Over the past few years, neural networks have re-emerged as powerful machine-learning models, yielding state-of-the-art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This tutorial surveys neural network models from the perspective of natural language processing research, in an attempt to bring natural-language researchers up to speed with the neural techniques. The tutorial covers input encoding for natural language tasks, feed-forward networks, convolutional networks, recurrent networks and recursive networks, as well as the computation graph abstraction for automatic gradient computation.

Machine Learning is an important concept in computer science and for higher education in general that is developing rapidly. Greg Corrado, a senior research scientist at Google, described the ML basics that educators and IT managers in higher education all need to be aware of. Although machine learning is not entirely new, it has gotten much more attention since last March, when it was used to defeat Lee Sedol, the Go world champion. But even before that, ML has been powering apps like Google photos, speech recognition, text-to-speech converters, and face recognition. The reason it is coming to the forefront now is that the computational resources that it requires have become readily available.

This is how humans operate. We try something, we judge the result and modify our behavior. What some considered to be science fiction only a few years ago, AI is edging closer to reality as decades of research -- combined with advances in compute power, memory, storage, network connectivity, sensors and the software that unites them all -- is poised to enable new classes of intelligent predictive analytics. These innovations will bring benefits to multiple industries, and to society as a whole in the way we lead our everyday lives. Al is going to change our lives for the better as machines learn, reason, act and adapt -- transforming industries by amplifying human capabilities, automating tedious or dangerous tasks, and solving some of our most challenging societal problems.

The new technique makes machine learning a little more like human learning; a more natural fit for natural language processing. In two separate experiments, the new method outperformed conventional machine learning techniques by about 10 percent. Conventional approaches to machine learning information extraction use vast amounts of training data, which increases the capacity of the system to handle difficult problems. The new approach uses much less data, which more realistically represents the amount of info typically available. The system then deals with the limited information in the same way a human would.

In 2011, the National Institute of Informatics initiated a project that would enable a robot with artificial intelligence to gain entry at the University of Tokyo. Like most students, in order to study in the school, all applicants must go through the mandatory entrance exam. University of Tokyo, or Todai, wanted to create an artificial intelligence program that is smart enough to do it. They hoped to have this goal fulfilled in March 2022. However, the team decided that it is abandoning that program when its latest AI robot failed to gain admission at Todai.

Populism is sweeping the nation, but it's likely just getting started. Donald Trump's win is a wake-up call that voters are angry with a system that's made middle-class jobs tougher to come by, and increased inequality. As pronounced as the trend already is, it's only just the beginning, experts say. Looming technological advances will wipe out more jobs, broadening the base of disenfranchised, unemployable and frustrated citizens. Meanwhile, elites with the skills to flourish in the digital economy will get richer.

Deaf-mute communities around the world experience a need in effective human-robot interaction system that would act as an interpreter in public places such as banks, hospitals, or police stations. The focus of this work is to address the challenges presented to hearing-impaired people by developing an interpreting robotic system required for effective communication in public places. To this end, we utilize a previously developed neural network-based learning architecture to recognize Cyrillic manual alphabet, which is used for finger spelling in Kazakhstan. In order to train and test the performance of the recognition system, we collected a depth data set of ten people and applied it to a learning-based method for gesture recognition by modeling motion data. We report our results that show an average accuracy of 77.2% for a complete alphabet recognition consisting of 33 letters.

Amid the wall-to-wall coverage of the U.S. presidential race, it was easy to miss the Obama administration's release this month of a slim, 48-page report titled "Preparing for the Future of Artificial Intelligence." Yet the subject of the report -- and the changes it foreshadows -- may prove to be as consequential for our society, and our education system, as even the most high-stakes national election. The term "artificial intelligence" means different things to different people, but broadly speaking, it refers to computers and advanced machines that can think, reason and communicate like humans, respond to novel or nuanced situations as a person might, and most critically, learn from experiences as a human would. According to a recent survey, 80 percent of AI researchers believe that computers and advanced machines will eventually achieve levels of artificial intelligence that rival human intelligence. Moreover, half believe that this will happen by the year 2040 -- just one generation from now.

As part of a three-week pilot study this quarter, half of the students in STATS 60: "Introduction to Statistical Methods" were assigned a RoboTA, an artificially intelligent robot teaching assistant (TA), to answer their questions by email. Students emailed different addresses based on the TA they were assigned. The emails were then funneled through a program, stripped of any identifying information and relayed to Lucas Janson, the TA for the class. Janson replied to all the emails without knowing whether they were intended for the human TA or the artificial intelligence (AI) TA. This study is double-blind, so neither the students nor the experimenters have information about the other, which helps prevent bias.

Intel announced a slew of products, technologies and investment in an effort to fix its position in the field of artificial intelligence. In the new move, Intel has assembled a set of technology options to drive AI capabilities in everything from smart factories and drones to sports, fraud detection and autonomous cars. Intel is increasing its focus on AI as it believes it can power the AI products released recently by companies like Facebook and Google. In a blog Intel CEO Brian Krzanich had said, "Intel is uniquely capable of enabling and accelerating the promise of AI. Intel is committed to AI and is making major investments in technology and developer resources to advance AI for business and society."