Guest Commentary: The future of human consciousness will be a hybrid affair. We will live and work in a ubiquitous computing environment, where physical reality and a pervasive digital layer mix seamlessly according to the logic of software and the richness of highly contextual data. This is mixed reality (MR) -- and it will soon simply be reality: Projected onto our mind's eye, always on, always connected, and deeply personalized. It will be delivered first through a head-mounted display, and ultimately embedded in our perception via subtler inputs. The resulting human network will be a massive, dynamic system capable of generating enormous value for humankind, as we embrace our cyborg future and the superpowers it enables.

The future of human consciousness will be a hybrid affair. We will live and work in a ubiquitous computing environment, where physical reality and a pervasive digital layer mix seamlessly according to the logic of software and the richness of highly contextual data. This is mixed reality (MR)--and it will soon simply be reality: projected onto our mind's eye, always on, always connected, and deeply personalized. It will be delivered first through a head-mounted display, and ultimately embedded in our perception via subtler inputs. The resulting human network will be a massive, dynamic system capable of generating enormous value for humankind, as we embrace our cyborg future and the superpowers it enables.

Are you tired of hearing about artificial intelligence yet? Well, I have some bad news. It's only going to become the most important thing in our lives. Every once in a while you read an article that completely changes and overwhelms the way you think about something. Today, for me, it was The Artificial Intelligence Revolution: Part 1 and Part 2. These are very long articles, so I have taken the liberty of extracting the best parts for a quick skim.

If you're a child of the '80s like me, you might recognize this famous line from the movie WarGames. This innocent-sounding question comes not from one of the movie's human stars, but from a military super-computer named Joshua, after a bored high school student, played by Matthew Broderick, accesses the computer's hard drive. Thinking he's hacked into a video game company, Broderick's character accepts Joshua's challenge and chooses the most intriguing game he can find: global thermonuclear war. Joshua is an intelligent computer programmed to learn through simulations like the one Broderick's character initiates. And because the computer actually does control the arsenal of U.S. nuclear weapons, it's a "game" that puts the planet on the brink of World War III.

Outcomes Based Healthcare and Big Data Partnership have won an Innovate UK grant for a 1m project to change healthcare's approach to diabetes. In partnership, these two organisations will be creating "a dashboard and software product for doctors to predict and pre-treat for complications of diabetes." The press release explains that this will utilise health and non-health data in conjunction with advanced Machine Learning and analytics techniques to develop a system that can identify the progression of the disease. This will come from a local London population. The reason this Big Data and Machine Learning approach is interesting though, is it offers a top-line independent perspective on a difficult disease.

Gensim users and developers have shaped the newest release 0.13.0 through their comments, requests and Python code contributions. The release includes new features like Word Movers Distance (WMD) and offers a new tool which is used to tune Topic Models. RaRe Technologies today announced a major update of the software package Gensim, an open source machine learning toolkit for understanding human language. Gensim users and developers have shaped the newest release 0.13.0. This release includes new features like Word Movers' Distance (WMD)*, a novel distance function between unstructured text documents, plus new Tutorials and Quickstarts.

When beginners get started with machine learning, the inevitable question is "what are the prerequisites? What do I need to know to get started?" You need to master math. A list like this is enough to intimidate anyone but a person with an advanced math degree. It's unfortunate, because I think a lot of beginners lose heart and are scared away by this advice.

This talk was given at a local TEDx event, produced independently of the TED Conferences. Jerome Pesenti, lead developer of IBM's Jeopardy-winning Watson supercomputer, says development of software relying on natural language and machine learning needs new ways of gathering and treating data. Jerome Pesenti, lead developer of IBM's Jeopardy-winning Watson supercomputer, says development of software relying on natural language and machine learning needs new ways of gathering and treating data. About TEDx, x independently organized event In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group.

Boston Limited have introduced the latest weapon to their machine learning armoury in the guise of the Boston ANNA Pascal, a new NVIDIA Tesla GPU-based solution at ISC 2016 in Frankfurt, Germany. The revolutionary NVIDIA Pascal architecture is purpose-built to act as the engine of computers that learn, see, and simulate our world. Leveraging this technology, the Boston ANNA Pascal should be considered for the title of the'world's fastest deep learning appliance'. By introducing four ground-breaking technologies, the appliance enables the system to deliver lightning fast, absolute performance to HPC and deep learning workloads with infinite computing needs. The Boston ANNA Pascal redefines what is possible for the research community and the industry; helping solve many big data problems such as computer vision, speech recognition, and natural language processing.

Conventional intrusion and detection methods to evaluate network anomalies have several impasses for large-scale datasets over ultra-blazing speed networks with disparate sources of data coming in with high-velocity and high-volume. Machine learning and artificial intelligence techniques mine the massive network datasets with IP size distribution can perform dichotomy of flow-based network traffic to diagnose the network anomalies as an effective solution. The simplex and similar size of the IP distribution with same attributes hitting the flow-based analysis on regular time intervals display the symptoms of network anomalies. Various flow-based monitoring tools such as nProbe and FlowMon Probe detect these intrusions on gigabit-sized networks. Two key detection techniques of NetFlow-based on large-scale and high-speed networks are: a) the misuse intrusion method; b) network anomaly detection method.