The report provides detailed assessment of key market dynamics and comprehensive information about the structure of the Artificial Intelligence (AI) in …

Federal regulators have cleared dozens of AI products used in health care, which might give the impression that the Food and Drug Administration has a firm handle on a technology that is already changing how patients are treated. But a meeting on AI regulation last week told a different story. The agency is still grappling with fundamental questions about algorithmic bias, data transparency, and how to ensure that patients benefit equally from AI's rapid progress in medicine. Unlock this article by subscribing to STAT and enjoy your first 30 days free! STAT 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.

In September, Azerbaijan and Armenia renewed fighting over Nagorno-Karabakh, a disputed territory in the Caucasus mountains. By then, an information warfare campaign over the region had been underway for several months. The campaign was identified using artificial intelligence technology being developed for US Special Operations Command (SOCOM), which oversees US special forces operations. The AI system, from Primer, a company focused on the intelligence industry, identified key themes in the information campaign by analyzing thousands of public news sources. In practice, Primer's system can analyze classified information too.

Future near-peer adversaries will attempt to contest all domains and utilize complex and congested terrain to mitigate current joint force capabilities and reduce effectiveness of U.S. Department of Defense (DoD) tactical maneuver elements. During Potomac Officers Club's Artificial Intelligence for Maneuver Virtual Event, a panel of expert speakers across the public and private sectors will discuss how the federal government, and its industry partners, can deter or defeat peer threats in contested multi-domain environments. To register for Artificial Intelligence for Maneuver Virtual Event, as well as learn about new upcoming opportunities, visit Potomac Officers Club's Event Page. Christian Dunbar of the Department of the Navy, the panelist will discuss how advances in artificial intelligence and machine learning algorithms can enable human-machine teams to bring greater precision, certainty, speed and mass to the battlefield. The panel will be moderated by Joel Dillon, vice president of Global Defense, Army Account, with Booz Allen Hamilton.

OneZero is partnering with the Big Technology Podcast from Alex Kantrowitz to bring readers exclusive access to interview transcripts with notable figures in and around the tech industry. This week, Kantrowitz sits down with Meredith Whittaker, an A.I. researcher who helped lead Google's employee walkout in 2018. This interview, which took place at World Summit A.I, has been edited for length and clarity. To subscribe to the podcast and hear the interview for yourself, you can check it out on Apple Podcasts, Spotify, and Overcast. When I interviewed Tristan Harris about The Social Dilemma earlier this month, my mentions filled with people saying, "You should speak to the people who were critical of the social web long before the film." One name stood out: Meredith Whittaker. An A.I. researcher and former Big Tech employee, Whittaker helped lead Google's walkout in 2018 amid a season of activism inside the company. On this edition of the Big Technology Podcast, we spoke not only about her views on the film, but also of the future of workplace activism inside tech companies in a moment where some are questioning if it belongs at all. Alex Kantrowitz: It seems like your perspective on The Social Dilemma is a little bit different from Tristan's.

"I have never seen another instance like this in my career," said Bradley Tusk, a venture capitalist and political consultant. It has permeated absolutely everything." Silicon Valley tech workers have long been regarded as liberal but not politically overactive. After President Trump's victory in 2016, however, workers at large tech companies such as Google and Amazon began agitating more on issues like the ethics of artificial intelligence, immigration and climate change. Now many start-up workers, who have been sold on a mission of changing the world, expect their employers to support their social and political causes, entrepreneurs and investors said.

In July 2019, Guillermo Federico Ibarrola was heading home on the subway when he was stopped by Buenos Aires police. The authorities told Ibarrola that he was being detained for an armed robbery that had happened three years ago in a city about 400 miles away. He said he had never even been to the city where he was accused of committing the crime. On the sixth day in police custody, he was suddenly released. The police officers offered Ibarrola coffee and dinner, and a bus ticket back home. As it turned out, a "Guillermo Ibarrola" had potentially committed a crime, but it wasn't this Guillermo Ibarrola.

The moon's shadowed, frigid nooks and crannies may hold frozen water in more places and in larger quantities than previously suspected. And for the first time, the presence of water on the moon's sunlit surface has been confirmed, scientists reported Monday. That's good news for astronauts at future lunar bases who could tap into these resources for drinking and making rocket fuel. While previous observations have indicated millions of tons of ice in the permanently shadowed craters of the moon's poles, a pair of studies in the journal Nature Astronomy take the availability of lunar surface water to a new level. More than 15,400 square miles of lunar terrain have the capability to trap water in the form of ice, according to a team led by the University of Colorado's Paul Hayne.

Production of news content is growing at an astonishing rate. To help manage and monitor the sheer amount of text, there is an increasing need to develop efficient methods that can provide insights into emerging content areas, and stratify unstructured corpora of text into `topics' that stem intrinsically from content similarity. Here we present an unsupervised framework that brings together powerful vector embeddings from natural language processing with tools from multiscale graph partitioning that can reveal natural partitions at different resolutions without making a priori assumptions about the number of clusters in the corpus. We show the advantages of graph-based clustering through end-to-end comparisons with other popular clustering and topic modelling methods, and also evaluate different text vector embeddings, from classic Bag-of-Words to Doc2Vec to the recent transformers based model Bert. This comparative work is showcased through an analysis of a corpus of US news coverage during the presidential election year of 2016.

A sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current design involves an understanding of the complex coupling of physical processes. While sophisticated simulations codes are used to model ICF implosions, these tools contain necessary numerical approximation but miss physical processes that limit predictive capability. Identification of relationships between controllable design inputs to ICF experiments and measurable outcomes (e.g. yield, shape) from performed experiments can help guide the future design of experiments and development of simulation codes, to potentially improve the accuracy of the computational models used to simulate ICF experiments. We use sparse matrix decomposition methods to identify clusters of a few related design variables. Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the variables (laser, hohlraum, and capsule). A variable importance analysis finds that in addition to variables highly correlated with neutron yield such as picket power and laser energy, variables that represent a dramatic change of the ICF design such as number of pulse steps are also very important. The obtained sparse components are then used to train a random forest (RF) surrogate for predicting total yield. The RF performance on the training and testing data compares with the performance of the RF surrogate trained using all design variables considered. This work is intended to inform design changes in future ICF experiments by augmenting the expert intuition and simulations results.