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.

In previous articles, we've talked about the merits of artificial intelligence and big data and how these technologies can enable a multitude of industries to begin learning how to do things more effectively. One area where these technologies can be used is in digital marketing. Also referred to as "programmatic marketing", AI and big data can be used to figure out what digital ad to serve you up at any given time to increase the likelihood that you'll click on it. While we've said before that you can't invest in artificial intelligence yet as a retail investor, we did come across one publicly traded company called Rocket Fuel (NASDAQ:FUEL) which is playing in the "programmatic marketing" space and while their value proposition sounds exciting, there's much more to this company than meets the eye. Founded in 2008, Rocket Fuel uses artificial intelligence and big data to determine which ad is best to serve at any given moment in order to increase the likelihood of you clicking on that ad, and then engaging with the advertiser.

It's a notion marketers will drool over: Imagine if their overloaded job responsibilities could be wiped clean by the use of robots to do tedious marketing tasks for them. Just as we already use programmatic buying and other data-driven marketing tools to simplify difficult and time-consuming processes, artificial intelligence (AI) is viewed by both robotics experts and marketing professionals as a tool to expedite menial content creation in the future. Efforts to bring AI to the mainstream are underway. IBM's Watson AI is already making appearances in ads holding conversations with celebrities. Robotic writing solutions have been used for simple writing tasks, such as recapping sporting events.

The experienced Machine Learning Technologist will have general knowledge in different techniques applied in machine learning such as supervised learning and unsupervised learning, and of basic concepts in probabilities and applied statistics. The types of algorithmic solutions should span across different types of data ranging from numeric, textual (structured or unstructured), images, and video. The candidate for this position will be able to effectively apply machine learning and data mining to different types of data collected from tasks and domains such as manufacturing, multimodal sensors, images and video, and online documents. They will have the general ability to design, apply, and create new algorithms, methods, and tools for the analysis of data to address project requirements. They will have the general ability to evaluate the performance of data analysis algorithms as well as general ability to select and apply algorithms to meet application requirements with respect to scalability.