A market basket analysis or recommendation engine [1] is what is behind all these recommendations we get when we go shopping online or whenever we receive targeted advertising. The underlying engine collects information about people's habits and knows that if people buy pasta and wine, they are usually also interested in pasta sauces. So, the next time you go to the supermarket and buy pasta and wine, be ready to get a recommendation for some pasta sauce! A typical analysis goal when applying market basket analysis it to produce a set of association rules in the following form: IF {pasta, wine, garlic} THEN pasta-sauce The first part of the rule is called "antecedent", the second part is called "consequent". A few measures, such as support, confidence, and lift, define how reliable each rule is.

On Thursday, we asked Google about its new custom-made chip for artificial intelligence called a Tensor Processing Unit, or TPU. Google politely declined to answer Recode's questions, saying only that "more information is coming later." Later in the day, after we posted our questions, Google changed its mind and gave us some short answers to a few -- but not all -- of our questions, via an email from a spokesperson. This is a big one that Google did not answer. It probably is pre-trained, but we don't know for sure.

Today, Google convened 7,000 developers and journalists at a popular outdoor concert venue in Silicon Valley and gave a two-hour state of the union on the future of arguably the most powerful and ambitious company in the world. In front of a packed crowd and tens of thousands watching via live stream, the company brought its best and brightest minds on stage and unveiled to the world that Google is playing catch-up with a slew of products and services we've already seen before from other companies. There's Google Assistant, a conversational AI chat and search bot (Facebook's M/Microsoft's bot projects/Apple's Siri/Viv/literally everyone has a bot these days); Google Home, a voice-powered home entertainment and task hub (Amazon's dreadfully popular and beloved Echo); Allo and Duo, two mobile messaging and mobile video apps (Facebook's Messenger goliath with nearly 1 billion users); and Daydream, Google's Android-powered virtual reality platform, headset, and multimedia content hub (Oculus). But make no mistake, Google, led by its fresh-faced, immaculately tailored blue jacket–wearing CEO, Sundar Pichai, didn't tiptoe around the stage today. Quite the opposite, Google's 2016 I/O keynote address was confident, enthusiastic, and more than just a little impressive.

Between the University of Washington, a thriving tech community, and strong research institutions, like the Allen Institute for Artificial Intelligence (AI2), many of the rapid developments in AI are playing out in Seattle. Perhaps that's why the White House has selected the Emerald City for its first public workshop on artificial intelligence. The Office of Science and Technology Policy will co-host the first of four events on artificial intelligence at the University of Washington May 24. The workshop, put on by the UW's Tech Policy Lab and School of Law, will explore issues such as policy, logistical applications, and safety, as they relate to AI. Speakers include AI2 CEO and UW Professor of Computer Science and Engineering Oren Etzioni, White House Deputy U.S. CTO Edward Felten, Microsoft Principal Researcher Kate Crawford, and other industry experts. The workshops are intended "to spur public dialogue on artificial intelligence and machine learning and identify challenges and opportunities related to this emerging technology," writes Felton in a White House blog post.

Paul Herzlich works in Google's legal department and helped develop a special sensor for "pressure sores" by those who use wheelchairs. Paul Herzlich works in Google's legal department and helped develop a special sensor for "pressure sores" by those who use wheelchairs. For most of us, eye tracking technology sounds interesting. Eye tracking allows users to move a cursor around a computer or mobile device simply by moving your eyes and head. Oded Ben Dov initially used eye tracking technology to develop a video game that he showed off on Israeli TV.

Lately, there's been a rumble pretty much everywhere about artificial intelligence, digital personal assistants, the Internet of Things, wearables and apps for everything. I've even written about what the rise of digital assistants means to search. There are some who claim that these new technologies will render search obsolete, passed over for the convenience and joy of an always-available digital world. I think they are wrong. Instead of looking at a search engine as an ad platform, we need to remember what it actually does for people.

Sabrina Knight's second-grade students at a Brooklyn public school receive lessons in coding. Some school districts in the United States are attempting to expand computer science education while the Obama administration is pushing to bring the subject to every public school in the nation. On a recent Friday afternoon at a Brooklyn public school, the children of Sabrina Knight's second-grade class listened intently as she used a peanut butter and jelly sandwich to talk about algorithms. Moments later, a student volunteer walked back and forth across the room to demonstrate looping, a technical term used in the field of computer programming. "Thumbs up if you got it," Knight said, as a flurry of 7- and 8-year-old hands and thumbs shot up in the air.

The 2016 IEEE GRSS Data Fusion Contest, organized by the IADF TC, was opened on January 3, 2016. The submission deadline was April 29, 2016. Participants submitted open topic manuscripts using the VHR and video-from-space data released for the competition. Evaluation and ranking were conducted by the Award Committee. The winners are reported below along with the abstracts of the submitted papers.

When Bob Greifeld became Nasdaq's CEO in 2003, he was presented with outdated tools and a company that was bleeding cash and rapidly losing market share. Thirteen years later, Nasdaq has the largest market share for options and equities of any exchange in the US. Through acquisitions and partnerships, Greifeld has shaped Nasdaq into what he primarily views as a cutting-edge technology company. During a recent interview with Business Insider, Greifeld discussed his vision for the future of Nasdaq and how it fits into the exchange industry. What follows is that portion of our interview, edited for length and clarity.

We know of a few types of word analogies, like "France capital Paris" and "US currency dollar", but has anyone tried to search for all the possible analogies that can be deducted by word2vec? They would have to find modifiers that have multiple matches, like "word1 modifier word2". An algorithm could be to cluster all the difference vectors (word1-word2, for all words) and select words that are close to the centers of dense clusters. Even if we don't find all modifiers, we can infer more by combining with ontologies/word net. If we find all the types of analogy we could make a large test dataset to benchmark how capable are the various word embeddings of representing analogy.