The Canadian government is preparing to welcome thousands of highly skilled tech workers who have been blocked from entering or staying in the U.S. due to President Donald Trump's executive order that effectively suspended new work visas for the remainder of the year. "If you don't want to go to the [U.S.], come to us, we will take you," said Canadian Prime Minister Justin Trudeau. "If you want to bring someone in to work in your company at a certain high-tech job or a high-value job, we'll give you a visa in two weeks to get them to come in. Regardless of what happens in the United States, Canada continues to be welcoming and excited about people coming to Canada." Alex Lu, a 26-year-old Chinese software engineer in San Francisco on an H1-B work visa, is planning to relocate to Canada in two years.

Click here to read the full article. THE WORLD first took notice of Beijing's prowess in artificial intelligence (AI) in late 2017, when BBC reporter John Sudworth, hiding in a remote southwestern city, was located by China's CCTV system in just seven minutes. At the time, it was a shocking demonstration of power. Today, companies like YITU Technology and Megvii, leaders in facial recognition technology, have compressed those seven minutes into mere seconds. What makes those companies so advanced, and what powers not only China's surveillance state but also its broader economic development, is not simply its AI capability, but rather the math power underlying it.

Alex Khizhniak, director of Technical Evangelism at IT services provider Altros, stated, "Being connected to other cars on the road will eventually make driving much safer. Combined with predictive analysis, smart systems could substitute for a driver in case of emergency. Although these technologies are still developing - and some legislations should also be introduced- the future looks promising for self-driving and intelligent driving assistants." While many have been vocal about their concerns regarding the regulation of autonomous or connected cars, there are many advantages that must be considered before delving into the risks. One of the many key benefits of connected cars is that they could contribute to safer traffic patterns in cities with congestion issues as a consequence of rapid urbanization.

Artificial intelligence (AI) and machine learning are allowing both businesses and consumers to boost their cybersecurity to unprecedented levels. In a recent post, we examined six ways that AI is leading the way towards rock-solid information security. In case you missed it, read it here. For this article, we'll take a closer look at how AI and machine learning are letting three major industries safeguard their data better. In each of these sectors, websites not only contain a wealth of sensitive information but also have a high volume of visitors every day.

The public remains sceptical over the use of artificial intelligence (AI) to make decisions, research suggests, with nearly two-thirds wanting tighter regulation around its use. A survey by AI innovation firm Fountech.ai Artificial intelligence is becoming more prominent in large-scale decision-making, with algorithms now being used in areas such as healthcare with the aim of improving speed and accuracy of decision-making. However, the research shows that the public does not yet have complete trust in the technology – 69 per cent say humans should monitor and check every decision made by AI software, while 61 per cent said they thought AI should not be making any mistakes in the first place. The idea of a machine making a decision also appears to have an impact on trust in AI, with 45 per cent saying it would be harder to forgive errors made by technology compared with those made by a human.

Mumbai-based startup Qure uses an AI imaging tool qER to save precious minutes for emergency room staff to take action based on head CT scans. After deployment in India and several other countries, qER is now entering the US where 75 million CT scans are performed every year. A couple of weeks ago, Qure received US FDA 510 (k) clearance for this product. What makes it special is a four-in-one clearance. The tool has been cleared for triaging four critical conditions--intracranial bleeds, mass effect (due to spaces in the brain filling up), midline shift (in the brain's alignment), and cranial fractures.

The Earth's magnetic field could change 10 times faster than previous thought, scientists have said. Using computer simulation of the iron deep beneath our feet that influences how the magnetic field appears to us, they showed that it could move around much more quickly than we had realised. The discovery could have important implications for our understanding of some of the most fundamental processes that power life on Earth: the magnetic field is not only used in compasses and for navigation, but helps protect us from radiation coming from space and keeps our atmosphere in place. The magnetic field is generated and regulated by a swirling flow of molten metal that creates the Earth's outer core. As the liquid iron moves around, it creates electric currents that power the magnetic field that is used on the surface.

Artificial intelligence has revolutionized all sectors with its great capacity to process information. In the cybersecurity space, it is capable of increasing the detection, range and precision of cyberattacks. In this article, we are going to discuss the most important characteristics of artificial intelligence and its relationship with cybersecurity, as well as the challenges that it can present if not properly managed. Through machine learning algorithms we are able to make predictions from past events and it is possible to consider infinite data and scenarios to identify probable events, in order to locate parameters faster, where a potential attack can hide. Machine learning systems can establish security protocols depending on the type of intrusion into the company's systems.

Artificial intelligence (AI): Gartner describes AI as a "technology that … Machine learning can provide the fastest way to identify new cyber-attacks, draw …

The free energy plays a fundamental role in descriptions of many systems in continuum physics. Notably, in multiphysics applications, it encodes thermodynamic coupling between different fields. It thereby gives rise to driving forces on the dynamics of interaction between the constituent phenomena. In mechano-chemically interacting materials systems, even consideration of only compositions, order parameters and strains can render the free energy to be reasonably high-dimensional. In proposing the free energy as a paradigm for scale bridging, we have previously exploited neural networks for their representation of such high-dimensional functions. Specifically, we have developed an integrable deep neural network (IDNN) that can be trained to free energy derivative data obtained from atomic scale models and statistical mechanics, then analytically integrated to recover a free energy density function. The motivation comes from the statistical mechanics formalism, in which certain free energy derivatives are accessible for control of the system, rather than the free energy itself. Our current work combines the IDNN with an active learning workflow to improve sampling of the free energy derivative data in a high-dimensional input space. Treated as input-output maps, machine learning accommodates role reversals between independent and dependent quantities as the mathematical descriptions change with scale bridging. As a prototypical system we focus on Ni-Al. Phase field simulations using the resulting IDNN representation for the free energy density of Ni-Al demonstrate that the appropriate physics of the material have been learned. To the best of our knowledge, this represents the most complete treatment of scale bridging, using the free energy for a practical materials system, that starts with electronic structure calculations and proceeds through statistical mechanics to continuum physics.