There are countless internet quizzes that helps sort you into Hogwarts houses depending on your personality, but nothing comes quite close to this: an actual sorting hat powered by IBM Watson. The hat, created by IBM engineer Ryan Anderson, started off as a fun project for him and his two daughters to help expose the girls to STEM while bridging it with their interests in the Harry Potter series. The hat uses Watson's Natural Language Classifier and Speech to Text to let the wearer simply talk to the hat, then be sorted according to what he or she says. Anderson coded the hat to pick up on words that fit the characteristics of each Hogwarts house, with brainy and cleverness going right into Ravenclaw's territory and honesty a recognized Hufflepuff attribute. His daughter helped by adding lines of established "ground truths" for each of the four houses, with Watson using deep learning to figure out more attributes and expanding known qualities of each house every time the hat is worn.

I recently participated in a meeting of technologists, economists and European philosophers and theologians. Other attendees included Andrew McAfee, Erik Brynjolfsson, Reid Hoffman, Sam Altman, Father Eric Salobir. One of the interesting things about this particular meeting for me was to have a theological (in this case Christian) perspective to our conversation. Among other things, we discussed artificial intelligence and the future of work. The question about how machines will replace human beings and place many people out of work is well worn but persistently significant. Sam Altman and others have argued that the total increase in productivity will create an economic abundance that will enable us to pay out a universal "basic income" to those who are unemployed.

When you think of leaders in artificial intelligence, Netflix doesn't usually jump to the top of the list. But the streaming video service's VP of Product Innovation Carlos Uribe-Gomez and Chief Product Officer Neil Hunt published a paper that says some of its AI algorithms save Netflix 1 billion each year. In their paper, the two Netflix execs detail how the company's recommendation engine impacts its churn rate. Netflix no longer reports its churn rate, but the paper notes that Netflix's "retention rates are already high enough that it takes a very meaningful improvement to make a retention difference of even 0.1%." Let's dive into how the recommendation engine saves Netflix money -- and what the return on investment looks like.

When getting started with Azure Machine Learning, the hardest part for many developers is staring down the list of Azure machine learning algorithms (there are currently 25 of them) and trying to figure out which one would work best. In this blog post, I will provide some resources for helping you choose the right algorithm. At a high level, there are currently 4 categories of algorithms available in Azure Machine Learning. Clustering Scenario: Group similar toys together, to be used for gift recommendation service. Clustering is a type of unsupervised learning, meaning we don't have labeled examples or data mappings in advance for it to learn from.

IBM delivers cognitive computing to healthcare and weather forecasting. Google launches a new machine learning research center. GE uses machine learning to restore a power plant in Northern Italy. Microsoft acquires one of the big contributors to big data open source software. Those are the highlights of this week's Big Data Roundup.

Analytics-driven embedded systems bring analytics to embedded applications, moving many of the functions found in cloud-based, big-data analytics to the source of data. This allows for more efficient data processing, leading to better real-time response and reduced communication overhead. I talked with Paul Pilotte, Technical Marketing Manager at MathWorks, about how the company is addressing this area, and how its tools can be used to create analytics-driven embedded systems. Wong: What are analytics-driven embedded systems and why are they important to today's design engineers? Pilotte: The ability to create analytics that process massive amounts of business and engineering data is enabling designers in many industries to develop intelligent products and services.

We get asked a lot of questions about how to start building with Watson, so we decided to compile our top 4 Frequently Asked Questions. You can use this as a guide to learn more about the technology, receive inspiration from use cases, get valuable resources, and ultimately begin building with the technology. Cognitive technology's strength lies in its ability to draw insights from unstructured data sets. Structured data is found in a spreadsheet, whereas unstructured data is text such as tweets, medical journals, etc. Today 80% of data is unstructured, so tools such as cognitive computing are becoming more important in helping humans understand what's inside that data.

On Monday Apple showed off a string of new iPhone features powered by recent advances in artificial intelligence--many of them aping ones already launched by rival Google. But Apple's announcements of features like facial recognition or software that knows what's in your photos, made during its annual Worldwide Developer Conference, were distinct in how much they emphasized privacy. Craig Federighi, senior vice president of software engineering at Apple, repeatedly stated that machine-learning algorithms able to understand personal data such as photos are being used only within the confines of a person's iPhone, not on Apple's cloud servers. "We believe you should have great features and great privacy," he said. A new version of Apple's Photos app, coming this fall with a new version of Apple's mobile operating system, will use facial recognition to maintain virtual albums of snaps containing people you frequently photograph.

It's Man v. Machine on Jeopardy this week as IBM super-robot Watson takes on former champions Ken Jennings and Brad Rutter. At The Atlantic, we're using Watson as an occasion to think about what smart robots mean for the American worker. This is Part Three of a three-part series on the exciting and sometimes scary capabilities of artificial intelligence. Read Part One --Anything You Can Do, Robots Can Do Better -- and Part Two -- Can a Computer Do a Lawyer's Job? Since the beginnings of the personal computer industry, computer hardware sales have often been driven by a particular software application so compelling that it has motivated customers to purchase the machine required to run it. When the Apple II was introduced in 1977, it was initially a success within a relatively small group of computer hobbyists.

If a new driverless shuttle bus makes riders nervous, IBM's Watson system is on board to make feel passengers feel less alone. And the bus' cute name – Olli – might help, too. TechRepublic reports that Maryland's Local Motors is partnering with IBM on its autonomous shuttle bus named Olli. IBM is contributing its Watson Internet of Things (IoT) for Automotive cognitive computing system. This comes as the global market for self-driving cars is expected to grow exponentially over the next few years.