On behalf of all at Intel who are focused everyday on supporting you to build, optimize and innovate on Intel architecture, I am proud to announce the launch of Intel Nervana AI Academy. We're at a great moment in the evolution of artificial intelligence (AI) that will open up incredible new experiences fueling the next wave of business opportunity, scientific discovery, and societal improvement. After decades of collectively advancing Intel architecture, we have reached the point that Moore's Law is creating new opportunities for the data science community. We're now supporting artificial intelligence as an enablement technology, through optimized machine and deep learning, providing developers new ways to add value to solutions and applications. Intel is investing heavily in AI and you.

John Charles Thomas was born in Akron, Ohio and attended Ellet High School. He graduated from Case Western Reserve University majoring in psychology and minoring in mathematics and drama. He received a Ph.D. in experimental psychology from the University of Michigan. His dissertation compared human performance in a problem solving task to that of an early AI system called "The General Problem Solver." After graduate school, Dr. Thomas managed a research project on the psychology of aging at Harvard Medical School.

We consider the problem of estimating the expected value of information (the knowledge gradient) for Bayesian learning problems where the belief model is nonlinear in the parameters. Our goal is to maximize some metric, while simultaneously learning the unknown parameters of the nonlinear belief model, by guiding a sequential experimentation process which is expensive. We overcome the problem of computing the expected value of an experiment, which is computationally intractable, by using a sampled approximation, which helps to guide experiments but does not provide an accurate estimate of the unknown parameters. We then introduce a resampling process which allows the sampled model to adapt to new information, exploiting past experiments. We show theoretically that the method converges asymptotically to the true parameters, while simultaneously maximizing our metric. We show empirically that the process exhibits rapid convergence, yielding good results with a very small number of experiments.

A visitor to a Chinese tech fair was injured yesterday after a robot suddenly went out of control and smashed a booth, according to Chinese media. The three-foot-tall droid, which has been launched recently, is designed by a Chinese company to teach children English and is popular among families. The victim sustained cuts in the ankle caused by shattered glass and was taken to the hospital by staff at the booth. The incident took place at the China Hi-Tech Fair held in Shenzhen, southern China, on November, 17, according to a reported on Huanqiu.com, an affiliation to the People's Daily. The robot, which has been named'Little Chubby' by Beijing-based developer Evolver, is programmed to teach English as well as general knowledge to children aged between four and 12.

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

After offering teachers early access to Minecraft: Education Edition this summer, Microsoft's classroom-friendly version of the immensely popular sandbox game was formally launched at the beginning of November. Not everyone is keen on Minecraft being used as a teaching tool, though, and ahead of Microsoft's UK launch event tomorrow, behavior expert for the government's Department for Education Tom Bennett has voiced his skepticism to The Times. "I am not a fan of Minecraft in lessons. This smacks to me of another gimmick which will get in the way of children actually learning," Bennett said. "Removing these gimmicky aspects of education is one of the biggest tasks facing us as teachers. We need to drain the swamp of gimmicks," he continued, mimicking some recent rhetoric from US President-elect Trump.

At about $85 per kit, the Arduino units are composed of a breadboard, resistors, switches, buzzers and LED lights that allow students to create a variety of projects powered by computer coding. Those projects include things like the "Love-O-Meter," – a system that lights up based on temperature - or one modeled after the Magic 8 Ball – a system that provides simple phrases randomly displayed.

We just finished our Lean LaunchPad class at UC Berkeley's engineering school where many of the teams embedded machine learning technology into their products. It struck me as I watched the teams try to find how their technology would solve real customer problems, is that machine learning is following a similar pattern of previous technical infrastructure innovations. Early entrants get sold to corporate acquirers at inflated prices for their teams, their technology, and their tools. Later entrants who miss that wave have to build real products that people want to buy. I've lived through several technology infrastructure waves; the Unix business, the first AI and VR waves in the 1980's, the workstation wave, multimedia wave, the first internet wave.

With wit, vision, and scholarship, Domingos describes how these scientists are creating programs that allow a computer to teach itself.

Many prediction problems can be phrased as inferences over local neighborhoods of graphs. The graph represents the interaction between entities, and the neighborhood of each entity contains information that allows the inferences or predictions. We present an approach for applying machine learning directly to such graph neighborhoods, yielding predicitons for graph nodes on the basis of the structure of their local neighborhood and the features of the nodes in it. Our approach allows predictions to be learned directly from examples, bypassing the step of creating and tuning an inference model or summarizing the neighborhoods via a fixed set of hand-crafted features. The approach is based on a multi-level architecture built from Long Short-Term Memory neural nets (LSTMs); the LSTMs learn how to summarize the neighborhood from data. We demonstrate the effectiveness of the proposed technique on a synthetic example and on real-world data related to crowdsourced grading, Bitcoin transactions, and Wikipedia edit reversions.