An artificial-intelligence market crash could drive AI researchers back into academia, experts say. Plus, the ethics of brain implants that detect ‘preconscious’ thoughts and a gene-editing tool that could tackle multiple diseases.
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A cell’s protein-making machinery (artist’s illustration) can be instructed to ignore mutations that would otherwise result in shortened proteins. Credit: Christoph Burgstedt/Science Photo Library
A multipurpose gene-editing tool can correct several genetic conditions in mice by restoring proteins that have been cut short by disease-causing mutations. The method, called PERT, uses engineered RNA molecules that allow protein synthesis to continue even when a DNA mutation tells it to stop prematurely. These ‘nonsense mutations’ comprise nearly one-quarter of known disease-causing DNA variants. As such, if PERT proves effective in humans, it could overcome the need to design bespoke treatments for individual diseases.
Nature | 5 min read
Reference: Nature paper
Female authors made up just 23% of those in a sample of nearly 900 retracted medical studies published between 2008 and 2017, a new study has found. For first- and last-author slots, the percentage drops to 16.5% and 12.7%, respectively. Why this pronounced gender disparity emerges is unclear. One possible explanation is that the underrepresentation of women in senior academic roles in medical sciences means they’re less exposed to the risks that are more commonly associated with retractions, suggests study author and internal medicine specialist Paul Sebo.
Nature | 4 min read
Reference: PLOS One paper
The boom in artificial intelligence (AI) technology is starting to show signs of strain, which many financial analysts speculate could indicate an upcoming market crash. Some say that an AI-market collapse would be even more catastrophic than the dot-com crash in the early 2000s, which could suggest that mass lay-offs of AI researchers and developers are on the horizon. But there might be a silver lining, says economist Josh Turner. Scientists forced out of one sector can take innovation into another. Redundancies in the tech industry could also drive AI researchers back into academia, which has struggled to attract top AI talent.
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