpose of solving equations (or to get good grades) never really clicked with me; physics, biology, and chemistry were so much more exciting! To me, math classes were so boring because they were missing a simple yet important ingredient: Applications.

By the end of the semester, "Jill" was reportedly answering questions with a 97 percent success rate, having learned to parse the context of queries and reply to them accurately. As Korn writes, students apparently hadn't suspected anything was unusual about the helpful interlocutor, and at least one claims he was "flabbergasted" when he learned its true nature. Goel apparently plans to repeat the performance next semester and says that he'll give his creation a new name to extend the fun (though presumably his next batch of students will having an easier time distinguishing real from fake).

Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but scaling GPs to large-scale data sets comes at the cost of approximating the uncertainty estimates. We extend Mondrian forests, first proposed by Lakshminarayanan et al. (2014) for classification problems, to the large-scale non-parametric regression setting. Using a novel hierarchical Gaussian prior that dovetails with the Mondrian forest framework, we obtain principled uncertainty estimates, while still retaining the computational advantages of decision forests. Through a combination of illustrative examples, real-world large-scale datasets, and Bayesian optimization benchmarks, we demonstrate that Mondrian forests outperform approximate GPs on large-scale regression tasks and deliver better-calibrated uncertainty assessments than decision-forest-based methods.

So basically... I've spent the past several years of my life working in biology and biomedical engineering, but I've always felt very called by math and computer science. I find biology interesting, however... academic jobs are absurdly competitive, and in industry... because biology is so fickle, and the whole point of industry is naturally to make money, the level of difficulty of the problems that most biotech companies I see are facing are not fundamental research questions. So... I'm planning on starting a PhD program in Fall of 2017, and I have the opportunity to do a computational only PhD, that might be sort of a... machine learning-y data science-y kind of gig. But it would largely be implementation/analysis, probably no theory at all. I'm trying to get a feel for the extent that a mostly analysis/implementation PhD hinder my career goals after graduate schools.