In this special guest feature, Debra Goldfarb from Intel writes that her recent panel discussion at SC16 illustrated just how fast Artificial Intelligence is advancing all around us. Computational science has come a long way with machine learning (ML) and deep learning (DL) in just the last year. Leading centers of high-performance computing are making great strides in developing and running ML/DL workloads on their systems. Users and algorithm scientists are continuing to optimize their codes and techniques that run their algorithms, while system architects work out the challenges they still face on various system architectures. At SC16, I had the honor of hosting three of HPC's thought leaders in a panel to get their ideas about the state of Artificial Intelligence (AI), today's challenges with the technology, and where it's going. My guests were Nick Nystrom from Pittsburgh Supercomputing Center (PSC), Ivan Rodero from Rutgers University, and Prabhat from NERSC at Berkeley National Laboratory. They answered both questions I put to them and from the audience.

Apple is known for its secrecy when it comes to ongoing product development, but the company is operating a little different with its efforts around artificial intelligence and machine learning. Apple's director of machine learning, Carlos Guestrin, openly discussed Apple's plans to grow its engineering presence in Seattle in a new GeekWire interview. Guestrin joined Apple through the company's acquisition of AI firm Turi last fall, and now the University of Washington is naming a $1 million professorship after him to help discover and support new talent in the machine learning field: A new $1 million endowed professorship, made possible by Apple's acquisition of Seattle-based machine learning startup Turi last year, will give the University of Washington Computer Science & Engineering department a chance to attract more top talent in the field of machine learning and artificial intelligence. In an interview at Apple's downtown Seattle engineering office, Guestrin said it was important to him and the Turi team to support the University of Washington's computer science and engineering program, allotting the funding as part of the acquisition process. "It's another way to foster the university's development," Guestrin said.

We've traditionally seen machine learning interview questions pop up in several categories. The first really has to do with the algorithms and theory behind machine learning. You'll have to show an understanding of how algorithms compare with one another and how to measure their efficacy and accuracy in the right way. The second category has to do with your programming skills and your ability to execute on top of those algorithms and the theory. The third has to do with your general interest in machine learning: you'll be asked about what's going on in the industry and how you keep up with the latest machine learning trends. Finally, there are company or industry-specific questions that test your ability to take your general machine learning knowledge and turn it into actionable points to drive the bottom line forward. We've divided this guide to machine learning interview questions into the categories we mentioned above so that you can more easily get to the information you need when it comes to machine learning interview questions. These algorithms questions will test your grasp of the theory behind machine learning.

Alphabet Jigsaw (formerly Google Ideas) officially unveiled this morning their new tool for fighting toxic speech online, appropriately called Perspective. Powered by a deep learning model trained on more than 17 million manually reviewed reader comments provided by the New York Times, the model assigns a score to a given passage of text, rating it on a scale from 0 to 100% similar to statements that human reviewers have previously rated as "toxic." What makes this new approach from Google so different than past approaches is that it largely focuses on language rather than ideas: for the most part you can express your thoughts freely and without fear of censorship as long as you express them clinically and clearly, while if you resort to emotional diatribes and name calling, regardless of what you talk about, you will be flagged. What does this tell us about the future of toxic speech online and the notion of machines guiding humans to a more "perfect" humanity? One of the great challenges in filtering out "toxic" speech online is first defining what precisely counts as "toxic" and then determining how to remove such speech without infringing on people's ability to freely express their ideas.

Notes from Reality is a series of posts on AI and its impact on humans, what can be done today, and what may happen tomorrow. No one has all the answers, but we are trying to arrive at the right questions. In this post, I interviewed Dr. David Bray, Harvard Executive In-Residence and Eisenhower Fellow. This effort began with the post "Let the New Machine Age Begin." The process to write that led to another interview with Dr. Bray and Michael Krigsman, a noted analyst, which is posted here.

With the rise of email came the rise of spam filling inboxes. Email has become sophisticated faster than spamming technology and now, the internet's junk mail is often caught in a folder; out of sight and out of mind are messages with the subject line "Kindly get back to me urgently" and the greeting "Dear Beneficiary." There's good news for anybody who sees fake news -- not the sort that's simply true but politically difficult for the president; but actual, fake, conspiracy theory-baiting chum -- as another form of spam. At least that's what Dean Pomerleau, research scientist at Carnegie Mellon University's Robotics Institute, said recently during a panel in New York on the proliferation of fake news. We solved the spam problem using artificial intelligence, he argued, and with A.I., we can solve the problem of fake news by filtering out credible news from the misinformation.

About 20 percent of youth in the United States live with a mental health condition, according to the National Institute of Mental Health. The good news is that mental health professionals have smarter tools than ever before, with artificial intelligence-related technology coming to the forefront to help diagnose patients, often with much greater accuracy than humans. A new study published in the journal Suicide and Life-Threatening Behavior, for example, showed that machine learning is up to 93 percent accurate in identifying a suicidal person. The research, led by John Pestian, a professor at Cincinnati Children's Hospital Medical Center, involved 379 teenage patients from three area hospitals. Each patient completed standardized behavioral rating scales and participated in a semi-structured interview, answering five open-ended questions such as "Are you angry?" to stimulate conversation, according to a press release from the university.

Last week, Blizzard posted a curious message in the Overwatch forums, that "24 isn't who you think." Many fans took that to mean that Blizzard was implying that Doomfist was not going to be the game's 24th hero. The character was never confirmed as next on the agenda, with fans really only speculating as such because of the whole "Terry Crews wants to voice him" saga that took place a few weeks ago. But now, Blizzard is actually teasing a new hero, and she's….an Blizzard is known for its unique takes on heroes from giant pig men to Russian bodybuilders, but a kid hero would be something new altogether.

If you're a fan of Shark Tank, then you may remember Jamie Siminoff. In September 2013, he pitched his company, then called DoorBot, in hopes of raising $700,000 for his revolutionary video doorbell. However, the sharks were not biting and Siminoff walked away without an investment. Kevin O'Leary even delivered his signature "you're dead to me" line. Today, Siminoff's company - now known as Ring - has raised $209 million from some of the world's top investors, including Kleiner Perkins, First Round, Richard Branson, Goldman Sachs, DFJ Growth, Qualcomm Ventures, American Family Insurance, Amazon Alexa Fund and Shea Ventures, among others.

Think that artificial intelligence isn't intelligent yet? Quick: What do you think of when you hear the words "artificial intelligence?" You might think of Siri or Alexa. Maybe you picture robots that are coming to steal your job. Or perhaps you think of technology that turns against its inventors and spells the end of the human race. Whatever your personal stance may be, according to recent headlines, the population is split when it comes to their belief in AI.