Isaac Newton's groundbreaking scientific productivity while isolated from the spread of bubonic plague is legendary. University of California San Diego physicists can now claim a stake in the annals of pandemic-driven science. A team of UC San Diego researchers and colleagues at Purdue University have now simulated the foundation of new types of artificial intelligence computing devices that mimic brain functions, an achievement that resulted from the COVID-19 pandemic lockdown. By combining new supercomputing materials with specialized oxides, the researchers successfully demonstrated the backbone of networks of circuits and devices that mirror the connectivity of neurons and synapses in biologically based neural networks. Like biologically based systems (left), complex emergent behaviors--which arise when separate components are merged together in a coordinated system--also result from neuromorphic networks made up of quantum-materials-based devices (right).
Last week, the Brookings Institution published an examination of the "extent, location, and concentration" of AI activity in 400 US metro areas, hailing it as the "next great'general purpose technology,'" with the power to spur economic growth. Key takeaways: Although it already feels like AI is everywhere, the tech is still in its early days--and in the US, AI development and commercialization is mega-concentrated in a handful of mostly coastal locales. But, but, but: Brookings also identified 13 other metro areas with "above-average involvement" in AI, including hubs you may have seen coming--New York, Boston, Seattle, Los Angeles, Washington, D.C., San Diego, Austin, Texas, and Raleigh, North Carolina--as well as smaller metro areas like Boulder, Colorado, Lincoln, Nebraska, Santa Cruz, California, Santa Maria-Santa Barbara, California, and Santa Fe, New Mexico. Zoom out: The above 15 metro areas account for two-thirds of AI activity nationwide--and for that matter, more than 50% of the areas Brookings looked at make up just 5% of AI activity, Wired reported.
A team at Brown University has developed a system that uses dozens of silicon microchips to record and transmit brain activity to a computer. Dubbed "neurograins," the chips--each about the size of a grain of salt--are designed to be sprinkled across the brain's surface or throughout its tissue to collect neural signals from more areas than currently possible with other brain implants. "Each grain has enough micro-electronics stuffed into it so that, when embedded in neural tissue, it can listen to neuronal activity on the one hand, and then can also transmit it as a tiny little radio to the outside world," says lead author Arto Nurmikko, a neuroengineer at Brown who led the development of the neurograins. The system, known as a brain-computer interface, is described in a paper published August 12 in Nature Electronics. Alongside other Brown researchers, as well as collaborators from Baylor University, the University of California at San Diego, and Qualcomm, Nurmikko began working on the neurograins four years ago with initial funding from the Defense Advanced Research Projects Agency.
Researchers have created a tool that allows glycomics datasets to be analysed using artificial intelligence for early cancer diagnoses. A team at the University of California (UC) San Diego, US, have developed a tool called GlyCompare that enables researchers to analyse glycomics datasets using artificial intelligence (AI), potentially leading to early cancer diagnoses. GlyCompare takes a systems-level perspective that accounts for shared biosynthetic pathways of glycans within and across samples. According to the team, one of the keys to the GlyCompare approach is that it looks at the biological steps needed to synthesise the subunits that make up glycans, rather than only looking at only the whole glycans themselves, thereby improving the accuracy of statistical analyses of glycomics data. To introduce their technology, the team demonstrated their ability to enhance comparisons of glycomics datasets by focusing on the hidden relationships between glycans in several contexts, including gastric cancer tissues.
Many have noted that the big contenders in the last two American presidential elections were well into their 70s, raising questions of the mental capacity, going forward, of these potential leaders. "Starting after middle age, say around 60 or so, memory and other abilities decline," says Dilip Jeste, professor of psychiatry and neuroscience at UC San Diego and director of the UCSD Center for Healthy Aging. But what actually declines--and what abilities might improve, as well as when, how, and at what speed--is a complex issue. It turns out, according to a new study in Nature Human Behavior, that many things improve with age, including some cognitive aspects that had previously been thought to get worse. John Verssimo, of the University of Lisbon, and his colleagues, looked at a large sample of people between the ages of 58 and 98 and measured their performance on a broad range of cognitive tasks to get a more detailed picture of cognitive aging.
It's often easier to understand the use cases for graph databases than understanding how graph databases work. For instance, asking the question of who the most powerful thought leaders across multiple social networks, with the greatest variety of connections, are better suited for graph databases because the alternative of running the query in a relational database would require a ridiculous number of table joins. And so, with TigerGraph ramping up product R&D out of a new San Diego base, and appointing a new head to the operation, Dr. Jay Yu, that provided the excuse to look at the trajectory for the company, not to mention our wish list for graph databases, which is all about simplification. Graph databases are all around us, but more often than not are hiding in plan sight. A good example is the Microsoft Graph, which Microsoft characterizes as "the gateway to data and intelligence in Microsoft 365."
Aicadium, a global AI Centre of Excellence, was launched today to empower companies to achieve better business outcomes through the adoption and delivery of AI technologies and solutions. Founded by Temasek, a global investment firm headquartered in Singapore, Aicadium will leverage a common machine learning platform to deliver AI-as-a-Service from discovery to deployment. Based in Singapore and San Diego, CA, Aicadium's management, data scientists, software and solutions engineers are being assembled with the guidance of Michael Zeller, Head of AI Strategy & Solutions at Temasek. Aicadium aims to fulfill a need shared by Temasek's global network of portfolio companies and others seeking to improve their business outcomes using artificial intelligence. However, these companies are typically faced with significant barriers to achieving operational AI within their organisations.
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Writing in the July 12, 2021 online issue of Nature Communications, researchers at University of California San Diego School of Medicine describe a new approach that uses machine learning to hunt for disease targets and then predicts whether a drug is likely to receive FDA approval. The study findings could measurably change how researchers sift through big data to find meaningful information with significant benefit to patients, the pharmaceutical industry and the nation's health care systems. "Academic labs and pharmaceutical and biotech companies have access to unlimited amounts of'big data' and better tools than ever to analyze such data. However, despite these incredible advances in technology, the success rates in drug discovery are lower today than in the 1970s," said Pradipta Ghosh, MD, senior author of the study and professor in the departments of Medicine and Cellular and Molecular Medicine at UC San Diego School of Medicine. "This is mostly because drugs that work perfectly in preclinical inbred models, such as laboratory mice, that are genetically or otherwise identical to each other, don't translate to patients in the clinic, where each individual and their disease is unique. It is this variability in the clinic that is believed to be the Achilles heel for any drug discovery program."
A team of scientists has developed a means to create a new type of memory, marking a notable breakthrough in the increasingly sophisticated field of artificial intelligence. "Quantum materials hold great promise for improving the capacities of today's computers," explains Andrew Kent, a New York University physicist and one of the senior investigators. "The work draws upon their properties in establishing a new structure for computation." The creation, designed in partnership with researchers from the University of California, San Diego (UCSD) and the University of Paris-Saclay, is reported in the Nature journal Scientific Reports. "Since conventional computing has reached its limits, new computational methods and devices are being developed," adds Ivan Schuller, a UCSD physicist and one of the paper's authors.