IBM wants to combine blockchain's distributed ledger technology with its artificial intelligence arm to make the billions of smart devices connected to the internet safer, and by doing so it would allow virtual time travel by letting regulators rewind to the point when the problem occurred and see just what happened. According to a report by CoinDesk, IBM is still in the very early stages of developing this project that brings together the company's Internet of Things Foundation and Watson divisions to create the Watson Internet of Things group, which is attempting to marry those technologies with the emerging blockchain technology that underpins the bitcoin network. The project is being overseen by IBM's chief architect in charge of Internet of Things security Tim Hahn who told CoinDesk that the possibilities of the collaboration of artificial intelligence, IoT and blockchain were huge. "What we're doing with blockchain and devices is enabling the information those devices supply to effect the blockchain. You begin to approach the kind of things we see in movies."

When self-driving cars get here, they'll make our commutes more efficient and allow us to get the kids to soccer practice without disrupting mom and dad's work days. They'll conserve resources, boost mobility for seniors and others who can't, and make deadly traffic accidents all but disappear. But the impact of self-driving cars will go deeper than even that, according to researchers at the Illinois Institute of Technology, who've begun to study the potential ultra-long-range impacts of self-driving cars on urban environments. Everything from sidewalks and curbs to streets, building designs, urban layouts, and living patterns will change as computers take the wheel. "We're looking at the broader urban effects--and urban opportunities--of this technology," says Illinois Tech architect Marshall Brown, one of the team members in the Chicago school's Driverless Cities Project. "It's in the news a lot, but nobody's been discussing what it will actually do to cities." Just six percent of long-range transportation plans in major US cities are factoring the impact of autonomous cars, according to a report released in the fall by the National League of Cities.

Ruth Toner Data Scientist Twitch Insight Fellow 2016 Physics Postdoc Harvard University Ruth Toner was a Fellow in our most recent Data Science session in Silicon Valley. She's since joined the Community team at Twitch as a Data Scientist. In this post she describes Fanguard, the tool she built at Insight to protect Tumblr readers from spoilers for blockbuster movies and popular TV shows. Before attending Insight Data Science, I spent eight years of my life in the field of particle physics. Like many postdocs and grad students, when I wasn't trying to discover the basic laws of matter (i.e., debugging my code), I spent a lot of time surfing the Internet.

If you're an intermediate Java/Scala developer interested in applying your skills at a growing start-up in the fascinating world of neural networks, artificial intelligence and machine learning, this could be a great opportunity for you. Bernie.ai is an angel-funded start-up in the dating space. This is not a dating app but rather an app that aggregates dating content in order to make the dating experience more efficient, focused and less time-intensive for the end-user. Here is a link to a recent NY Times article on the app. The app, called Bernie, uses machine vision to find great dating matches, learns user preferences based on attributes that you like and don't like and begins conversations with promising partners to see if there is interest.

Inc. (CRM - Analyst Report) acquired MetaMind, a start-up specialized in offering "deep-learning" services. However, the financial terms of the deal remain under wraps. Founded in 2014, MetaMind received a combined investment of 8 million from Salesforce CEO Marc Benioff and Khosla Ventures in December the same year. MetaMind's "deep-learning" AI technique, according to Bloomberg "helps business users employ deep learning technology, which trains networks of computers to mimic the human brain's ability to recognize and analyze pictures or text without explicit instructions". MetaMind's technology and tools span natural language and image recognitions which help users to analyze data and make better decisions.

As a way to meet the demands of artificial intelligence, NVIDIA developed the first deep learning supercomputer called NVIDIA DGX-1, giving researchers and data scientists a new class of intelligent machines that could learn and perceive the world as humans do. The DGX-1, which was launched today at NVIDIA's GPU Technology Conference, is the first system designed specifically for deep learning. It comes fully integrated with hardware, deep learning software, and development tools for easy deployment. It also has a turnkey system that contains a new generation of GPU accelerators, which delivers the equivalent throughput of 250 x86 servers, the company said. The deep learning system allows researchers to reduce the time for training larger, more sophisticated deep neural networks.

Kik's is a bit different from its peers, and the differences reveal some fascinating nuances about how messaging is evolving into a new user-interface paradigm to rival apps. For people who want to create a bot, Kik offers a simple API that allows them to specify how the bot will behave: What it can do, what it will say, and what responses you can type in reply. For users, the bots can be summoned into any conversation with an "@" mention. So you might be making plans with a friend depending on the weather, and bring forth @weatherchannel to give you an update on the forecast. Then, the bot simply leaves your conversation--a telling, human-centered detail meant to teach users that bots are here to help rather than to listen passively to their conversations.

Sometimes engineers are prone to overkill- or making machine learning too complex for their needs. This guy from the complexity initiative at Quora gives explanations and valuable tips on how to avoid that. "Today, more and more products and engineering teams rely on machine learning (referred to as ML through out this blog post). The abundance of open source tools and libraries also makes it much easier to learn, develop, and build ML models even for people with little prior knowledge or experience. ML is a powerful tool for many problems, but it comes with costs -- it can introduce complexity to systems which builds up over time and evolves into large technical debt. A recent publication by Google argues that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying ML. At Quora, we've been using ML to tackle many interesting problems such as ranking, search, recommendation, and spam detection. We are constantly evaluating new approaches and building new product features with ML. At the same time, we also strive to be careful about the complexity that these models introduce and have developed principles and best practices to avoid or reduce such complexity. In this blog post, we will share our thinking about complexity in ML systems and describe some of our approaches to mitigate them."

Larry Doherty was in good hands, steady hands, like the metal ones you can find on an automaker's assembly line. The 64-year-old bean salesman from Bow Island, Alta., had come to the University of Calgary's Department of Clinical Neurosciences and Hotchkiss Brain Institute to undergo arteriovenous malformation surgery – to untie the tangled blood vessels in his brain. When everyone in the operating room was ready, the operating surgeon began his work sitting in a whole other room surrounded by computer monitors, including one with a 3-D image of Mr. Doherty's brain. Using specially designed hand controls, Dr. Garnette Sutherland manoeuvred the robot to its ready position. For Mr. Doherty, it was the first time in his life he had undergone surgery.

Today we are rolling out automatic alternative (alt) text on Facebook for iOS. Automatic alt text provides visually impaired and blind people with a text description of a photo using object recognition technology. Starting today, people using a screen reader to access Facebook on an iOS device will hear a list of items that may be shown in a photo. This feature is now available in English for people in the U.S., U.K., Canada, Australia, and New Zealand. We plan to roll it out to more platforms, languages, and markets soon.