Penn State has launched an expanded initiative in artificial intelligence (AI), termed the AI Hub. It will bring together the University's considerable resources and talent in AI to further advance its position as a global leader developing fundamental innovations in AI, in using AI and machine learning (ML) to solve the hardest challenges, and to create unique applications of AI and ML in unforeseen areas. The hub also will help to address a national priority for the U.S. to be the world leader in developing responsible AI. Lora Weiss, senior vice president for research, announced the initiative, which is designed to forge collaborations among Penn State's AI researchers, centers and institutes and increase the visibility and impact of Penn State's AI research. "Penn State is poised to take AI to new levels where researchers are already creating new knowledge and discoveries with AI, from tackling climate change to improving health outcomes to developing new materials," said Weiss.

So much happened in the auto industry this month it can't fit in one column; I whittled it down to the things that interested me. There is a lot of variety by topic -- from Auto Shanghai to a new operating system to an interesting DARPA story to the European Union proposal for AI use regulation. These ten stories are summarized in the following table. Auto Shanghai Auto Shanghai is among the world's largest auto shows and is the first auto show to be held after the pandemic. The attendance is expected to reach about 1 million people and around 1,000 exhibitors.

Research and development in new technologies for migration management are rapidly increasing. To quote certain migration examples, big data was used to predict population movements in the Mediterranean, AI lie detectors used at the European border, and the recent one is the government of Canada using automated decision-making in immigration and refugee applications. Artificial intelligence in migration is helping countries to manage international migration. Every corner of the world is encountering an unprecedented number of challenging migration crises. As an increasing number of people are interacting with immigration and refugee determination systems, nations are taking a stab at artificial intelligence. AI in global immigration is helping countries to automate a plethora of decisions that are made almost daily as people want to cross borders and look for new homes.

The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.

The developed world is on the cusp of turning the corner in the fight against COVID-19 thanks to the unprecedented effort to rapidly develop and distribute effective vaccines. Now technologists are hoping to take drug development to the next level, and AI will play a big role. One of the companies at the forefront of using machine learning and AI to develop drugs is CytoReason. The company helps pharmaceutical firms like Pfizer accelerate drug development by providing high resolution models of the human body that's infected with the disease that the drug companies are targeting. "If I told you that in 200 years, drugs would be developed in a computer, you would not be real surprised," said CytoReason CEO and founder David Harel.

Five federal financial regulatory agencies are gathering insight on financial institutions' use of artificial intelligence (AI).

MOST LAWS are local--except in the digital realm. When the European Union comes up with some new tech regulation, it can quickly spread around the world. Global companies adopt its typically strict rules for all their products and markets in order to avoid having to comply with multiple regimes. Other governments take more than one page from the EU's rule book to help local firms compete. The textbook example for what has been dubbed the "Brussels effect", is the EU's General Data Protection Regulation (GDPR), which went into force in 2018 and swiftly became the global standard. Your browser does not support the audio element.

Indonesia's military on Sunday officially said all 53 crew members from a submarine that sank and broke apart last week are dead, and that search teams had located the vessel's wreckage on the ocean floor. Officials previously said the KRI Nanggala 402's oxygen supply would have run out early Saturday, three days after the vessel went missing off the resort island of Bali. "We received underwater pictures that are confirmed as parts of the submarine, including its rear vertical rudder, anchors, outer pressure body, embossed dive rudder and other ship parts," military chief Hadi Tjahjanto told reporters in Bali on Sunday. "With this authentic evidence, we can declare that KRI Nanggala 402 has sunk and all the crew members are dead," Tjahjanto said. An underwater robot equipped with cameras documented the lost submarine lying in at least three pieces on the ocean floor at a depth of 2,750 feet, said Adm. Yudo Margono, the navy's chief of staff.

Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of established methods and also for making substantial and serendipitous discoveries. Beyond vast amounts of complex data ubiquitous in many modern scientific fields, the study of the ocean poses a combination of unique challenges that ML can help address. The observational data available is largely spatially sparse, limited to the surface, and with few time series spanning more than a handful of decades. Important timescales span seconds to millennia, with strong scale interactions and numerical modelling efforts complicated by details such as coastlines. This review covers the current scientific insight offered by applying ML and points to where there is imminent potential. We cover the main three branches of the field: observations, theory, and numerical modelling. Highlighting both challenges and opportunities, we discuss both the historical context and salient ML tools. We focus on the use of ML in situ sampling and satellite observations, and the extent to which ML applications can advance theoretical oceanographic exploration, as well as aid numerical simulations. Applications that are also covered include model error and bias correction and current and potential use within data assimilation. While not without risk, there is great interest in the potential benefits of oceanographic ML applications; this review caters to this interest within the research community.

Algorithms produce a growing portion of decisions and recommendations both in policy and business. Such algorithmic decisions are natural experiments (conditionally quasi-randomly assigned instruments) since the algorithms make decisions based only on observable input variables. We use this observation to develop a treatment-effect estimator for a class of stochastic and deterministic algorithms. Our estimator is shown to be consistent and asymptotically normal for well-defined causal effects. A key special case of our estimator is a high-dimensional regression discontinuity design. The proofs use tools from differential geometry and geometric measure theory, which may be of independent interest. The practical performance of our method is first demonstrated in a high-dimensional simulation resembling decision-making by machine learning algorithms. Our estimator has smaller mean squared errors compared to alternative estimators. We finally apply our estimator to evaluate the effect of Coronavirus Aid, Relief, and Economic Security (CARES) Act, where more than \$10 billion worth of relief funding is allocated to hospitals via an algorithmic rule. The estimates suggest that the relief funding has little effects on COVID-19-related hospital activity levels. Naive OLS and IV estimates exhibit substantial selection bias.