Expedia has grown far beyond a search engine for flights -- it's now the parent company of a dozen travel brands including Trivago and Hotels.com The business of delivering quality flight search results is tough, and Fleischman describes it as an "unbounded computer science problem". The reason for this is because flight itineraries and schedules are constantly changing, and Expedia's proprietary'best fare search' (BFS) has to'learn' and adapt all the time. The extent of the problem can be summed up by one statistic. In those three seconds you will see, on average, 16,000 flight options, in order of convenience or price or time.

Tree methods are commonly used in data science to understand patterns within data and to build predictive models. The term Tree Methods covers a variety of techniques with different levels of complexity but my aim is to highlight three I find useful. To set the problem up let's assume we have a census dataset containing age, education, employment status and so on. Given all this information we want to see if we can predict whether a person earns more than 50k per year. How can tree methods help us?

It's a common myth that the PDF is the final format of a document, where the data and content can only be rendered on a screen or printed on paper. This is far from correct and we will explain why. John Warnock had a vision. "This project's goal is to solve a fundamental problem that confronts today's companies. The problem is concerned with our ability to communicate visual material between different computer applications and systems."

This video is the introductory'explainer', to be followed by other animations in due course.

He and his team are focused on optimizing C2FO's capital markets through applied machine learning and developing contemporary quantitative risk management systems. Piero also enjoys teaching, rowing, and hacking on open data. You can find him on Twitter and LinkedIn. Geospatial analysis is a massive field with a rich history. Python has some pretty slick packages for working with geospatial data such as, but not limited to, Shapeley, Fiona, and Descartes.

Based on the disturbing number of successful data breaches over the past few years, it's pretty evident that organizations are being overwhelmed by the growing number of threats.

Booz Allen Hamilton and Conversable are teaming up to tackle what could be government's next big move to improve customer service -- chatbots. Booz Allen announced Monday that the two companies have a "joint strategic focus to advance, test and deliver world-class automated interactive messaging, tools, services and experiences that fundamentally enhance and transform customer care in all channels." "The federal government is uniquely positioned and motivated to seize the opportunity and deliver on the promise for self-service," Michael Isman, vice president in Booz Allen's Strategic Innovation Group, told FedScoop. The companies plan to leverage advances in artificial intelligence, machine learning and natural language processing to advance the capabilities of chatbots -- automated software programs developed to converse with people, usually via messaging. "The idea is -- we believe that the gains [in artificial intelligence, machine learning, automated next-gen analytics, crowdsourcing] can be most readily realized through the deployment of chatbots," Isman said, "to improve client, customer and stakeholder access, listening, responsiveness, [and] service delivery."

This past spring, Google began feeding its natural language algorithm thousands of romance novels in an effort to humanize its "conversational tone." The move did so much to fire the collective comic imagination that the ensuing hilarity muffled any serious commentary on its symbolic importance. The jokes, as they say, practically wrote themselves. But, after several decades devoted to task-specific "smart" technologies (GPS, search engine optimization, data mining), Google's decision points to a recovered interest among the titans of technology in a fully anthropic "general" intelligence, the kind dramatized in recent films such as Her (2013) and Ex Machina (2015). Amusing though it may be, the appeal to romance novels suggests that Silicon Valley is daring to dream big once again. The desire to automate solutions to human problems, from locomotion (the wheel) to mnemonics (the stylus), is as old as society itself.

An anonymous reader writes: At the Code Conference on Wednesday, Bill Gates balanced his fears of artificial intelligence with praise. He talked about two of the challenges AI will pose: a loss of existing jobs, and making sure humans remain in control of super-intelligent machines. Gates, as well as many other experts in the field, predict there will be an excess of labor resources as robots and AI systems take over. He plans to talk with others about ideas to combat the threat of AI controlling humans, specifically noting work being done at Stanford. Even with such threats, Gates called AI the "holy grail" as he envisions a future "with machines that are capable and more capable than human intelligence."

We don't understand AI because we don't understand intelligence