Using software to compare genetic information in bacterial isolates from animals and people, researchers have predicted that less than 10% of Escherichia coli 0157:H7 strains are likely to have the potential to cause human disease. According to Nadejda Lupolova, from the University of Edinburgh, Scotland, and colleagues, "machine-learning approaches have tremendous potential to interrogate complex genome information for which specific attributes of the organism, such as disease or isolation host, are known." The researchers published the results of their study in Proceedings of the National Academy of Sciences. Although most E. coli strains live in the gastrointestinal tracts of people and animals without causing disease, infection with E. coli 0157 is associated with serious illness in people. E. coli 0157 was first identified as a cause of disease in the United States in 1982, during an investigation into an outbreak of hemorrhagic colitis.

Despite computers performing increasingly impressive feats, when it comes to recognising real world objects, they have fallen short. But the machines are learning that just like the robots of sci-fi blockbusters, being able to tell one thing from another is a key skill. A team of UK researchers has created software which has taught itself to recognise objects around it, with impressive accuracy. UK researchers have created software, called RadarCat, which can teach itself to recognise objects around it with impressive accuracy . Called the RadarCat, short for radar categorisation for input and interaction, the software scans the world around it uses radar to scan the world around it.

What is driving the'robot age' and how can businesses leverage the capabilities being produced? Artificial intelligence is one of the 21st century's dominant fields of innovation. So it's no surprise that cutting-edge robots and other advanced smart machines fall under the rapidly expanding Internet of Things, which is projected to reach 25 billion devices by 2020. Every day we're reading headlines on machines getting'smarter' and robotics transforming a variety of industries, but what's driving this'robot age' and how can businesses successfully integrate and leverage this advanced automation? It's clear that artificial intelligence (AI) is a new industrial revolution, one that's driving the rise of robotics. But AI won't just be an industry – it will be part of every industry.

The Cooper Hewitt Design Museum houses an impressive collection of designed objects that chronicle the history and significance of design in our evolving world. These objects range from unrealized works of architecture to handwoven textiles from Africa to graphic designed posters that reflect the culture and pulse of humanity of their time. The museum is housed in the former mansion of Andrew Carnegie. Upon its completion in 1901, the sixty-four room mansion was the first private residence in the United States to have a structural steel frame that allowed for more expansive spaces and a feeling of lightness. The Carnegie Mansion was also the first private residence to have a residential elevator, central heating, and a precursor to central AC.

We propose a unified and systematic framework for performing online nonnegative matrix factorization in the presence of outliers. Our framework is particularly suited to large-scale data. We propose two solvers based on projected gradient descent and the alternating direction method of multipliers. We prove that the sequence of objective values converges almost surely by appealing to the quasi-martingale convergence theorem. We also show the sequence of learned dictionaries converges to the set of stationary points of the expected loss function almost surely. In addition, we extend our basic problem formulation to various settings with different constraints and regularizers. We also adapt the solvers and analyses to each setting. We perform extensive experiments on both synthetic and real datasets. These experiments demonstrate the computational efficiency and efficacy of our algorithms on tasks such as (parts-based) basis learning, image denoising, shadow removal and foreground-background separation.

Artificial intelligence needs transparency so humans can hold it to account, a researcher has claimed. Virginia Dignum, associate professor at the Delft University of Technology, told an audience at New York University on Friday that if we don't understand why machines act the way they do, we won't be able to judge their decisions. Dignum cited a story by David Berreby, a science writer and researcher, that was published in Psychology Today: "Evidence suggests that when people work with machines, they feel less sense of agency than they do when they work alone or with other people." The trolley problem, Dignum explained, is an area where people may place blind faith in a machine to choose the right outcome. The question is whether to switch the lever on a hypothetical runaway train so that it kills one person instead of five.

Unlike other components to an enterprises' technology mix, determining the ROI of machine learning is a less-than-obvious process, particularly when solutions are new and little by way of case studies or benchmarks exist. While we're far from a world where SMBs (small- and mid-sized businesses) outside of Silicon Valley integrate AI into their regular operations, we will undoubtedly see an explosion of novel uses in industry and enterprise over the next 5 to 10 years, and executives are rightly concerned with how to make the most of those technology, time, and staffing decisions. If you're a business who's new to the machine learning scene (and that's a vast majority), there are more burning questions than answers at present. "What are the criterion needed for a company to derive maximal value from the application of machine learning in a business problem?" Tapping into our hundreds of interviews (on our podcast and otherwise), as well as reaching out to other experts in the field, allowed us to glean valuable insight from researchers and executives across the globe.

Just about every service industry--from retailers to restaurants to hotels--has developed some kind of robot to attend to your needs on the cheap. The latest effort in the banking world (there are already robotic bank receptionists in China and Japan) is to take the rote responses of bots to the next level, by adding a touch of human empathy. The Royal Bank of Scotland (paywall) plans to unveil its new artificial intelligence system, known as "Luvo," by the end of the year. The AI service, designed by IBM, will attend to customer banking needs through its mobile or online as a chatbot. It will function similarly to Siri, the iPhone virtual assistant that answers questions with a distinct voice and "personality."

The DeepMind artificial intelligence (AI) being developed by Google's parent company, Alphabet, can now intelligently build on what's already inside its memory, the system's programmers have announced. Their new hybrid system – called a Differential Neural Computer (DNC) – pairs a neural network with the vast data storage of conventional computers, and the AI is smart enough to navigate and learn from this external data bank. What the DNC is doing is effectively combining external memory (like the external hard drive where all your photos get stored) with the neural network approach of AI, where a massive number of interconnected nodes work dynamically to simulate a brain. "These models... can learn from examples like neural networks, but they can also store complex data like computers," write DeepMind researchers Alexander Graves and Greg Wayne in a blog post. At the heart of the DNC is a controller that constantly optimises its responses, comparing its results with the desired and correct ones.

Having a pool is fun. Cleaning it by dragging a net around is not. That's why I've thought up the Moobi, a water-loving take on the Roomba vacuum cleaner. Like a whale, the Moobi use an oscillating tail, wagging up and down, to move through the water. And like a whale, it would live its life with its mouth wide open, sucking down water and filtering out waste as if it were tasty plankton.