Lifeboat Foundation: Safeguarding Humanity
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Solar rays are a plentiful, clean source of energy that is becoming increasingly important as the world works to shift away from power sources that contribute to global warming. But current methods of harvesting solar charges are expensive and inefficient—with a theoretical efficiency limit of 33 percent. New nanomaterials developed by researchers at the Advanced Science Research Center (ASRC) at The Graduate Center of The City University of New York (CUNY) could provide a pathway to more efficient and potentially affordable harvesting of solar energy.

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Buried deep within the DNA of Asian individuals is a genetic clue pointing to the existence of an unknown human ancestor. Remarkably, it wasn’t a human who reached this startling conjecture, but rather an artificially intelligent algorithm. Welcome to archaeology in the 21st century.

New research published last week in Nature Communications suggests a yet-to-be discovered hominid interbred with modern humans tens of thousands of years ago. This mystery species eventually went extinct, but an AI developed by researchers from the Institute of Evolutionary Biology (IBE) and several other European institutions found traces of its existence in the DNA of present-day people with Asian ancestry. A press release issued by the Centre for Genomic Regulation said it’s the first time deep learning has been used to explain human history, “paving the way for this technology to be applied in other questions in biology, genomics and evolution.”

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Many of the new developments in artificial intelligence that we hear about nowadays are actually just applications of machine learning techniques that have been hammered out for years.

And as the research community’s attention shifts from deep learning, it remains unclear what will take its place, according to MIT Tech. In the past, older types of artificial intelligence that didn’t really take off when they were first developed later resurfaced and taken off with new life. For instance, scientists first developed machine learning decades ago, but it only became commonplace about a decade ago.

MIT Tech didn’t predict what will come next. It may be that some form of existing technology will finally hits its stride, but it’s also possible that an AI engineer will develop some brand-new type of AI that’ll shape the future.

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How might the process be used in the future?

Due to advancements in genetic engineering, the researchers say they&s;re able to reprogram bacteria and create mutations in cell surfaces with “vast diversity.”

“By combining genetic tools (for creating mutations) with our microfluidic screening (for selection), we have the vision to mutate cells and then pick out the best candidates for electron transfer.”

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