In GTC China yesterday, NVIDIA made a series of announcements. Some had to do with local partners and related achievements, such as powering the likes of Alibaba and Baidu. Partners of this magnitude are bound to generate impressive numbers and turn some heads. Another part of the announcements had to do with new hardware. NVIDIA unveiled Orin, a new system-on-a-chip (SoC) designed for autonomous vehicles and robots, as well as a new software-defined platform powered by the SoC, called Nvidia Drive AGX Orin.

TED talks are simply fascinating. They provide tightly knit stories in short doses with mind-blowing information and experiences. It is amazing how much knowledge has been shared in this world using this simple and powerful medium. With Artificial Intelligence and Machine Learning getting so much attention in the spheres of research and business, I started looking out for TED talks on Artificial Intelligence in particular. I was in for such a treat – information treat to be precise. I gained much more from watching these TED talks than I have from following some of the most popular YouTube channels out there.

About the talk In this talk we first consider the situation where one is interested in gaining understanding of general dynamical properties of a chaotically time evolving system solely through access to time series measurements that depend on the evolving state of an, otherwise unknown, system. Using examples, we show that machine learning is an extremely effective tool for accomplishing this task, and we discuss how the ability to do this can be of practical utility. In the second part of the talk, we turn the problem around and utilize chaos theory to explain the dynamical basis for how a machine learning system is able to do accomplish this task [Z. About the speaker Edward Ott received his Ph.D. from The Polytechnic Institute of Brooklyn and was an NSF Postdoctoral Fellow at Cambridge University, following which he became a faculty member of the Department of Electrical Engineering at Cornell University. After 11 years at Cornell, he moved to the University of Maryland where he is currently a Distinguished University Professor in the Department of Physics and the Department of Electrical and Computer Engineering.

For years, people have tried to re-engineer learning with artificial intelligence, but it was not until the machine-learning revolution of the past seven years that real progress has been made. Slowly, algorithms are making their way into classrooms, taking over repetitive tasks like grading, optimizing coursework to fit individual student needs and revolutionizing the preparation for College Board exams like the SAT. A plethora of online courses and tutorials also have freed teachers from lecturing and allowed them to spend class time working on problem solving with students instead. While that trend is helping people like Mrs. Turner teach, it has just begun. Researchers are using A.I. to understand how the brain learns and are applying it to systems that they hope will make it easier and more enjoyable for students to study.

Finland is offering a techy Christmas gift to all European Union citizens -- a free-of-charge online course in artificial intelligence in their own language, officials said Tuesday. The tech-savvy Nordic nation, led by the 34-year-old Prime Minister Sanna Marin, is marking the end of its rotating presidency of the EU at the end of the year with a highly ambitious goal. Instead of handing out the usual ties and scarves to EU officials and journalists, the Finnish government has opted to give practical understanding of AI to 1% of EU citizens, or about 5 million people, through a basic online course by the end of 2021. It is teaming up with the University of Helsinki, Finland's largest and oldest academic institution, and the Finland-based tech consultancy Reaktor. Teemu Roos, a University of Helsinki associate professor in the department of computer science, described the nearly $2 million project as "a civics course in AI" to help EU citizens cope with society's ever-increasing digitalization and the possibilities AI offers in the jobs market.

News that Pearson, the world's largest textbook publisher, is phasing out print publications for higher education to adopt a resolutely digital-first policy may signal an eventual full stop for traditional book learning. In the view of Mike Buchanan, executive director of HMC, which represents independent school head teachers, digital education will unlock a less rigid approach to classroom-based learning, as well as enable closer collaboration with pupils' families. "In a growing number of schools, the use of modern management information and recording systems to harvest details of classroom activities and pupil progress is already allowing parents to access and aggregate their child's attainment records," he says. "In the future, this will no doubt see the traditional termly report being replaced by daily digital updates." Mr Buchanan predicts individual academic achievement will be charted by artificial intelligence (AI), rather than by a plethora of exams, and argues that for teachers disenchanted by the current need to "teach to the test", the freedom to pursue a more rounded curriculum will foster a new optimism.

Learning to Program An introduction to programming for those who have never programmed before, by Alan Gauld. It introduces several programming languages but has a strong emphasis on Python.

Andrew Ng is most of the world's most prominent AI scientist's and educators. On this episode of CxOTalk, he shares practical advice for adopting AI in the enterprise. Dr. Andrew Ng is a globally recognized leader in AI (Artificial Intelligence). He has helped two of the world's leading technology companies in their "AI transformation": He was Chief Scientist at Baidu, where he led the company's 1300 person AI Group and was responsible for driving the company's global AI strategy and infrastructure. He was also the founding lead of the Google Brain team.

I will also discuss the common technical challenges of executing A/B tests on ML algorithms, such as infrastructure requirements, connecting online and offline metrics, and handling ramp up periods for online learning algorithms. Overall, the goal of this talk will be to motivate ML practitioners to use A/B testing when evaluating their algorithms and provide them with high-level guidelines on how to do it. Profile Pavel Dmitriev is a Vice President of Data Science at Outreach, where he works on enabling data driven decision making in sales through experimentation and machine learning. He was previously a Principal Data Scientist with Microsoft's Analysis and Experimentation team, where he worked on scaling experimentation in Bing, Skype, and Windows OS. Pavel co-authored numerous papers at top-tier data mining and machine learning conferences, such as WWW, ICSE, KDD, has given keynotes and tutorials at WWW, SIGIR, SEAA, and KDD.

The simultaneous control of multiple coordinated robotic agents represents an elaborate problem. If solved, however, the interaction between the agents can lead to solutions to sophisticated problems. The concept of swarming, inspired by nature, can be described as the emergence of complex system-level behaviors from the interactions of relatively elementary agents. Due to the effectiveness of solutions found in nature, bio-inspired swarming-based control techniques are receiving a lot of attention in robotics. One method, known as swarm shepherding, is founded on the sheep herding behavior exhibited by sheepdogs, where a swarm of relatively simple agents are governed by a shepherd (or shepherds) which is responsible for high-level guidance and planning. Many studies have been conducted on shepherding as a control technique, ranging from the replication of sheep herding via simulation, to the control of uninhabited vehicles and robots for a variety of applications. We present a comprehensive review of the literature on swarm shepherding to reveal the advantages and potential of the approach to be applied to a plethora of robotic systems in the future.