HeartVista, a pioneer in AI-assisted MRI solutions, announced the formation of its Medical and Scientific Advisory Board, including notable thought leaders from Stanford University and the University of Wisconsin. "The past year was an inflection point for HeartVista, which was full of significant milestones as we received FDA 510(k) clearance for our AI-assisted One Click Cardiac Package," said Itamar Kandel, CEO of HeartVista. "This year, we will continue to progress our MRI software platform and expand its use across additional radiology centers within the US and globally. Our Medical and Scientific Advisory Board will provide strategic direction to our leadership team, enabling us to continue advancing the MRI field." "Automated, AI-driven prescription, as pioneered by HeartVista will change the way we perform advanced MRI exams, by dramatically reducing exam time, standardizing acquisitions, reducing error and rework, and ultimately improve the patient experience," said Dr. Scott Reeder, Vice Chair of Research and Chief of MRI, University of Wisconsin School of Medicine.

Just-released proposals out of Europe that call for new rules to regulate high-risk artificial intelligence systems provide another marker for U.S. insurers and regulators as they consider the opportunities and risks of this evolving technology, industry experts say. The proposals and accompanying data strategy unveiled Feb. 19 are part of the EU's broader digital strategy aimed at setting global standards on technological development that put people first. In its report, the European Commission says that while artificial intelligence can bring advances by tackling climate change and making production more efficient, it also "entails a number of risks, such as opaque decision-making, gender-based or other kinds of discrimination, intrusion in our private lives, or being used for criminal purposes." Jon Godfread, insurance commissioner for North Dakota, said the policy document is "another fencepost and guideline that we can all take a look at" as international discussions on how to regulate artificial intelligence continue to develop. The EU's risk-based regulatory approach outlined in the report says clear rules are needed for high-risk artificial intelligence systems in recruitment, health care, transport, energy and law enforcement so that they are "transparent, traceable and guarantee human oversight."

The face recognition system is designed to be used by drones. The U.S. Special Operations Command (SOCOM) is developing a portable facial recognition system that can identify individuals from 1 kilometer (0.6 miles) away. The Advanced Tactical Facial Recognition at a Distance Technology project demonstrated a working prototype last year; its use could be extended to drones. Long-range face-recognition device manufacturer Secure Planet is developing the system, which must render captured images as pictures that are sufficiently clear for software to identify. Secure Planet bases its devices on digital single-lens reflex cameras with commercial face-recognition software running on a standard laptop.

Artificial Intelligence is a technology used to plan for the future. Planification implies intelligibility, calculability, and systematization. The future as a concept has been, in occidental cultures, closely tied to monotheism and the development of a linear narrative about societies, with a predicted end of the world, where individuals end up either in paradise or hell. This was a radical change from the narratives of classic cultures, where there was no notion of the past or prehistory, but rather a narrative of a cultural, god-given origin similar to the present. It did not anticipate change in the manner of future narratives. Future narratives see the time to come as a time when evolution happens, when neither clothes nor context nor social habits remain the same. With the development of Protestantism and capitalism, the future became more than a point in time when the story would end. It became an unwritten point of opportunity to be shaped by human beings.

Wind-generated electricity has expanded greatly over the past decade. In the U.S., for example, by 2018 wind was generating 6.6% of utility-scale electricity generation, according to the U.S. Energy Information Administration. The criteria for efficient design and reliable operation of the familiar horizontal-axis wind turbines have been well established through decades of experience, leading to ever-larger structures over time, both to intercept more wind and to reach faster winds higher up. As these gargantuan turbines are assembled into large wind farms, often spread over uneven terrain, complex aerodynamic interactions between them have become increasingly important. To address this issue, researchers have proposed protocols that slightly reorient individual turbines to improve the output of others downwind, and they are working with wind farm operators to assess their real-life performance.

Mind Foundry has been a pioneer in the development and use of'humble and honest' algorithms from the very beginning of its applications development. As Davide Zilli, Client Services Director at Mind Foundry explains, 'baked in' transparency and explainability will be vital in winning the fight against biased algorithms and inspiring greater trust in AI and ML solutions. Today in so many industries, from manufacturing and life sciences to financial services and retail, we rely on algorithms to conduct large-scale machine learning analysis. They are hugely effective for problem-solving and beneficial for augmenting human expertise within an organisation. But they are now under the spotlight for many reasons – and regulation is on the horizon, with Gartner projecting four of the G7 countries will establish dedicated associations to oversee AI and ML design by 2023.

Artificial intelligence offers Britain the opportunity to have a world-leading school system. We have a good education system, but it is not innovative nor exciting. Nor is it in tune with the post-Brexit world. AI is transforming every aspect of the human experience. Britain is making considerable progress in applying it to healthcare, to the professions and to industry, but despite good progress schooling remains the Cinderella of AI.

Defenses against adversarial attacks, which in the context of AI refer to techniques that fool models through malicious input, are increasingly being broken by "defense-aware" attacks. In fact, most state-of-the-art methods claiming to detect adversarial attacks have been counteracted shortly after their publication. To break the cycle, researchers at the University of California, San Diego and Google Brain, including Turing Award winner Geoffrey Hinton, recently described in a preprint paper an approach that deflects attacks in the computer vision domain. Their framework either detects attacks accurately or, for undetected attacks, pressures the attackers to produce images that resemble the target class of images. The proposed architecture comprises (1) a network that classifies various input images from a data set and (2) a network that reconstructs the inputs conditioned on parameters of a predicted capsule.

Any sufficiently advanced technology is indistinguishable from magic. In the world of artificial intelligence & machine learning (AI & ML), black- and white-box categorization of models and algorithms refers to their interpretability. That is, given a model trained to map data inputs to outputs (e.g. And just as the software testing dichotomy is high-level behavior vs low-level logic, only white-box AI methods can be readily interpreted to see the logic behind models' predictions. In recent years with machine learning taking over new industries and applications, where the number of users far outnumber experts that grok the models and algorithms, the conversation around interpretability has become an important one.

A team of researchers at Leiden University in the Netherlands have developed a neural network called "Hazardous Object Identifier" that they say can predict if an asteroid is on a collision course with Earth. Their new AI singled out 11 asteroids that were not previously classified by NASA as hazardous, and which were larger than 100 meters in diameter -- big enough to explode with the force of hundreds of nuclear weapons if they impacted Earth, potentially leveling entire cities. They also focused on space rocks that could come within 4.7 million miles of Earth, as detailed in a paper published in the journal Astronomy & Astrophysics earlier this month. None are an imminent threat, however: not only are their chances of ever hitting Earth astronomically slim, but they are making their flyby between the years 2131 and 2923 -- hundreds of years from now. The team then reversed the simulation, simulating future Earth-impacting asteroids by flinging them away from Earth and tracking their exact locations and orbits.