Estimators in the previous two cases often have to solve intractable optimization problems, which leads to approximations and local maxima: you don't know quite what you'll get. But in simple settings, the errors of both approaches can be analyzed, which gratifies the people at the left vertex. Frequentist statisticians and COLT folks (computational learning theorists) cluster around that vertex; e.g., they solve convex optimization problems and try to bound the error. Examples include spectral learning, SVMs, and other convex or closed-form frequentist estimators.

Estimators in the previous two cases often have to solve intractable optimization problems, which leads to approximations and local maxima: you don't know quite what you'll get. But in simple settings, the errors of both approaches can be analyzed, which gratifies the people at the left vertex. Frequentist statisticians and COLT folks (computational learning theorists) cluster around that vertex; e.g., they solve convex optimization problems and try to bound the error. Examples include spectral learning, SVMs, and other convex or closed-form frequentist estimators. For my take on the different priorities of frequentists and Bayesians, see here.

Despite plans to invest in machines and artificial intelligence as part of their strategy to boost productivity, many automotive and industrial equipment companies are failing to implement the measures needed to harness these capabilities, according to a new report from Accenture. The report, "Machine dreams: Making the Most of the Connected Industrial Workforce," is based on interviews with more than 500 business executives in Asia, Europe and the United States involved in setting their company's strategy for the connected industrial workforce. According to the report, manufacturing and production are undergoing rapid change as machines and AI are becoming closely integrated with personnel, creating the connected industrial workforce. By combining mobile, safety and tracking technologies with analytics, companies are enhancing the activities of an industrial worker. The report concludes that the creation of a connected industrial workforce is already part of the business strategy of the majority of automotive and industrial equipment producers, cited by 94 percent of respondents.

Mainland Chinese start-up Horizon Robotics, founded by the former head of online search giant Baidu's Institute of Deep Learning, claims it is on pace to bring chips with built-in artificial intelligence (AI) technology to market. "General processors are too slow for AI functions. A dedicated chip will dramatically increase the speed of these functions," Yu Kai, the founder and chief executive of Horizon Robotics told the South China Morning Post. Founded in Beijing in July, Horizon Robotics is developing chips and software that attempt to mimic how the human brain solves abstract tasks, such as voice and image recognition, that are difficult for regular computer programmes. It also makes sensors for smart devices.

A car's driver doesn't necessarily have to be human: The artificial intelligence behind Google Inc.'s self-driving system could count, according to federal highway safety officials. In a letter posted on the National Highway Traffic Safety Administration's website, the agency responded to Google's request for interpretation of several federal safety standards as they apply to the tech giant's self-driving cars. As a premise of the interpretation, "NHTSA will interpret'driver' in the context of Google's described motor vehicle design as referring to the [self-driving system], and not to any of the vehicle occupants," Chief Counsel Paul Hemmersbaugh said in the letter. "We agree with Google its [self-driving vehicle] will not have a driver in the traditional sense that vehicles have had drivers during the last more than 100 years." Google's not-so-secret special projects lab, Google X, is housed in an old shopping mall near Mountain View, Calif.

Artificial intelligence (AI) is already in the classroom: as digital textbooks that include question-and- answer simulations; as intelligent nano-tutors to help students work through complex problems and as intelligent systems to grade student assignments. Ashok Goel teaches Knowledge-Based AI as part of the Institute's Online Master of Science in Computer Science (OMS CS) program. He says he and his peers are on the verge of ushering AI into higher education in bold, new ways. What's next are virtual teaching assistants (VTAs). This modern form of AI will become omnipresent and available on demand for students.

Google recently open-sourced its Artificial Intelligence/Numerical Computing library called TensorFlow. TensorFlow was developed by members of the Google Brain team, and has the flexibility to run on a variety of platforms – including GPUs and mobile devices. TensorFlow's methodology uses what they called data-flow graphs. As you probably understood, the graphical structure is a way of representing a computational expression in the form of a Tree. Every node is an operation (TensorFlow calls them ops, short for operations).

Cybersecurity threats are among the most pressing concerns for businesses and institutions that need to protect information, but today's security systems are limited. Most security systems fall into two categories: human analyst or machine learning. Now, a new research paper from MIT shows that a combination of human experts with a machine learning system--in other words, supervised machine learning--provides better results than either human or machine alone. "AI squared," which uses a system developed by PatternEx, is 10 times better at catching threats than machine learning alone, and reduces false positives by a factor of five. This, said MIT's researchers, is three times better than current benchmarks.

Customers are becoming "impatient narcissists," according to Rick Davidson, president and CEO of the consultancy Cimphoni. "They want what they want, and they want it now," he said during this presentation on cognitive computing for the enterprise at the recent Fusion CEO-CIO Symposium in Madison, Wis. Today's technology-connected customers care about wait times, ease of use and responsiveness. The deeper the technology is integrated into their lives, the more they'll expect from the companies they do business with, Davidson warned. CIOs should begin to consider the machine learning and artificial intelligence (AI) investments they'll likely have to make to meet evolving customer expectations.

Recent advances in deep learning have enabled the extraction of high-level features from raw sensor data which has opened up new possibilities in many different fields, including computer generated choreography. We have in collaboration with The Lulu Art group developed a system, chor-rnn, for generating novel choreographic material in the nuanced choreographic language and style of an individual choreographer. It also shows promising results in producing a higher level compositional cohesion, rather than just generating sequences of movement. At the core of chor-rnn is deep recurrent neural network trained on raw motion capture data and that can generate new dance sequences for a solo dancer. Chor-rnn can be used for collaborative human-machine choreography or as a creative catalyst, serving as inspiration for a choreographer.