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.

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.