The researchers, from the University of Cambridge, studied industry data from the UK and 24 other European countries between 1995 and 2017, and found that at low levels of adoption, robots have a negative effect on profit margins. But at higher levels of adoption, robots can help increase profits. According to the researchers, this U-shaped phenomenon is due to the relationship between reducing costs, developing new processes and innovating new products. While many companies first adopt robotic technologies to decrease costs, this'process innovation' can be easily copied by competitors, so at low levels of robot adoption, companies are focused on their competitors rather than on developing new products. However, as levels of adoption increase and robots are fully integrated into a company's processes, the technologies can be used to increase revenue by innovating new products.

Processing information locked within clinical health records is a challenging task that remains an active area of research in biomedical NLP. In this work, we evaluate a broad set of machine learning techniques ranging from simple RNNs to specialised transformers such as BioBERT on a dataset containing clinical notes along with a set of annotations indicating whether a sample is cancer-related or not. Furthermore, we specifically employ efficient fine-tuning methods from NLP, namely, bottleneck adapters and prompt tuning, to adapt the models to our specialised task. Our evaluations suggest that fine-tuning a frozen BERT model pre-trained on natural language and with bottleneck adapters outperforms all other strategies, including full fine-tuning of the specialised BioBERT model. Based on our findings, we suggest that using bottleneck adapters in low-resource situations with limited access to labelled data or processing capacity could be a viable strategy in biomedical text mining. The code used in the experiments are going to be made available at https://github.com/omidrohanian/bottleneck-adapters.

Climate change, clean energy and sustainability, building low emissions technologies and developing ethical artificial intelligence are some of the challenges being tackled by CSIRO, Australia's national science agency, and the United States National Science Foundation (NSF) under a multi-million-dollar partnership. The recently established partnership between the two leading science organisations is aiming to accelerate joint research and initiatives in areas of mutual priority between Australia and the United States. CSIRO Chief Executive Larry Marshall said the two leading science organisations have already enabled a number of opportunities across the two countries in only a year, launching this month an AUD$100 million Global Centers initiative, partnering in the areas of responsible and ethical Artificial Intelligence (AI) and developing sustainable materials for global challenges. "As national science agencies, CSIRO and the NSF are working together to build international bridges for national benefit, strengthening our science and innovation to improve lives around the world," Dr Marshall said. "As the world races towards new applications for technologies like AI, it will take global collaboration to champion responsible and ethical applications that embrace the full potential of technological advances and drive healthy competitive advantages.

Glioblastoma is an aggressive and hard-to-treat type of brain cancer. But because it affects fewer than 10 in 100,000 people each year, it's considered to be a rare disease. Defining the boundaries of glioblastoma tumors is important for treatment. One key region represents the breakdown of the blood-brain barrier inside the tumor. Another, called the tumor core, could be relevant for surgical removal.

The heat capacity of a material is a fundamental property of great practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications, the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine learning approach, trained on density functional theory simulations, to accurately predict the heat capacity of these materials, that is, zeolites, metal–organic frameworks and covalent–organic frameworks. The accuracy of our prediction is confirmed with experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials, the heat requirement is reduced by as much as a factor of two using the correct heat capacity. Heat capacity of nanoporous materials is important for processes such as carbon capture, as this can affect process design energy requirements. Here, a machine learning approach for heat capacity prediction, trained on density functional theory simulations, is presented and experimentally verified.

Artificial intelligence (AI) systems employ learning algorithms that adapt to their users and environment, with learning either pre-trained or allowed to adapt during deployment. Because AI can optimize its behavior, a unit's factory model behavior can diverge after release, often at the perceived expense of safety, reliability, and human controllability. Since the Industrial Revolution, trust has ultimately resided in regulatory systems set up by governments and standards bodies. Research into human interactions with autonomous machines demonstrates a shift in the locus of trust: we must trust non-deterministic systems such as AI to self-regulate, albeit within boundaries. This radical shift is one of the biggest issues facing the deployment of AI in the European region.

DALLAS – Nov. 16, 2021 – UT Southwestern and University of Washington researchers led an international team that used artificial intelligence (AI) and evolutionary analysis to produce 3D models of eukaryotic protein interactions. The study, published in Science, identified more than 100 probable protein complexes for the first time and provided structural models for more than 700 previously uncharacterized ones. Insights into the ways pairs or groups of proteins fit together to carry out cellular processes could lead to a wealth of new drug targets. "Our results represent a significant advance in the new era in structural biology in which computation plays a fundamental role," said Qian Cong, Ph.D., Assistant Professor in the Eugene McDermott Center for Human Growth and Development with a secondary appointment in Biophysics. Dr. Cong led the study with David Baker, Ph.D., Professor of Biochemistry and Dr. Cong's postdoctoral mentor at the University of Washington prior to her recruitment to UT Southwestern.

An international team led by researchers at UT Southwestern and the University of Washington predicted the structures using artificial intelligence techniques. UT Southwestern and University of Washington researchers led an international team that used artificial intelligence (AI) and evolutionary analysis to produce 3D models of eukaryotic protein interactions. The study, published in Science, identified more than 100 probable protein complexes for the first time and provided structural models for more than 700 previously uncharacterized ones. Insights into the ways pairs or groups of proteins fit together to carry out cellular processes could lead to a wealth of new drug targets. "Our results represent a significant advance in the new era in structural biology in which computation plays a fundamental role," said Qian Cong, Ph.D., Assistant Professor in the Eugene McDermott Center for Human Growth and Development with a secondary appointment in Biophysics.

Unprecedented access to thousands of artefacts gathered by the 18th century physician, naturalist and prolific collector Sir Hans Sloane, will be available online to everyone for free, thanks to a multi-million pound digital project led by UCL. Following his death, Sir Hans Sloane's collections formed the basis of the British Museum, Natural History Museum and British Library. The Sloan Lab: Looking back to build future shared collections will bring Sloane's immense collections, ranging from coins to manuscripts and stuffed animals, which are currently held in a variety of locations, together online for the first time. Researchers will work with experts and communities to link up Sloane's collections, put them in context for the 21st century, and give new opportunities to search, explore and engage critically with the UK's cultural heritage online. The Sloane Lab is one of five'Discovery Projects' sharing £14.5m of Arts and Humanities Research Council (AHRC) funding to democratise and decolonise the UK's culture and heritage collections.

NEW YORK – Age Labs, a Norwegian diagnostics company that specializes in developing tests related to aging, has decided to use its expertise in data analysis and machine learning to craft a new test for SARS-CoV-2, the virus that causes COVID-19. The Oslo-based company plans to develop a test that can identify high-risk individuals based on an epigenetic signature. With the ability to triage patients at risk of developing severe disease, the company believes its test could be used to guide treatment decisions, not only in its home market of Norway, which according to the World Health Organization has reported roughly 9,000 COVID-19-related cases and 250 deaths, but in adjacent Sweden, which has reported nearly 80,000 cases and 6,000 deaths since the virus was first identified late last year, as well as in other hard-hit markets, such as the US. "As we see it, this is not over at all," said CEO Espen Riskedal. "It's hard to predict who will have a serious outcome, so any sort of diagnostic that can help clinicians early on in making treatment decisions is needed," he said.