The past decade has brought about a multitude of advancements in computers and software development including IBM's unveiling of Watson. Watson, in the words of IBM, is a "technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data." In simple terms, it's a giant search engine that learns from trial and error, using key terms that are relevant to the search criteria in order to deliver the best suitable answer. This enables Watson to swiftly search through vast amounts of unstructured data which, according to IBM, is eighty percent of all data. This is possible because of artificial intelligence (AI) and machine learning.

The result is that the massive computer power so harnessed helps us to analyse what has happened in the past and, with the use of predictive analytics techniques, opens a window leading to accurate predictions. Undoubtedly, artificial intelligence is fast becoming a major technology for prescriptive analytics, the step beyond predictive analytics that helps us determine how to implement and/or optimise optimal decisions. In business applications, it can assess future risks and quantify probabilities, giving us insights into how to improve market penetration, customer satisfaction, security analysis, trade execution and fraud detection and prevention, while proving indispensable in land and air-traffic control, national security and defence, not to mention a host of healthcare applications such as patientspecific treatments for diseases and illnesses. Typically, the giant search engine firm Google is a pioneer in the field of artificial intelligence, developing self-driving automobiles, smartphone assistants and other examples of machine learning, while it is no secret that Facebook founder Mark Zuckerberg and actor Ashton Kutcher recently invested 40 million in a project focusing on developing artificial brains. In science fiction films such as Matrix, we have seen how futuristic devices will facilitate facial recognition, interpret human comments and perform complex language translations.

We are excited to announce Google Cloud Platform Education Grants for computer science faculty and students. Starting today, faculty in the United States who teach courses in computer science or related subjects can apply for free credits for students to use across the full complement of Google Cloud Platform tools, without having to submit a credit card. These credits can be used anytime during the 2016-17 academic year. Consider the work of Duke University undergrad Brittany Wenger. After watching several women in her family suffer from breast cancer, Brittany used her knowledge of artificial intelligence to create Cloud4Cancer, an artificial neural network built on top of Google App Engine.

Intel wants to take on Google's Tensor Processing Unit and Nvidia's GPUs in machine learning computing with improvements to its Xeon Phi mega-chips. The company will add new features to Xeon Phi to tune it for machine learning, said Nidhi Chappell, director of machine learning at Intel.

During his 15 years as president of the Service Employees International Union, Andy Stern was a controversial figure. He suffered his share of criticism from inside and outside the union. There was, however, no disputing his success in making SEIU the largest and fastest growing union in the country and a powerful political machine that was instrumental in electing President Obama and getting the Affordable Care Act passed. During Stern's tenure as national organizing director and president, he introduced and implemented strategies of industry-wide organizing and bargaining to counter the changing reality of employers who were becoming large and international. He took SEIU out of the AFL-CIO and formed a new labor federation called Change to Win, because he felt the mainstream labor movement was too conservative about organizing and limited its power by refusing to consolidate smaller unions into bigger and more powerful ones.

Graphics-chip company NVIDIA (NASDAQ:NVDA) has dominated the market for accelerators in recent years, with its Tesla GPUs being used for both high-performanceIn this point alphabet can not compete with Apple. Tesla has become a big business for NVIDIA -- during the past 12 months, the company's data-center segment generated nearly 400 million of revenue. Intel (NASDAQ:INTC) has been eyeing this market for some time, but its Xeon Phi line of accelerator cards has so far failed to make much of an impact. Knights Landing, the latest Xeon Phi product from Intel, could change that. I first talked about Knights Landing two years ago, and following a major delay, the product is finally shipping in volume to customers.

If you have feedback, please let us know in the ads subreddit. Who would like to start a collaborative Youtube channel that provides an explanation of various research papers? Aside from the Deep Learning Hype, What are some other interesting research topics for grad students coming into the field of statistics/machine learning? If a binary classifier (neural network model) achieves 99% training accuracy with 65% validation accuracy, what to do next?

Software faults raise questions about the validity of brain studies (arstechnica.com) Is "Python Machine Learning" by Sebastian Raschka a good book?

The result is that the massive computer power so harnessed helps us to analyse what has happened in the past and, with the use of predictive analytics techniques, opens a window leading to accurate predictions. Undoubtedly, artificial intelligence is fast becoming a major technology for prescriptive analytics, the step beyond predictive analytics that helps us determine how to implement and/or optimise optimal decisions. In business applications, it can assess future risks and quantify probabilities, giving us insights into how to improve market penetration, customer satisfaction, security analysis, trade execution and fraud detection and prevention, while proving indispensable in land and air-traffic control, national security and defence, not to mention a host of healthcare applications such as patientspecific treatments for diseases and illnesses. Typically, the giant search engine firm Google is a pioneer in the field of artificial intelligence, developing self-driving automobiles, smartphone assistants and other examples of machine learning, while it is no secret that Facebook founder Mark Zuckerberg and actor Ashton Kutcher recently invested 40 million in a project focusing on developing artificial brains. In science fiction films such as Matrix, we have seen how futuristic devices will facilitate facial recognition, interpret human comments and perform complex language translations.

According to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries like Mr Elon Musk and Professor Nick Bostrom about "the singularity" - when machines become smarter than humans - have attracted millions of dollars and spawned a multitude of conferences. But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorised and advertised to. Take a small example from last year: Users discovered that Google's photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologised; it was unintentional. But similar errors have emerged in Nikon's camera software, which misread images of Asian people as blinking, and in Hewlett-Packard's Web camera software, which had difficulty recognising people with dark skin tones.