Google's launching a new feature on mobile and its Google search app that promises to provide answers without forcing you to dig through dozens of overwhelming (and possibly misleading) medical forums, let alone consult established sites like WebMD. In the coming days, when you ask Google about your symptoms, the search engine will return a list of related conditions. For instance, if you search for "headache on one side," Google will offer up a list of informational cards with possible answers for what's ailing you. Tap a card, and it'll provide further information on the condition and treatment. Google takes pains to emphasize that it's trying to make it easier for you to find good medical information on the web, but it's also making sure you don't use the web as a substitute for professional advice.

We've long known that there are many tasks that computers can perform faster – and better – than humans. Of course, we still have to teach computers HOW to do these tasks, and when using conventional programming techniques we have to be very specific about what computers should do and when. With machine learning, we're essentially teaching computers how to learn what to do, and some of them are becoming better than we are at complex tasks. For example, machine learning is a key enabler of self-driving cars and experts predict that they will eventually be safer than human-driven vehicles. That's just one example of how machine learning is letting us use computers in new ways to do new things.

This blog about machine learning was written by Emily Barry. Emily is a Data Scientist in San Francisco, California. Another thing she loves is data science. The more she learns about machine learning algorithms, the more challenging it is to keep these subjects organized in her brain to recall at a later time. So, she decided to marry these two loves in as productive a fashion as possible.

The TensorFlow project is bigger than you might realize. The fact that it's a library for deep learning, and its connection to Google, has helped TensorFlow attract a lot of attention. Cool stuff, but--especially for someone hoping to explore machine learning for the first time--TensorFlow can be a lot to take in. Let's break it down so we can see and understand every moving part. We'll explore the data flow graph that defines the computations your data will undergo, how to train models with gradient descent using TensorFlow, and how TensorBoard can visualize your TensorFlow work. The examples here won't solve industrial machine learning problems, but they'll help you understand the components underlying everything built with TensorFlow, including whatever you build next!

National Harbor, MD - 16 Jun 2016: Local Motors, the leading vehicle technology integrator and creator of the world's first 3D-printed cars, today introduced the first self-driving vehicle to integrate the advanced cognitive computing capabilities of IBM (NYSE: IBM) Watson. Starting today, Olli will be used on public roads locally in DC, and late in 2016 in Miami-Dade County and Las Vegas. "Olli offers a smart, safe and sustainable transportation solution that is long overdue," Rogers said. "Olli with Watson acts as our entry into the world of self-driving vehicles, something we've been quietly working on with our co-creative community for the past year. We are now ready to accelerate the adoption of this technology and apply it to nearly every vehicle in our current portfolio and those in the very near future. I'm thrilled to see what our open community will do with the latest in advanced vehicle technology."

If you write marketing or advertising text for a living, you may want to get a second job skill. That's because software that writes text is here, and it is tackling a growing list of assignments. Several companies offer software that regularly churns out thousands of stories and reports based on structured data, like financial results. Ads that literally write themselves emerged last week, as IBM announced a new service based on its Watson supercomputer. A program called Quakebot has generated earthquake stories for the LA Times.

Intel's strategy for tackling the AI CPU market, where it is facing competition from leading GPU makers and potentially also big customers that make their own specialized processors for this purpose, such as Google, rests to a great extent on designing systems that scale out rather than up. The latter, according to the chipmaker, is the conventional but inefficient approach to architecting these systems. Software code in today's machine learning systems (machine learning is one of the most active subfields in the development of artificial intelligence) is tough to scale and usually lives in a single box, Charles Wuischpard, VP of the Intel Data Center Group and general manager of the giant's HPC Platform Group, said. Companies generally buy high-power scale-up systems filled with GPUs. "In a way, there's an efficiency loss here," he said on a call with reporters last week.

Google says it's offering all of these details strictly for informational purposes and that you should always consult a real doctor for proper medical advice. However, the company did consult with a team of doctors to review symptom info and experts at Harvard Medical School and Mayo Clinic evaluated the conditions to help improve the lists. That's in addition to collected data from medical searches and doctors in Google's own Knowledge Graph. The company also wants to know if the information it gives you in response to those queries is helpful, and will ask for you to offer feedback on the feature. The new symptoms search is rolling out on mobile over the next few days in the US, but only in English. Google says that eventually it plans to expand the tool to other countries and languages while also including answers about more symptoms.

Trust greases the wheels of the sharing economy, paving the way for transactions to take place between total strangers. But figuring out who is trustworthy and who is not remains a sticky bottleneck for digital businesses wanting to scale faster. Meanwhile the consequences for customers when startups screw up these risk calculations can be very unpleasant indeed. The traditional route to assessing risk is to run a full background check on an individual -- a process that can be time-consuming and expensive, given it can involve sending an actual person to an actual courthouses to parse actual paper records. Which is why, in recent years as sharing economy businesses have been gunning to scale up, other entrepreneurs have spotted an opportunity to step in to offer online services for verifying identity and screening for unsavory behavior, to try to steal a march on more established but slower paced background checkers.

Editor's Note: The author of this post is a legal technology and management consultant. The legal media has lately had a mania for tech headlines. Many commentators claim that tech, especially artificial intelligence (AI), will do something to Big Law. Tech more likely will do something in it: incremental change. I start with the case against disruption, then look at four headline-grabbing technologies: AI, Bots, Big Data, and Blockchain.