A team of researchers at Rice University and Baylor College of Medicine has developed a new strategy for identifying hazardous pollutants in soil, even ones that have never been isolated or studied in a lab. The new approach, described in a study published in Proceedings of the National Academy of Sciences, uses light-based imaging, theoretical predictions of compounds' light signatures and machine learning (ML) algorithms to detect toxic compounds like polycyclic aromatic hydrocarbons (PAHs) and their derivative compounds (PACs) in soil. A common by-product of combustion, PAHs and PACs have been linked to cancer, developmental issues and other serious health problems. Identifying pollutants in soil usually requires advanced laboratories and standard physical reference samples of the suspected contaminants. However, for many environmental pollutants that pose a public health risk, there is no experimental data available that can be used to detect them.

The UK's national institute for artificial intelligence and data science has launched a consultation process that could lead to redundancies among its 440 staff. In a memo sent to staff this month the Alan Turing Institute gave an update on its new strategy, under which it will concentrate on fewer projects. It was addressed to "affected employees" and stated that the government-backed institute "may need to consider making redundancies". According to informal calculations by staff, the memo could have been sent to as many as 140 people. The internal document said the institute – which carries out research with universities, the private sector and government entities – was working on 111 live projects and needed to "move away from engaging in large numbers of individual projects". Last year the institute unveiled a new strategy dubbed "Turing 2.0" in which it would focus on three key areas: health, the environment, and defence and security.

It is essential for healthcare and pharmaceutical companies to be aware of both critical and non-critical risks when conducting quality clinical trials. However, managing both takes time and money -- resources that clinical teams are often strapped for. Additionally, the risks that organisations define at the start of the trial may change, meaning the data they need to collect will also change. In order to address these challenges, researchers must break down silos and create a centralised process for monitoring and managing risk. Many organisations are turning to risk-based quality management (RBQM) practices to make that happen.

ChatGPT, the artificial-intelligence program captivating Silicon Valley with its sophisticated prose, had its origin three years ago, when technology investor Sam Altman became chief executive of the chatbot's developer, OpenAI. Mr. Altman decided at that time to move the OpenAI research lab away from its nonprofit roots and turn to a new strategy, as it raced to build software that could fully mirror the intelligence and capabilities of humans--what AI researchers call "artificial general intelligence." Mr. Altman, who had built a name as president of famed startup accelerator Y Combinator, would oversee the creation of a new for-profit arm, believing OpenAI needed to become an aggressive fundraiser to meet its founding mission.

The U.S Army is rolling out a strategy focused on software, data and artificial intelligence practices, a move officials believe will clarify for industry what the service needs to transform into a high-tech, digital-forward force and how, exactly, it plans to get there. The strategy, which will be unveiled during the Association of the U.S. Army's annual conference, is meant to help "pivot our programs to adopt modern software practices, adopt data-centricity, and get us to artificial intelligence, machine learning and [figuring] out where the right applications of that are so that we can really enable commanders in the field to make data-driven, fast decisions," Jennifer Swanson, the deputy assistant secretary of the Army for data engineering and software, told Defense News in an interview ahead of the event. Swanson's title alone hints at the transformation underway for the Army's acquisition branch. When she was hired earlier this year, she was chief systems engineer. The strategy arrives as the Army conducts a massive overhaul of its virtual footprint and computer infrastructure in order to better prepare for potential conflicts with China and Russia.

… adopt data-centricity, and get us to artificial intelligence, machine learning and [figuring] out where the right applications of that are so …

There's a new use for 5G networks: spotting wildfires before they get out of control, an increasing worry as climate change makes fires spread faster and burn longer than before. The startup Pano AI uses a series of cameras that survey the wilderness and AI algorithms that watch for telltale smoke -- an indicator of small blazes that could grow into raging wildfires. That footage is sent to the startup's headquarters for human confirmation, and if a fire is burning, evidence is sent to clients who could be affected. While Pano AI had been sending evidence photos over 4G LTE networks at slow rates of around 20 to 30 6-megapixel images per minute, its new partnership with T-Mobile has it using the carrier's 5G network to send video at 30 frames per second, which is around 90 times more data. Ultimately, getting evidence to Pano AI's clients, which include utility companies, much quicker on 5G means a faster response from firefighters and potentially squashing big fires before they get dangerous.

In competitive two-agent environments, deep reinforcement learning (RL) methods based on the \emph{Double Oracle (DO)} algorithm, such as \emph{Policy Space Response Oracles (PSRO)} and \emph{Anytime PSRO (APSRO)}, iteratively add RL best response policies to a population. Eventually, an optimal mixture of these population policies will approximate a Nash equilibrium. However, these methods might need to add all deterministic policies before converging. In this work, we introduce \emph{Self-Play PSRO (SP-PSRO)}, a method that adds an approximately optimal stochastic policy to the population in each iteration. Instead of adding only deterministic best responses to the opponent's least exploitable population mixture, SP-PSRO also learns an approximately optimal stochastic policy and adds it to the population as well. As a result, SP-PSRO empirically tends to converge much faster than APSRO and in many games converges in just a few iterations.

Israel has adopted a new strategy for the incorporation and use of artificial intelligence across the branches of its armed forces, according to a senior Israel Defense Forces official. The new strategy was unveiled amid the AI Week 2022 three-day event at The Blavatnik Interdisciplinary Cyber Research Center and Tel Aviv Center for AI and Data Science at Tel Aviv University. The event included a session on the IDF's new information and AI strategy. A senior Israel Defense Forces official, whose name could not be used due to the sensitivity of their position, noted that the IDF is undergoing a digital transformation in dealing with AI in all its branches and the commands. This will be the first time the IDF has a multi-branch and multi-command plan for use of AI.

Digital Secretary Oliver Dowden revealed the move as he set out his Ten Tech Priorities to power a golden age of tech in the UK this week. Unleashing the power of AI is a top priority in our plan to be the most pro-tech government ever. The UK is already a world leader in this revolutionary technology and the new AI Strategy will help us seize its full potential - from creating new jobs and improving productivity to tackling climate change and delivering better public services. The Government will build on the UK's strong foundations put in place through the AI Sector Deal to develop and deliver an AI Strategy that is both globally ambitious and socially inclusive. It will consider recommendations from the AI Council, an independent expert committee that advises the government, which published its AI Roadmap in January, alongside input from industry, academia and civil society.