In research conducted in China, a rhesus monkey whose spinal cord was partially severed quickly regained lost control over his paralyzed leg after researchers implanted a signal-emitting electronic array below the site of the spinal injury. That pulse generator sent out electrical signals to the monkey's leg to move, and the monkey's affected leg responded as early as six days after his spinal cord was deliberately injured. The signals to move were commands collected from the motor cortex of unharmed rhesus monkeys as they freely walked and used their legs. Together, the two devices leaped over the broken connection between brain and limb, allowing the partially paralyzed monkey to mimic key walking motions. The brain-spine interface offers new hope that patients who have lost function due to spinal cord injury might be able to restore movement and prevent the degeneration of the neural wiring that is needed for an eventual return to movement.

We @ProjectJunoAI are big fans of landscapes. That's why we've created a machine intelligence landscape focused entirely on Europe [1]. Europe deserves a landscape of its own to highlight its talent and expertise. Until recently, its contribution to the innovation and commercialisation of machine intelligence technologies has been under-appreciated. We now see growing self-confidence borne of the success, and continued presence, of local acquired startups like VocalIQ, Swiftkey, Deepmind, Magic Pony Technology, and PredictionIO.

Despite steady progress in detection and treatment in recent decades, cancer remains the second leading cause of death in the United States, cutting short the lives of approximately 500,000 people each year. To better understand and combat this disease, medical researchers rely on cancer registry programs--a national network of organizations that systematically collect demographic and clinical information related to the diagnosis, treatment, and history of cancer incidence in the United States. The surveillance effort, coordinated by the National Cancer Institute (NCI) and the Centers for Disease Control and Prevention, enables researchers and clinicians to monitor cancer cases at the national, state, and local levels. Much of this data is drawn from electronic, text-based clinical reports that must be manually curated--a time-intensive process--before it can be used in research. For example, cancer pathology reports, text documents that describe cancerous tissue in detail, must be individually read and annotated by experts before becoming part of a cancer registry.

Mist unveiled two services launched on the Mist cloud platform: Mist business-critical Wi-Fi; and Mist's patented Virtual Bluetooth Low Energy (vBLE). Currently available, Mist products are already in use by many medium-to-large organizations globally. Mist is an extensible, programmable microservices cloud architecture for the indoor wireless technologies of Wi-Fi and Bluetooth Low Energy (BLE). A Mist wireless network understands and adapts to each user, how they are moving, the devices they are carrying and the content they are consuming at a scale never before possible. Mist's cloud platform enables a new approach to wireless in nearly a decade, applying data science and machine learning to transform and assure mobile user experience. With businesses managing more connected devices, enterprise-level IoT implementations and location-based marketing initiatives, Mist delivers products that go beyond simple connectivity, empowering businesses to deliver better mobile experiences.

Do humans make computers smarter? Could machine learning save this sea cow? Artificial intelligence is fact, not fantasy! Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term.

Nearly half of Australian workers will find their role filled by a machine in the next 20 years, according to the Government's latest report on the future of the workforce. The battle of man vs. machine may come sooner than we think – with billions of dollars of venture capital being poured into Machine Learning. Rapidly advancing technology is allowing us to make tremendous strides in simulating human thinking, meaning that even jobs that involve complicated decision-making may soon be automated. With evolved smart machines, knowledge workers will soon find their career paths disrupted in the near future. However, this change needn't be seen as a negative.

I recently read an article in which the winner of a Kaggle Competition was not shy about sharing his technique for winning not one, but several of the analytical competitions. "I always use Gradient Boosting," he said. And then added, "but the key is Feature Engineering." A couple days later, a friend who read the same article called and asked, "What is this Feature Engineering that he's talking about?" It was a timely question, as I was in the process of developing a risk model for a client, and specifically, I was working through the stage of Feature Engineering.

Deep learning just may be the buzziest, most misunderstood term in the tech world. And October was a banner month for the enigmatic subject. The term "deep learning" is, essentially, a synonym for a type of neural network approach. And though voguish at the moment, the goal of the field has long been to create a system that can independently learn. Now, research into machine learning is rapidly accelerating, and those intellectual seeds are bearing widely applicable fruits.

Facebook has seen the success of sponsored ad messages and has decided to expand its reach. Starting this week, businesses and companies will be able to directly send ads to users on Facebook Messenger and start conversations with chatbots. With Facebook's advertising platform, businesses and companies will be able to display "highly-targeted, in-context" ads to a user's Messenger thread, according to The Next Web. Also, ads displayed on the News Feed will be able to redirect users to Messenger to start conversations. To avoid any potential pushback from users, Facebook has given some limitations on how these ads will pop up on Messenger.

A year ago, Danny Lange took over as the head of machine learning at Uber. The ride-sharing company, which launched in 2009, is, essentially, a tech company: It operates entirely through an app. Lange manages a team in San Francisco, and Uber has a smaller team in Seattle. And machine learning has become the underlying foundation for every part of the company. "Machine learning and AI technologies can really solve some very fundamental business problems that are really hard to create hardwired solutions to," said Lange.