On 7 November 2019, the Australian Federal Government's Minister for Industry, Innovation, and Science, Karen Andrews, announced the Australian …

Finland is offering a hi-tech Christmas gift to all European Union citizens - a free-of-charge online course in artificial intelligence, in their own language, officials said on 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 percent of all EU citizens - about five 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 $2m project as "a civics course in AI" to help EU citizens cope with society's ever-increasing digitisation and the possibilities AI offers in the jobs market.

The nation's leading association that fights against diabetes released a new set of clinical standards that for the first time include the use of autonomous artificial intelligence (AI). The American Diabetes Association (ADA)'s 2020 Standards of Medical Care in Diabetes states that, "AI systems that detect more than mild diabetic retinopathy and diabetic macular edema authorized for use by the FDA represent an alternative to traditional screening approaches." To date, IDx-DR is the first and only FDA-authorized autonomous AI diagnostic system for the detection of diabetic retinopathy and macular edema. It is currently in use at a number of large health systems that each serve tens of thousands of people with diabetes and have struggled to implement diabetic retinopathy eye exams at scale for their large diabetes population. "The ADA's inclusion of our technology in its Standards of Care marks a significant move toward mainstream adoption of autonomous AI in clinical care," said Michael Abramoff, MD, PhD, Founder and Executive Chairman at IDx. "Our early customers are visionary leaders who foresaw that autonomous AI would one day become a standard of care for diabetic retinopathy screening, and taking that leap is paying off for them. Already, health systems that are using IDx-DR have experienced significant improvements in accessibility, efficiency and compliance rates, unleashing massive potential for cost savings and improved patient outcomes."

The irony of the ethical scandal enveloping Joichi Ito, the former director of the MIT Media Lab, is that he used to lead academic initiatives on ethics. After the revelation of his financial ties to Jeffrey Epstein, the financier charged with sex trafficking underage girls as young as 14, Ito resigned from multiple roles at MIT, a visiting professorship at Harvard Law School, and the boards of the John D. and Catherine T. MacArthur Foundation, the John S. and James L. Knight Foundation, and the New York Times Company. Many spectators are puzzled by Ito's influential role as an ethicist of artificial intelligence. Indeed, his initiatives were crucial in establishing the discourse of "ethical AI" that is now ubiquitous in academia and in the mainstream press. In 2016, then-President Barack Obama described him as an "expert" on AI and ethics. Since 2017, Ito financed many projects through the $27 million Ethics and Governance of AI Fund, an initiative anchored by the MIT Media Lab and the Berkman Klein Center for Internet and Society at Harvard University.

Advanced Symbolics Inc. (ASI), who are an artificial intelligence-driven market research company, have an AI tool called "Polly". This AI program was shown to be more accurate than many polling companies, such as YouGov, when it came to predicting the U.K. General Election result and the large majority of the Conservative and Unionist Party. According to commentary from ASI's head Erin Kelly, the company and its artificial intelligence technology have a strong record of predicting elections and referenda. The success rate embraces the 2015 Canadian Federal Election, the referendum leading to the U.K.'s exit from the European Union in 2016 ('Brexit'), the U.S. 2016 election heartrending in Donald trump, plus the 2019 Canadian Federal Election. The 2019 UK election surprised many political observers by delivering the Conservative and Unionist Party, led by right-winger Boris Johnson, an 80-set majority over the combined opposition parties.

We discuss why James wrote this book 3 years ago now, how far away he really thinks we are from artificial human intelligence, the warning bells recently being sounded about artificial intelligence, and why he thinks there will not be another AI winter. Our guest today is James Barrat author of the book "Our final Invention" Artificial Intelligence and the end of the Human Era". It's good to be here. Kathleen Walch: [00:00:39] Great, I'd like to get started by having you introduce yourself to our listeners and to tell us a little bit about your book and also what additional things that you're doing in the field of AI and let's go from there. I got into artificial intelligence, or the study of artificial intelligence, and the critique of AI because I made a film about 17 years ago now about artificial intelligence. I interviewed Ray Kurzweil and Rodney Brooks and Arthur C. Clarke among others … and Ray Kurzweil of course who is now chief engineer at Google and the Google brain project.

Nearly every day, in war zones around the world, American military forces request fire support. By radioing coordinates to a howitzer miles away, infantrymen can deliver the awful ruin of a 155-mm artillery shell on opposing forces. If defense officials in Washington have their way, artificial intelligence is about to make that process a whole lot faster. The effort to speed up fire support is one of a handful initiatives that Lt. Gen. Jack Shanahan describes as the "lower consequence missions" that the Pentagon is using to demonstrate how it can integrate artificial intelligence into its weapons systems. As the head of the Joint Artificial Intelligence Center, a 140-person clearinghouse within the Department of Defense focused on speeding up AI adoption, Shanahan and his team are building applications in well-established AI domains--tools for predictive maintenance and health record analysis--but also venturing into the more exotic, pursuing AI capabilities that would make the technology a centerpiece of American warfighting.

Recent concern about harms of information technologies motivate consideration of regulatory action to forestall or constrain certain developments in the field of artificial intelligence (AI). However, definitional ambiguity hampers the possibility of conversation about this urgent topic of public concern. Legal and regulatory interventions require agreed-upon definitions, but consensus around a definition of AI has been elusive, especially in policy conversations. With an eye towards practical working definitions and a broader understanding of positions on these issues, we survey experts and review published policy documents to examine researcher and policy-maker conceptions of AI. We find that while AI researchers favor definitions of AI that emphasize technical functionality, policy-makers instead use definitions that compare systems to human thinking and behavior. We point out that definitions adhering closely to the functionality of AI systems are more inclusive of technologies in use today, whereas definitions that emphasize human-like capabilities are most applicable to hypothetical future technologies. As a result of this gap, ethical and regulatory efforts may overemphasize concern about future technologies at the expense of pressing issues with existing deployed technologies.

In this work, we develop Gaussian process regression (GPR) models of hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy stress tensor as a function of the components of the Finger stretch tensor in a Gaussian process. We then consider an improvement on this approach that embeds rotational invariance of the stress-stretch constitutive relation in the GPR representation. This approach requires fewer training examples and achieves higher accuracy while maintaining invariance to rotations exactly. Finally, we consider an approach that recovers the strain-energy density function and derives the stress tensor from this potential. Although the error of this model for predicting the stress tensor is higher, the strain-energy density is recovered with high accuracy from limited training data. The approaches presented here are examples of physics-informed machine learning. They go beyond purely data-driven approaches by embedding the physical system constraints directly into the Gaussian process representation of materials models.

Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.