Machine learning can predict strains of bacteria likely to cause food poisoning outbreaks, research has found. The study – which focused on harmful strains of E. coli bacteria – could help public health officials to target interventions and reduce risk to human health. Researchers at the University of Edinburgh's Roslin Institute used software that compares genetic information from bacterial samples isolated from both animals and people. The software learns the DNA signatures that are associated with E. coli samples that have caused outbreaks of infection in people. It can then pick out the animal strains that have these signatures, which are therefore likely to be a threat to human health.

IBM Research (NYSE: IBM) today announced a multi-year collaboration with the Department of Brain & Cognitive Sciences at MIT to advance the scientific field of machine vision, a core aspect of artificial intelligence. The new IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension's (BM3C) goal will be to develop cognitive computing systems that emulate the human ability to understand and integrate inputs from multiple sources of audio and visual information into a detailed computer representation of the world that can be used in a variety of computer applications in industries such as healthcare, education, and entertainment. The BM3C will address technical challenges around both pattern recognition and prediction methods in the field of machine vision that are currently impossible for machines alone to accomplish. For instance, humans watching a short video of a real-world event can easily recognize and produce a verbal description of what happened in the clip as well as assess and predict the likelihood of a variety of subsequent events, but for a machine, this ability is currently impossible. Beginning in September 2016 in Cambridge, the BM3C collaboration will bring together leading brain, cognitive, and computer scientists to conduct research in the field of unsupervised machine understanding of audio-visual streams of data, using insights from next-generation models of the brain to inform advances in machine vision.

Webroot has acquired the assets of San Diego-based CyberFlow Analytics in a move that beefs up the company's machine learning capabilities. CyberFlow's FlowScape network behavioral analytics solution applies data science to network anomaly detection. Thus, the acquisition extends Webroot's machine learning-based cybersecurity to the network layer, to address the explosion of internet-connected devices and an increasingly complex threat landscape. SaaS-based FlowScape adversarial analytics and unsupervised machine can identify polymorphic malware and advanced persistent threats (APTs) that mask their activities within everyday network noise, the company said, identifying network anomalies in both IPv4 and IPv6 traffic. Security analysts can view alerts via a SIEM solution or the automated FlowScape visualization console, which creates self-forming behavioral clusters that provide an early warning system of high-risk activity that forms over time.

This is a follow up to an article I wrote last year, Machine Learning in a Week, on how I kickstarted my way into machine learning (ml) by devoting five days to the subject. After this highly effective introduction, I continued learning on my spare time and almost exactly one year later I did my first ml project at work, which involved using various ml and natural language processing (nlp) techniques to qualify sales leads at Xeneta. This felt like a blessing: getting paid to do something I normally did for fun! It also ripped me out of the delusion that only people with masters degrees or Ph.D's work with ml professionally. The truth is you don't need much maths to get started with machine learning, and you don't need a degree to use it professionally.

But deciding to build an app in the first place was driven by a broader purpose. As one doctor put it, "If you want to do world-class sociology research, build Facebook."

Beginning in September 2016 in Cambridge, the BM3C collaboration will bring together leading brain, cognitive, and computer scientists to conduct research in the field of unsupervised machine understanding of audio-visual streams of data, using insights from next-generation models of the brain to inform advances in machine vision. The vision is that this integrated cross-disciplinary research will lead to advances that are likely to change both our personal and professional lives - from helping clinicians improve elderly and disabled care to helping organizations maintain and repair complex machinery as well as a host of cross-industry applications. "In a world where humans and machines are working together in increasingly collaborative relationships, breakthroughs in the field of machine vision will potentially help us live healthier more productive lives," said Guru Banavar, Chief Scientist, Cognitive Computing and VP at IBM Research. "By bringing together brain researchers and computer scientists to solve this complex technical challenge, we will advance the state-of-the-art in AI with our collaborators at MIT." The BM3C will be led by Professor James DiCarlo, head of the Department of Brain & Cognitive Sciences (BCS) at MIT, who will be supported by a team of faculty members, researchers, and graduate students from both the Brain & Cognitive Sciences department and the MIT Computer Science and Artificial Intelligence Lab (CSAIL).

A controversial neurosurgeon who wants to carry out the first human head transplant has outlined plans to conduct'Frankenstein' experiments to reanimate a human corpse to test his technique. Dr Sergio Canavero, director of the Turin Advanced Neuromodulation Group, and his collaborators believe they may be able to conduct the first human head transplant next year. They have outlined plans to test whether it is possible to reconnect the spinal cord of a head to another body with tests that will stimulate fresh human corpses with electrical pulses. However, the Russian man who has volunteered to have the first transplant has also revealed that his girlfriend is opposed to him having the operation. Dr Sergio Canavero (pictured) believes the first human head transplant will take place next year.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Google's DeepMind announced the WaveNet project, a fully convolutional, probabilistic and autoregressive deep neural network. It synthesizes new speech and music from audio and sounds more natural than the best existing Text-To-Speech (TTS) systems, according to DeepMind. Speech synthesis is largely based on concatenative TTS, where a database of short speech fragments are recorded from a single speaker and recombined to form speech. This approach isn't flexible and can't be adjusted to new voice inputs easily, often resulting in the need to completely rebuild a dataset when there's a desire to drastically alter existing voice properties. DeepMind notes that while previous models typically hinge around a large audio dataset from a single input source, or single person, WaveNet retains its models as sets of parameters that can be modified based on new input to an existing model.

New drugs typically take 12-14 years to make it to market, with a 2014 report finding that the average cost of getting a new drug to market had ballooned to a whopping 2.6 billion. It's a topic I've covered before, with a study published earlier this year highlighting how automation could be used to reduce the cost of drug discovery by approximately 70%. It's an approach that a number of companies are taking to market. For instance, London based start-up Benevolent.AI utilizes complex AI to look for patterns in the scientific literature. They have already managed to identify two potential drug targets for Alzheimer's that has already attracted the attention of pharmaceutical companies.