Texas A&M's Robin Murphy has deployed robots at 29 disasters, including three building collapses, two mine disasters and an earthquake as director of the Center for Robot-Assisted Search and Rescue. She has also served as a technical search specialist with the Hillsboro County (Florida) Fire and Rescue Department. The Conversation talked to Murphy to provide readers an understanding of the types of technologies that search and rescue crews at the Champlain Towers South disaster site in Surfside, Florida, have at their disposal, as well as some they don't. The interview has been edited for length. We don't have reports about it from Miami-Dade Fire Rescue Department, but news coverage shows that they're using drones.

Zachary Ulissi (right) explores how surface chirality affects chemical reactions.Credit: Materials Science and Engineering Department/Carnegie Mellon University Materials scientists are increasingly turning to machine learning and other computational techniques to discover new materials. From corrosion resistant aeroplane components and better batteries to new drugs or novel catalysts, big data can help to find them. "The problem is that the number of possible materials is infinite," says Matthias Scheffler, a computational materials scientist at the Fritz-Haber Institute in Berlin, Germany. "With high-throughput screening, you can screen thousands of systems, and a thousand is nothing compared to infinite." Along with physicist Claudia Draxl, of Humboldt University Berlin, Scheffler launched the Novel Materials Discovery Laboratory (NOMAD) at Fritz-Haber, a data repository for a wide variety of information about chemical compounds.

Rescuers deployed sonar and camera equipment early on as officials scoured the rubble for survivors. Heavy machinery was brought in to remove some bits of the pancaked building materials. Yet, nearly 150 people remain unaccounted for. And officials still have a tedious mission ahead as teams try to avoid falling debris and other unforeseen obstacles.

Feeding the world when there are limits to land, resources and skilled labor, exerts pressures on farmers to increase crop yields. In the past, farmers did this by rotating crops, fertilizing fields, using pesticides and installing irrigation--but with weather conditions changing almost unpredictably, farmers need to take as much guesswork out of planning and harvesting crops as possible. "Farmers face a major challenge in having to feed a world population that will increase to 2 billion people over the next 30 years according to the United Nations and the USDA," said Zach Bonefas, senior staff engineer and automation technology leader, at John Deere. "They also must work through many variables inherent to farming, like changing weather conditions, variations in soil quality and the presence of pests, which can have a big impact on their ability to produce food. Creating predictability out of that variability is the key, and that's where technology comes in."

There is an escalating need for methods to identify latent patterns in text data from many domains. We introduce a new method to identify topics in a corpus and represent documents as topic sequences. Discourse Atom Topic Modeling draws on advances in theoretical machine learning to integrate topic modeling and word embedding, capitalizing on the distinct capabilities of each. We first identify a set of vectors ("discourse atoms") that provide a sparse representation of an embedding space. Atom vectors can be interpreted as latent topics: Through a generative model, atoms map onto distributions over words; one can also infer the topic that generated a sequence of words. We illustrate our method with a prominent example of underutilized text: the U.S. National Violent Death Reporting System (NVDRS). The NVDRS summarizes violent death incidents with structured variables and unstructured narratives. We identify 225 latent topics in the narratives (e.g., preparation for death and physical aggression); many of these topics are not captured by existing structured variables. Motivated by known patterns in suicide and homicide by gender, and recent research on gender biases in semantic space, we identify the gender bias of our topics (e.g., a topic about pain medication is feminine). We then compare the gender bias of topics to their prevalence in narratives of female versus male victims. Results provide a detailed quantitative picture of reporting about lethal violence and its gendered nature. Our method offers a flexible and broadly applicable approach to model topics in text data.

Machine learning techniques have contributed to progress in science and technology fields ranging from health care to high-energy physics. Now, machine learning is poised to help accelerate the development of stronger alloys, particularly stainless steels, for America's thermal power generation fleet. Stronger materials are key to producing energy efficiently, resulting in economic and decarbonization benefits. "The use of ultra-high-strength steels in power plants dates back to the 1950s and has benefited from gradual improvements in the materials over time," says Osman Mamun, a postdoctoral research associate at Pacific Northwest National Laboratory (PNNL). "If we can find ways to speed up improvements or create new materials, we could see enhanced efficiency in plants that also reduces the amount of carbon emitted into the atmosphere."

A coastal survey using drones in Dorset has laid bare the scale of the UK's litter problem. The drones flew over beaches in Bournemouth, Christchurch and Poole across seven days in the May half term this year. Eighteen sites along the seafront in the region were monitored between May 27 and June 2, covering an overall area of 475,000 square metres. The technology found more than 1.5 tonnes of rubbish left behind by visitors – a third of which were glass bottles when measured by volume. In all, more than 123,000 items were identified, up from 22,266 in a drone survey of the same areas during the March lockdown – marking an astonishing 454 per cent increase due to relaxing lockdown measures.

Just a few years ago, there were no legions of deep learning scientists developing intelligent products and services at major companies and startups. When the youngest among us (the authors) entered the field, machine learning did not command headlines in daily newspapers. Our parents had no idea what machine learning was, let alone why we might prefer it to a career in medicine or law. Machine learning was a forward-looking academic discipline with a narrow set of real-world applications. And those applications, e.g., speech recognition and computer vision, required so much domain knowledge that they were often regarded as separate areas entirely for which machine learning was one small component. Neural networks then, the antecedents of the deep learning models that we focus on in this book, were regarded as outmoded tools. In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, reinforcement learning, and statistical modeling. With these advances in hand, we can now build cars that drive themselves with more autonomy than ever before (and less autonomy than some companies might have you believe), smart reply systems that automatically draft the most mundane emails, helping people dig out from oppressively large inboxes, and software agents that dominate the worldʼs best humans at board games like Go, a feat once thought to be decades away. Already, these tools exert ever-wider impacts on industry and society, changing the way movies are made, diseases are diagnosed, and playing a growing role in basic sciences--from astrophysics to biology.

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Showcasing America's most promising young scientists and engineers, the Blavatnik Family Foundation and the New York Academy of Sciences today named 31 finalists for the world's largest unrestricted prize honoring early-career scientists and engineers. Three winners of the Blavatnik National Awards for Young Scientists – in life sciences, chemistry, and physical sciences and engineering – will be announced on July 20, each receiving $250,000 as a Blavatnik National Awards Laureate. The finalists, culled from 298 nominations by 157 United States research institutions across 38 states, have made trailblazing discoveries in wide-ranging fields, from the neuroscience of addiction to the development of gene-editing technologies, from designing next-generation battery storage to understanding the origins of photosynthesis, from making improvements in computer vision to pioneering new frontiers in polymer chemistry. Descriptions of the honorees' research are listed below. "Each day, young scientists tirelessly seek solutions to humanity's greatest challenges," said Len Blavatnik, founder and chairman of Access Industries, and head of the Blavatnik Family Foundation. "The Blavatnik Awards recognize this scientific brilliance and tenacity as we honor these 31 finalists. We congratulate them on their accomplishments and look forward to their continued, future discoveries and success." President and CEO of the New York Academy of Sciences Nicholas B. Dirks said: "Each year, it is a complete joy to see the very'best of the best' of American science represented by the Blavatnik National Awards Finalists."