The Food and Drug Administration has allowed medical devices that rely on artificial intelligence algorithms onto the market, but so far, the agency has given the green light only to devices with "locked algorithms" -- those that remain the same as the product is used until they're updated by the manufacturer. Systems with algorithms that evolve and sharpen on their own, however, are already in development. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free! STAT Plus is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis. Our award-winning team covers news on Wall Street, policy developments in Washington, early science breakthroughs and clinical trial results, and health care disruption in Silicon Valley and beyond.

Up to 30 additional Alexander von Humboldt Professorships in the field of Artificial Intelligence are to be filled in the years up to 2024. Through these professorships, the Alexander von Humboldt Foundation intends to contribute to the German government's Artificial Intelligence Strategy which targets the establishment of new AI chairs in Germany. Alexander von Humboldt Professorships are financed by the Federal Ministry of Education and Research. The award comes with €5 million in funding for individuals conducting experimental research and €3.5 million for researchers working in theoretical fields. The award brings top international researchers from abroad to German universities and offers them long-term prospects for conducting research in Germany.

In 1953, US senators grilled General Motors CEO Charles "Engine Charlie" Wilson about his large GM shareholdings: Would they cloud his decision making if he became the US secretary of defense and the interests of General Motors and the United States diverged? Wilson said that he would always put US interests first but that he could not imagine such a divergence taking place, because, "for years I thought what was good for our country was good for General Motors, and vice versa." Although Wilson was confirmed, his remarks raised eyebrows due to widespread skepticism about the alignment of corporate and societal interests. The skepticism of the 1950s looks quaint when compared with today's concerns about whether business leaders will harness the power of artificial intelligence (AI) and workplace automation to pad their own pockets and those of shareholders--not to mention hurting society by causing unemployment, infringing upon privacy, creating safety and security risks, or worse. But is it possible that what is good for society can also be good for business--and vice versa?

A US federal appeals court has rejected Facebook's effort to undo a class action lawsuit alleging it illegally collected and stored biometric data for millions of users without their consent using facial recognition technology. The 3-0 decision from the ninth US circuit court of appeals in San Francisco exposes the company to billions of dollars in potential damages paid out to the Illinois users who brought the case. The decision came as the social media company faces broad criticism from American politicians, lawmakers and regulators over its privacy practices. Last month, Facebook agreed to pay a record $5bn (£4bn) fine to settle a Federal Trade Commission (FTC) data privacy investigation. "This biometric data is so sensitive that if it is compromised, there is simply no recourse," Shawn Williams, a lawyer for plaintiffs in the class action, said in an interview.

What is it exactly that makes humans so smart? In his seminal 1950 paper, "Computer Machinery and Intelligence," Alan Turing argued human intelligence was the result of complex symbolic reasoning. Philosopher Marvin Minsky, cofounder of the artificial intelligence lab at the Massachusetts Institute of Technology, also maintained that reasoning--the ability to think in a multiplicity of ways that are hierarchical--was what made humans human. Patrick Henry Winston begged to differ. "I think Turing and Minsky were wrong," he told me in 2017. "We forgive them because they were smart and mathematicians, but like most mathematicians, they thought reasoning is the key, not the byproduct." Winston, a professor of computer science at MIT, and a former director of its AI lab, was convinced the key to human intelligence was storytelling. "My belief is the distinguishing characteristic of humanity is this keystone ability to have descriptions with which we construct stories. I think stories are what make us different from chimpanzees and Neanderthals. And if story-understanding is really where it's at, we can't understand our intelligence until we understand that aspect of it."

Zindi is convening Africa's data scientists to create AI solutions for complex problems. Founded in 2018, the Cape Town-based startup allows companies, NGOs or government institutions to host online competitions around data-oriented challenges. Zindi's platform also coordinates a group of more than 4,000 data scientists based in Africa who can enroll to join a competition, submit their solution sets, move up a leader board and win the challenge -- for a cash prize payout. The highest purse so far has been $12,000, split across the top three data scientists in a competition, according to Zindi co-founder Celina Lee. Competition hosts receive the results, which they can use to create new products or integrate into their existing systems and platforms. Zindi's model has gained the attention of some big corporate names in and outside of Africa.

High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially characterize the degree of chemical ordering and identify order-disorder transitions through efficient simulation and modeling of thermodynamics. In this study, a robust data-driven framework based on Bayesian approaches is proposed and demonstrated on the accurate and efficient prediction of configurational energy of high entropy alloys. The proposed effective pair interaction (EPI) model with ensemble sampling is used to map the configuration and its corresponding energy. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. Compared with the arbitrary determination of model complexity, we further conduct a physical feature selection to identify the truncation of coordination shells in EPI model using Bayesian information criterion. The results achieve efficient and robust performance in predicting the configurational energy, particularly given small data. The developed methodology is applied to study a series of refractory HEAs, i.e. NbMoTaW, NbMoTaWV and NbMoTaWTi where it is demonstrated how dataset size affects the confidence we can place in statistical estimates of configurational energy when data are sparse. Introduction As one of the typical multicomponent alloys, high entropy alloys (HEAs) consisting of four or more principal elements have been widely studied due to their exceptional mechanical properties [1, 2, 3, 4].

There is an increasing interest in estimating heterogeneity in causal effects in randomized and observational studies. However, little research has been conducted to understand heterogeneity in an instrumental variables study. In this work, we present a method to estimate heterogeneous causal effects using an instrumental variable approach. The method has two parts. The first part uses subject-matter knowledge and interpretable machine learning techniques, such as classification and regression trees, to discover potential effect modifiers. The second part uses closed testing to test for the statistical significance of the effect modifiers while strongly controlling familywise error rate. We conducted this method on the Oregon Health Insurance Experiment, estimating the effect of Medicaid on the number of days an individual's health does not impede their usual activities, and found evidence of heterogeneity in older men who prefer English and don't self-identify as Asian and younger individuals who have at most a high school diploma or GED and prefer English.

NEW YORK – A federal appeals court on Thursday rejected Facebook Inc.'s effort to undo a class action lawsuit claiming that it illegally collected and stored biometric data on millions of users without their consent. The 3-0 decision from the 9th U.S. Circuit Court of Appeals in San Francisco over Facebook's facial recognition technology exposes the company to billions of dollars in potential damages to the Illinois users who brought the case. It came as the social media company faces broad criticism from lawmakers and regulators over its privacy practices. Last month, Facebook agreed to pay a record $5 billion fine to settle a Federal Trade Commission data privacy probe. "This biometric data is so sensitive that if it is compromised, there is simply no recourse," Shawn Williams, a lawyer for plaintiffs in the class action, said in an interview.

In September 2018, Hurricane Florence devastated the eastern U.S., forcing more than 1.7 million people to evacuate their homes. Immediately after the storm, federal relief agencies started collecting imagery to identify flooded areas and damaged infrastructure to rescue, relieve, and rebuild affected communities, but current analysis methods can be slow and inefficient. The Department of Defense's Joint Artificial Intelligence Center (JAIC) and the Johns Hopkins Applied Physics Laboratory (APL) teamed to quickly build AI-enabled capabilities that accelerated that analysis; the results immediately increased the effectiveness of relief efforts. Learn how the JAIC/APL team used deep learning algorithms in this work, and preview upcoming tech and new AI-based disaster response tools.