GENEVA – A new device has allowed two monkeys to regain use of their paralyzed legs by transmitting brain signals wirelessly, bypassing their spinal cord lesions, a study released Wednesday by the journal Nature said. The implantable device, called a neuroprosthetic interface, was developed by an international team led by researchers at the Federal Polytechnic School of Lausanne (EPFL) and may soon be tested as a remedy for paralysis in humans. "For the first time, I can imagine a completely paralyzed patient able to move their legs through this brain-spine interface," Jocelyne Bloch, a neurosurgeon at the Lausanne University Hospital, said in a press release from EPFL. The interface conceived at EPFL is a multicomponent brain-spine connector, which decodes signals from the part of the motor cortex responsible for leg movements. It then relays those signals in real time to the lumbar region of the spinal cord that activates leg muscles to walk.

It's fun to buy a drone from a store, but perhaps more satisfying to build one from scratch. Intel in December will start shipping a fully loaded drone kit to let you do just that, with all the parts including the rotors, software, 3D camera and flight controller. Intel's Aero Ready to Fly Drone kit will go on sale on the company's website. An Intel spokeperson couldn't immediately provide a price. But it won't be cheap -- likely more than $600.

As technology progresses and society moves further into the digital age, the volume of data that needs to be processed and distributed is increasing at a rapid rate. As if that weren't enough, the data is becoming increasingly complex as well, with some modern software systems consisting of millions of lines of code. Their maintenance requires huge data centers with large teams of software developers, and they consume a ton of energy and financial resources. To mitigate this industry-wide problem, data science experts from Lancaster University have developed an artificially intelligent computer software system that can rapidly self-assemble itself into the most efficient form without human input. The system is called REx, and it uses machine learning to perform.

Constellation Research estimates that by 2020, at least 60 percent of the data considered to be mission-critical will live outside of the corporate firewall instead of in on-premises data centers. This trend has accelerated through the mobile, social and big data movements, with data and applications increasingly living and running outside of the four walls of the enterprise. As organizations shift from ownership to access, artificial intelligence (AI), Internet of things (IOT), and block chain will all require contextual insights to be delivered at the speed of thought. Against this backdrop, Integration Platform as a Service (IPaaS) is emerging as a one-stop, cloud-based choice for supporting both data integration and application integration to help address the new requirements and challenges that organizations face. IPaaS provides the foundational and mission-critical capabilities required to support disruptive business models.

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Artificial-intelligence tech has been moving out of academic circles into real-world applications like screening out spam or making it look like Van Gogh painted your photo. But now it's also tapping our weakness for cute sea creatures threatened by extinction. The large marine animal is really hard to keep track of. Researchers are using drones to take aerial photos of the ocean, but detecting them in those photos is a challenging task...for humans. That's where Google's TensorFlow neural network software project comes in, developer advocate Josh Gordon explained in a story posted Wednesday to Google's machine learning blog.

BenevolentAI is a London start-up that specializes in artificial intelligence (AI); its BenevolentBio division, formerly Stratified Medical, applies AI to human health and biotech. Its baby is a technology that could speed up late-stage development of drugs and provide richer clinical data. Now, it will test it using clinical stage drug candidates licensed from Janssen. Although there are no details on the particulars of the agreement, BenevolentAI is confident that it can accelerate clinical development and begin Phase IIb trials in mid-2017. If everything works out well, the company will have exclusive rights to develop, manufacture and commercialize these candidates.

Musk's tweets come at a time when a group of hackers used a Distributed Denial of Service (DDoS) attack to wipe out part of the internet on Oct. 21. This resulted in many sites like PayPal, Twitter, Netflix, and Spotify being unreachable and offline for hours. Authorities are of the view that a person or a group of people orchestrated the attack. Musk said that in the future massive DDoS attacks will not require real human hackers to create a ruckus on the infrastructure that runs the internet. With the improvement of artificial intelligence, Musk said that hackers will misuse it to optimize their attacks.

There is much discussion these days about Lambda Architecture and its benefits for developing high performance analytic architectures. It offers a combination of a high performance, low latency ETL with a real-time layer, and a slower, more accurate, and flexible solution that runs in batch. As I work with it, I have learned to appreciate Cassandra's relative "immortality" and fit for such analytic systems. In a complex distributed system it's nice to know you have one component that you can rely on without much tending. Need to be highly available and regionally distributed?

Efforts to understand suicide risks can be roughly clustered into traits or states. Trait analyses focus on stable characteristics rooted in and measured using biological processes (Costanza et al., 2014; Le-Niculescu et al., 2013), whereas state analyses measure dynamic characteristics like verbal and nonverbal communication, termed "thought markers" (Pestian et al., 2015). Machine learning and natural language processing have successfully identified differences in retrospective suicide notes, newsgroups, and social media (Gomez, 2014; Huang, Goh, & Liew, 2007; Matykiewicz, Duch, & Pestian, 2009). Jashinsky et al. (2015) used multiple annotators to identify the risk of suicide from the keywords and phrases (interrater reliability .79) in geographically based tweets. Thompson, Poulin, and Bryan (2014) and Desmet (2014) used text-based signals to identify suicide risk that ranged from 60% to 90%.