Berkeley scientists to develop safer and better CRISPR tools and apply them to medicine and mosquito gene drives.
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By Marc Pollefeys, Director of Science, HoloLens
It is not an exaggeration to say that deep learning has taken the world of computer vision, and many other recognition tasks, by storm. Many of the most difficult recognition problems have seen gains over the past few years that are astonishing.
Although we have seen large improvements in the accuracy of recognition as a result of Deep Neural Networks (DNNs), deep learning approaches have two well-known challenges: they require large amounts of labelled data for training, and they require a type of compute that is not amenable to current general purpose processor/memory architectures. Some companies have responded with architectures designed to address the particular type of massively parallel compute required for DNNs, including our own use of FPGAs, for example, but to date these approaches have primarily enhanced existing cloud computing fabrics.
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Gene editing aims to make precise changes to the target DNA whilst avoiding altering other parts of the DNA. The objective of this is to remove undesirable genetic traits and introduce desirable changes in both plants and animals. For example, it could be used to make crops more drought resistant, prevent or cure inherited genetic disorders or even treat age-related diseases.
As some of you may recall, back in May a study was published which claimed that the groundbreaking gene editing technique CRISPR caused thousands of off target and potentially dangerous mutations[1]. The authors of the paper called for regulators to investigate the safety of the technique, a move that could potentially set back research years if not decades.
This publication has been widely blasted by the research community due to serious questions about the study design being raised. One of the problems with this original paper was that it involved only three mice, this is an extremely poor number to make the kind of conclusions the paper did. There have been calls for the paper to be withdrawn and critical responses to the study.
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When British billionaire Jim Mellon wants to map out an investment strategy, he likes to write a book first. Out of that process came his most recent work — Juvenescence: Investing in the Age of Longevity. Now he and some close associates with some of the best connections in biotech are using the book as inspiration to launch a new company — also named Juvenescence — with plans to make a big splash in anti-aging research.
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Hot on the heels of last month’s nuclear fusion breakthrough comes the first results from a multi-year partnership between Google and Tri Alpha Energy, the world’s largest private fusion company. The two organizations joined forces in 2014 in the hopes that Google’s machine learning algorithms could advance plasma research and bring us closer to the dream of fusion power.
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