Add one more to the growing tally of security funding deals in early 2017. RiskSense, an Albuquerque, NM cyber-risk management company, announced this week that it had raised $14 million in a Series A round of financing. "The funding raised by existing investors Paladin Capital Group, Sun Mountain Capital, EPIC Ventures, and CenturyLink and a new investor Jump Capital will enable RiskSense to expand sales and marketing, enter new markets such as cyber-security insurance, and broaden and accelerate product development," Dr. Srinivas Mukkamala, co-founder and CEO of RiskSense, told eSecurity Planet. Spun off from New Mexico Institute of Mining and Technology and acting as advisors to the U.S. Department of Defense and U.S. Intelligence Community, RiskSense uses of artificial intelligence (AI) technologies, particularly machine learning, to help governments and enterprise organizations identify and prioritize risks to their networks and data. "RiskSense is changing the way organizations detect and manage cyber risk," said Mukkamala.

For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting. PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks. Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function.

As odd as it sounds, chatbots can let customers communicate in a natural way with companies and greatly enhance the experience. As customers increasingly demand a better experience when they interact with companies, including insurers, help is coming from a counterintuitive source. It turns out that one of the best ways to be more personal is through… robots. More precisely, the answer is turning out to be chat robots, or "chatbots." People don't like having to phone call centers and wade through that phone tree -- "Para continuar en espanol, oprima uno… For billing, press 2; for…."

Although China could initially only observe the advent of the Information-Technology Revolution in Military Affairs, the People's Liberation Army might presently have a unique opportunity to take advantage of the military applications of artificial intelligence to transform warfare. When the United States first demonstrated its superiority in network-centric warfare during the first Gulf War, the PLA was forced to confront the full extent of its relative backwardness in information technology. Consequently, the PLA embarked upon an ambitious agenda of "informatization" (???). To date, the PLA has advanced considerably in its capability to utilize information to enhance its combat capabilities, from long-range precision strike to operations in space and cyberspace. Currently, PLA thinkers anticipate the advent of an "intelligentization Revolution in Military Affairs" that will result in a transformation from informatized ways of warfare to future "intelligentized" (???) warfare. For the PLA, this emerging trend heightens the imperative of keeping pace with the U.S. military's progress in artificial intelligence, after its failure to do so in information technology.

If you;re an average retail investor who just saw a 60-minute special on artificial intelligence (AI), you probably thought to yourself;why can;t we use AI for stock trading;? Then maybe you plugged the phrase;artificial intelligence stock trading; into Google and this article popped up.

The reCAPTCHA is the most popular CAPTCHA service made by Google. You've seen it a million times when you sign up a page across the web. Its primary goal is to separate humans from a bot. It challenges users by deciphering a photo of words or numbers or picking objects in a grid of photos. But that process of verifying whether you're a human or not is over because Google is changing it. Recently, the company introduces the invisible reCAPTCHA.

People complain that governments or hackers are reading our messages for nefarious purposes. Of course this "reading" is done automatically, in large volume, by machines and NLP (natural language processing) algorithms looking (among other things) at keywords. The advantage is that the text is still very easy to read by a human being, but unless a specific rule is introduced in NLP algorithms to detect such patterns (this means that the trick would be used by many people,) for automated algorithms, it looks like gibberish. And such rudimentary encoding is straightforward to implement. It is also more compact than encoding text as images using a screenshot capture tool.

On the heels of acquiring data science community Kaggle, Google is launching a machine learning competition of its own for startups. Google is targeting early-stage companies taking an innovative approach to machine learning. The competition is being run in partnership with seven venture capital firms that include Sequoia, KPCB, GV, Data Collective, Emergence Capital, Andreessen Horowitz and Greylock. Two of the firms, Data Collective and Emergence Capital, plan to contribute $500,000 each to the winning startup. To qualify for that prize, startups will not be required to use Google services.

At the beginning when I started working with natural language processing, I used the default Python lists. But soon enough with bigger experiments and more data I run out of RAM. Python lists are not optimized for memory space so onto Numpy. Numpy arrays are much like in C – generally you create the array the size you need beforehand and then fill it. Merging, appending is not recommended as Numpy will create one empty array in the size of arrays being merged and then just copy the contents into it.

Inside the University of Rochester's Robotics and Artificial Intelligence Laboratory, a robotic torso looms over a row of plastic gears and blocks, awaiting instructions. Next to him, Jacob Arkin '13, a doctoral candidate in electrical and computer engineering, gives the robot a command: "Pick up the middle gear in the row of five gears on the right," he says to the Baxter Research Robot. The robot, sporting a University of Rochester winter cap, pauses before turning, extending its right limb in the direction of the object. Baxter, along with other robots in the lab, is learning how to perform human tasks and to interact with people as part of a human-robot team. "The central theme through all of these is that we use language and machine learning as a basis for robot decision making," says Thomas Howard '04, an assistant professor of electrical and computer engineering and director of the University's robotics lab.