Park's team tested 14 different chatbot models by asking them a series of political questions on topics such as immigration, climate change, the role of government and same-sex marriage. The research, released earlier this summer, showed that a series of models developed by Google called Bidirectional Encoder Representations from Transformers, or BERT, were more socially conservative, potentially because they were trained more on books compared to other models that leaned more on internet data and social media comments. Facebook's LLaMA model was slightly more authoritarian and right wing, while OpenAI's GPT-4, its most up-to-date technology, tended to be more economically and socially liberal.

More than three-quarters of businesses with active natural language processing (NLP) projects plan to increase spending on in the next 12 to 18 months, according to new data from expert.ai, a leading company in artificial intelligence (AI) for language understanding. The finding is one of many data points culled from a recent survey and detailed in expert.ai's The report shows a burgeoning appetite for NLP-driven efficiencies that reduce costs, drive growth and offer a competitive advantage. NLP enables businesses to automatically interpret unstructured data, bridging the language gap between humans and technology. As a result, there has been an increase in use cases across business operations, ranging from marketing to finance and customer care to sales.

Artificial intelligence (AI) machine learning is a rapidly emerging brain modeling tool for mental health research, psychiatry, neuroscience, genomics, pharmaceuticals, life sciences, and biotechnology. Scientists have identified areas of potential weak spots in AI brain models and offer solutions on how to prevent bias in a new peer-reviewed study. The research team led by Abigail Greene at Yale School of Medicine along with co-authors affiliated with Yale University, Brigham and Women's Hospital, Harvard Medical School, University of Washington, and Columbia University Irving Medical Center's Department of Psychiatry points out the need to identify why AI algorithms for brain models do not work for everyone when seeking to understand brain-phenotype relationships without biases. "Individual differences in brain functional organization track a range of traits, symptoms and behaviors," wrote the scientists. "So far, work modelling linear brain–phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants."

While French is one of the most popular GCSEs in the UK, many Brits are nervous when it comes to using their language skills later in life. But a new suggests there's nothing to fear - even if it has been decades since you last studied a foreign language. Researchers from the University of York have shown that people tested on foreign languages 50 years after they last sat any exam perform just as well as recent students. 'We often say if you don't use a language, you will lose it, but this doesn't seem to be the case,' said Professor Monika Schmid, Head of the University of York's Department of Language and Linguistics. During recent tests, experts from Abertay University in Dundee, found that speaking more than one language didn't have any cognitive benefit.

New research into artificial intelligence (AI) algorithms coming out of the University of York is enabling scientists to develop more complete models of the protein structures in the human body. This can have a big impact on the design of therapeutics and vaccines.  The research was published in the journal Nature Structural and Molecular Biology. Up […]

Artificial intelligence (AI) and deep learning models can help advance research on neural degeneration, showing its capabilities in identifying and categorizing its forms on a model organism. Using the organism Caenorhabditis elegans or the roundworm – a 1-millimeter near-transparent nematode – researchers used deep learning to conduct a quantitative image-based analysis of neural degeneration patterns observed in the PVD neuron of the organism. Researchers from North Carolina State University have detailed their work in the journal BMC Biology, September 23. The worms were found alive last week in a biological container that was among the debris from the Space Shuttle Columbia recovered in East Texas. The worms are descendants of those that were part of an experiment that flew on Columbia's last mission before the spacecraft broke up on reentry February 1, killing all seven astronauts.

Artificial intelligence (AI) and deep learning models can help advance research on neural degeneration, showing its capabilities in identifying and categorizing its forms on a model organism. Using the organism Caenorhabditis elegans or the roundworm - a 1-millimeter near-transparent nematode - researchers used deep learning to conduct a quantitative image-based analysis of neural degeneration patterns observed in the PVD neuron of the organism. Researchers from North Carolina State University have detailed their work in the journal BMC Biology, September 23. "Researchers want to study the mechanisms that drive neural degeneration, with the long-term goal of finding ways to slow or prevent the degeneration associated with age or disease," explained Adriana San Miguel in a NCSU news release. San Miguel serves as the corresponding author on the study, as well as a chemical and biomolecular assistant professor.

According to VentureBeat, AI researchers at Uber have recently posted a paper to Arxiv outlining a new platform intended to assist in the creation of distributed AI models. The platform is called Fiber, and it can be used to drive both reinforcement learning tasks and population-based learning. Fiber is designed to make large-scale parallel computation more accessible to non-experts, letting them take advantage of the power of distributed AI algorithms and models. Fiber has recently been made open-source on GitHub, and it's compatible with Python 3.6 or above, with Kubernetes running on a Linux system and running in a cloud environment. According to the team of researchers, the platform is capable of easily scaling up to hundreds or thousands of individual machines.

The "Report for the State of RPA and Smart Automation" interviewed more than 1,000 business executives in North America and found that while 75.3 percent believe automation will make them more competitive, significant disparities exist between industries – with public sector and surprisingly, technology companies lagging significantly when it comes to adoption. While more than half of businesses in North America have already implemented some type of automation solution, such as RPA and AI, the research uncovered notable differences between industries. For instance, nearly nine in 10 manufacturing organizations have already adopted some form of intelligent automation, compared to less than three in 10 public sector organizations. Despite these identified barriers to overcome, nine in 10 organizations that have not yet implemented RPA and AI-based automation solutions report having sufficient internal technical competencies to do so, showing that technical implementation is no longer a significant hurdle for most organizations. "Intelligent automation marks a quantum leap for humanity, and like the early stages of the internet, companies that do not adapt to this new reality risk becoming obsolete," said Daniel Newman, founder and principal analyst at Futurum Research.

Across age groups, U.S. employees believe that paralegals (4%), insurance underwriters (5%), and pharmacists (7%) have the best chance to survive automation; More part time employees (25%) fear that AI will take their jobs within 10 years compared to full-time workers (18%), although there is no significant difference in attitudes on the specific jobs they think are likely to disappear. Employees at the largest companies (with more than 20,000 staff) are slightly less afraid (17%) than the overall group (19%) about the effect of AI/bots on their jobs, possibly because they have already experienced its negative impact (10%), and see a more stable future. Across age groups, U.S. employees believe that paralegals (4%), insurance underwriters (5%), and pharmacists (7%) have the best chance to survive automation; More part time employees (25%) fear that AI will take their jobs within 10 years compared to full-time workers (18%), although there is no significant difference in attitudes on the specific jobs they think are likely to disappear. Employees at the largest companies (with more than 20,000 staff) are slightly less afraid (17%) than the overall group (19%) about the effect of AI/bots on their jobs, possibly because they have already experienced its negative impact (10%), and see a more stable future.