As some of you may have already heard, I'm leaving CMU to join Amazon, effective July 1, 2016. There I will be in charge of Amazon's Cloud Machine Learning Platform with the task to make machine learning as easy to use and widespread as it could possibly be. This is a terrific task and it was an offer that I could not turn down. Our lab will be in the Bay Area and we will strive to turn the state of the art in machine learning research into the state of the art in industry. This is a very exciting time and I'm looking forward to it.

Organizations create style guides to capture the rationale of their design decisions and help other teams build great experiences. You might have read gov.UK's service manual or the U.S. Digital Services Playbook. I wanted to do the same for chatbots build on the Facebook's messenger platform. At Sure, we are creating an online assistant that helps you find food and drinks that are better for yourself and the planet. It is still very early days for bots, so I wanted to take the opportunity to share some of our early learnings.

Machine-learning is all the rage in fraud detection, with industry analysts, academics, businesses and technology media examining the advantages of algorithms and big data in the fight against e-commerce fraud. Especially for fraud analysts working in companies with small budgets, machine-learning tools are seen as a cost-effective way to tighten fraud controls while maintaining fast decision times, as Forrester noted in its 2015 cross-channel fraud report. There's no question that machine-learning tools can be an effective component of fraud reduction program, but relying on them to save staffing costs may not be cost-effective in the long run. That's because while machine learning is an invaluable tool in the fight against fraud, it relies on human input and insight to create a comprehensive solution that yields the best results. Algorithms are useful for identifying potential fraud quickly, but due to variability in consumer behavior – such as making online purchases while traveling abroad -- some transactions will be falsely flagged for decline.

Where does true power reside? Is it waving at you from a stage-lit, Presidentially-sealed podium? Or tucked away inside a billionaire's wallet? Can you smell it in a mahogany-clad clubroom at Yale in the smoke of a Bonesman's cigar, or catch a glimpse of its dark feathers perching on the advisory council of a transnational bank? One thing we know for sure: the nature and location of power is changing, and the agents of that change are the Californian technology companies that have the taken the 21st century by the throat.

Olli, a 3D printed, self-driving minibus, to hit the road in US A new maker of self-driving vehicles burst onto the scene Thursday in partnership with IBM's supercomputer platform Watson, and it's ready to roll right now. The vehicle -- a 3D-printed minibus called "Olli" capable of carrying 12 people -- was unveiled by Arizona-based startup Local Motors outside the US capital city Washington. Napster's improbable journey This week the Rhapsody music service announced that it will retire the Rhapsody name and re-brand its service (and the company) under the Napster brand. The Napster name has endured a long journey in the 17 years since Shawn Fanning first created the service in early 1999. I thought it might be helpful to put together a short history tracing that path.

There's been a lot of handwringing about the algorithms driving what we see related to political news. First, a month ago, there was concern that Facebook's news feed results were biased against conservative news, and this week, there's concern that Google favors Hillary Clinton in its autocomplete suggestions in its search engine. The video shows that Google seems to have suppressed the appearance of "Hillary Clinton indictment" in favor of "Hillary Clinton India," even though data shows people search for information on Clinton's indictment more than information on Clinton and India. It points out that the executive chairman of Google's parent company, Eric Schmidt, is a big Clinton supporter and that Google has many ties to her as well.) Search engines and algorithms decide what's relevant on these sites in very complicated ways, and the public generally doesn't know when it gets tweaked. Generally speaking, people want artificial intelligence.

Check out their answers here: https://goo.gl/jpbMy9

Have you tried out a few Kaggle competitions, but you aren't quite sure what you're supposed to be doing? Or perhaps you've heard all the talk in the Kaggle forums about Python's scikit-learn library, but you haven't figured out how to take advantage of this powerful tool for machine learning? If so, this post is for you! As a data science instructor and the founder of Data School, I spend a lot of my time figuring out how to distill complex topics like "machine learning" into small, hands-on lessons that aspiring data scientists can use to advance their data science skills. I especially enjoying teaching students how to perform effective machine learning using scikit-learn, which is why I'm very excited to announce this new video series!

Zoltan Istvan believes that artificial intelligence will advance in 10 to 15 years. Such progress, according to the transhumanist politician, will address all of the concerns of the human civilization, including the slowing down or blocking of the aging process through the combination of technology and human selves. For Istvan, the likelihood does not include in the field of weird science fiction. He thinks that artificial intelligence, which is a computer brain that is capable of doing anything which humans do, is about to come soon, and that the US should develop it first. He noted that the government must have a plan in order to deal with it before the "arms race of civilization" arrives.

This week, Google released a research paper chronicling one of its latest forays into artificial intelligence. Researchers at the company programmed an advanced type of "chatbot" that learns how to respond in conversations based on examples from a training set of dialogue. And the bot doesn't just answer by spitting out canned answers in response to certain words; it can form new answers from new questions. This means Google's researchers could get a little creative with it, and they certainly did - they asked the bot everything from boring IT questions to inquiries about the meaning of life. The responses were alternately impressive, amusing, and unnerving.