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.

I am giving a talk (in French) at the 85th edition of the ACFAS congress, May 9. I will discuss the engineering aspects of doing machine learning. But more importantly, I will discuss how Semantic Web techniques, technologies and specifications can help solving the engineering problems and how they can be leveraged and integrated in a machine learning workflow. The focus of my talk is based on my work in the field of the semantic web in the last 15 years and my more recent work creating the KBpedia Knowledge Graph at Cognonto and how they influenced our work to develop different machine learning solutions to integrate data, to extend knowledge structure, to tag and disambiguate concepts and entities in corpuses of texts, etc. One thing we experienced is that most of the work involved in such project is not directly related to machine learning problems (or at least related to the usage of machine learning algorithms). And then I recently read a survey conducted by CrowdFlower in 2016 that support what we experienced.

The concept has been around for decades and most of us use it everyday without thinking about it. However, AI represents the next wave in marketing innovation for tech marketers and will become more prevalent in 2017 and beyond. AI is a collection of technologies and algorithms that do things that require human intelligence such as learning, understanding natural language, image recognition and problem solving. A simple example is when someone calls your iPhone an algorithm searches your phone data for a possible match to identify the caller. Another common use case is bidding/optimization tools on programmatic ad platforms; and ad A/B testing is conducted by machine learning and AI tools.