Like self-driving cars, drones are often misperceived by the American public. The most common concerns with drones relate to safety, privacy, and security, and we have already seen early regulatory responses from state lawmakers as well as the FAA. Despite increased regulation, Lisa Ellman, partner and co-chair of global UAS practice at Hogan Lovells, said, "Drone fever is here and drones are here to stay, whether or not we have the policy to enable their use." The solution she proposes is "polivation," bringing policymakers together with innovators to ensure policy promotes innovation. This is especially important since innovations such as drones are redefining what qualifies as aircraft.

Once the machines came and took the menial jobs. Artificial intelligence has come of age, says Matthew Partridge. Until recently, artificial intelligence (AI) – machines that can think for themselves – was the technology that was long promised but never quite delivered. Even when the Deep Blue computer defeated chess champion Garry Kasparov in a match in 1997, or when a similar machine solved draughts a decade later (ie, could follow a provably optimal strategy), these victories [...]

Rage Frameworks, a provider of knowledge-based automation technology and services, announced new deployments of its traceable "deep learning" technology known as Rage AI across several global financial services, consumer products and manufacturing firms. The challenges these organizations faced required the understanding and interpretation of complex documents and integration of other transaction data from enterprise resource planning (ERP) systems to identify significant cost efficiencies and compliance conformance. RAGE AI incorporates deep linguistic parsing and proprietary linguistics-based innovations to understand the real meaning of documents and interpret them as a human would, and can operate completely unsupervised or with assistance by human experts. With its traceable, deep learning technology, RAGE AI significantly extends the frontier of deep learning and machine intelligence from "natural language processing" to "natural language understanding." The platform reads and interprets documents within its context, and as a totally transparent solution, RAGE AI enables knowledge workers to move forward confidently knowing the reasoning behind the platform's insights is completely auditable.

Saqib is a core Microsoft developer living in London, who lost the use of his eyes at age 7. He found inspiration in computing and is helping build Seeing AI, a research project that helps people who are visually impaired or blind to better understand who and what is around them. The app is built using intelligence APIs from Microsoft Cognitive Services (www.microsoft.com/cognitive). Access the audio description version of this video at https://youtu.be/3WP7Id8SxYQ

The automatic extraction of features from images is then called to tag the scene in a manner which is close to the human perception of it. For example, image features might include landscape at dusk or broad day time, human or animal interactions, textured image and so on. To this purpose, computer vision algorithms need first to extract salient features of the image and then cluster them according to semantically meaningful groups. Region-growing techniques are utilize to subdivide the image into non-overlapping regions containing salient features. The edge map of the image (in all color channels) is used to determine stiff boundaries for region growing and peaks of the edge distance-transform are used as initial seeds for the process.

Machine learning is not new. SAS has been doing it for over 20 years and some early machine learning papers date back to the 50's. So why is it one of the hottest topics at the Strata Hadoop World conference later this week? Clearly, Hadoop is playing a major role in the increased focus on machine learning. Patrick Hall is a Senior Machine Learning Scientist at SAS.

What are the differences between data science, data mining, machine learning, statistics, operations research, and so on? Here I compare several analytic disciplines that overlap, to explain the differences and common denominators. Sometimes differences exist for nothing else other than historical reasons. Sometimes the differences are real and subtle. I also provide typical job titles, types of analyses, and industries traditionally attached to each discipline.

New Delhi: Accenture Operations, the 7-billion business segment of technology firm Accenture Plc, which deals with process outsourcing, infrastructure consulting and outsourcing, security and cloud services, is betting on automation platforms backed by artificial intelligence (AI) and machine learning for its next phase of growth, said Manish Sharma, senior managing director, Accenture Operations in an interview earlier this week. "Analytics and automation are critical and are high-focus areas for us. We are using automation technologies at scale for our client base," he said. "We are investing a lot of money and time into the AI piece. In automation, there is simple automation like Mini-Bots and then there is high-end automation solutions like Virtual Assistance and artificial or cognitive solutions," he explained.

DARPA today announced the newest of its Grand Challenges, one designed to ensure that the exponentially growing number of military and civilian wireless devices will have full access to the increasingly crowded electromagnetic spectrum. The agency's Spectrum Collaboration Challenge (SC2) will reward teams for developing smart systems that collaboratively, rather than competitively, adapt in real time to today's fast-changing, congested spectrum environment--redefining the conventional spectrum management roles of humans and machines in order to maximize the flow of radio frequency (RF) signals. DARPA officials unveiled the new Challenge before some 8000 engineers and communications professionals gathered in Las Vegas at the International Wireless Communications Expo (IWCE). The primary goal of SC2 is to imbue radios with advanced machine-learning capabilities so they can collectively develop strategies that optimize use of the wireless spectrum in ways not possible with today's intrinsically inefficient approach of pre-allocating exclusive access to designated frequencies. The challenge is expected to both take advantage of recent significant progress in the fields of artificial intelligence and machine learning and also spur new developments in those research domains, with potential applications in other fields where collaborative decision-making is critical.

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