It’s ten times more powerful than the current U.S. effort.

Earlier this month, Chinese artificial intelligence (A.I.) researchers at the Beijing Academy of Artificial Intelligence (BAAI) unveiled Wu Dao 2.0, the world’s biggest natural language processing (NLP) model. And it’s a big deal.

NLP is a branch of A.I. research that aims to give computers the ability to understand text and spoken words and respond to them in much the same way human beings can.

Last year, the San Francisco–based nonprofit A.I. research laboratory OpenAI wowed the world when it released its GPT-3 (Generative Pre-trained Transformer 3) language model. GPT-3 is a 175 billion–parameter deep learning model trained on text datasets with hundreds of billions of words. A parameter is a calculation in a neural network that shapes the model’s data by assigning to each chunk a greater or lesser weighting, thus providing the neural network a learned perspective on the data.

The gene they chose to inactivate is PCSK9, which has been a hot topic in drug discovery for some years now. It’s a target validated by several converging lines of evidence from the human population (see the “History” section of that first link). People with overactive PCSK9 have high LDL lipoproteins and cholesterol, and people with mutations that make it inactive have extremely low LDL and seem to be protected from a lot of cardiovascular disease. There are several drugs and drug candidates out there targeting the protein, as well there might be.

It’s a good proof-of-concept, then, because we know exactly what the effects of turning down the expression of active PCSK9 should look like. It’s also got the major advantage of being mostly a liver target – as I’ve mentioned several times on the blog already, many therapies aimed at gene editing or RNA manipulation have a pharmacokinetic complication. The formulations used to get such agents intact into the body (and in a form that they can penetrate cells) tend to get combed out pretty thoroughly by the liver – which after all, is (among other things) in the business of policing the bloodstream for weird, unrecognized stuff that is then targeted for demolition by hepatocytes. Your entire bloodstream goes sluicing through the liver constantly; you’re not going to able to dodge it if your therapy is out there in the circulation. It happens to our small-molecule drugs all the time: hepatic “first pass” metabolism is almost always a factor to reckon with.

The harmfulness of pesticides to beneficial organisms is one of the most serious concerns in agriculture. Therefore scientists are eagerly looking for new, more environmentally friendly and species-specific solutions. Researchers at the Estonian University of Life Sciences, Ghent and the University of Maastricht took a long step forward in this regard.

The detrimental impact of pesticides on non-target organisms is one of the most urgent concerns in current agriculture. Double-stranded RNAs (dsRNAs) represent the most species-specific class of pesticides to date, potentially allowing control of a target pest without effecting other species. The unprecedented target-specificity of dsRNA is due to its nucleotide sequence-specific mode of action that results in post-transcriptional gene silencing, or RNA interference (RNAi), in the target species. The development and field use of dsRNAs, via both the insertion of transgenes into the plant genome and the application of dsRNA sprays, is a rapidly growing area of research. Simultaneously, there exists the growing prospect of harnessing RNAi within integrated pest management schemes.

Using the pollen beetle (Brassicogethes aeneus) and its host crop oilseed rape (Brassica napus) as a model crop-pest system, a team of researchers collectively from Estonian University of Life Sciences, Ghent University and Maastricht University examined how RNAi efficacy depends on duration of dietary exposure to dsRNA. To this end, the authors applied dsRNA (specifically designed to induce RNAi in the pollen beetle) to oilseed rape flowers, and analyzed RNAi-induced mortality between insects chronically fed dsRNA and insects fed dsRNA for 3 days. Most notably, their data suggest that, with chronic dietary exposure to dsRNA, reduced dsRNA concentrations can be applied in order to achieve a similar effect compared to short-term (e.g. 3 days) exposure to higher concentrations. This observation has important implications for optimizing dsRNA spray approaches to managing crop pests.

Join Aubrey de Grey and Vitalik Buterin on our fireside chat where they discuss and answer questions at the intersection of longevity and web3.

The AMA is hosted by VitaDAO — VitaDAO is the world’s first decentralized intellectual property collective.

Its mission is to extend the human lifespan by collectively financing and commercializing longevity therapeutics in an open and democratic manner.

VitaDAO and its members will own the intellectual property assets that result from the projects it supports. Members can contribute work or funds to join VitaDAO by purchasing or earning VITA tokens.

Physics World

System works in minutes and is claimed to be as sensitive as PCR tests.

Michael I. Jordan explains why today’s artificial-intelligence systems aren’t actually intelligent.

THE INSTITUTE Artificial-intelligence systems are nowhere near advanced enough to replace humans in many tasks involving reasoning, real-world knowledge, and social interaction. They are showing human-level competence in low-level pattern recognition skills, but at the cognitive level they are merely imitating human intelligence, not engaging deeply and creatively, says Michael I. Jordan, a leading researcher in AI and machine learning. Jordan is a professor in the department of electrical engineering and computer science, and the department of statistics, at the University of California, Berkeley.

He notes that the imitation of human thinking is not the sole goal of machine learning—the engineering field that underlies recent progress in AI—or even the best goal. Instead, machine learning can serve to augment human intelligence, via painstaking analysis of large data sets in much the way that a search engine augments human knowledge by organizing the Web. Machine learning also can provide new services to humans in domains such as health care, commerce, and transportation, by bringing together information found in multiple data sets, finding patterns, and proposing new courses of action.

“People are getting confused about the meaning of AI in discussions of technology trends—that there is some kind of intelligent thought in computers that is responsible for the progress and which is competing with humans,” he says. “We don’t have that, but people are talking as if we do.”

A Thousand Brains provides a new theory of intelligence, how it can lead to the creation of truly intelligent AI, and implications for the future of humanity. ‘’Brilliant…exhilarating’’ — from the foreword by Richard Dawkins.

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A team of physicists and chemists from the University of Bristol hope to recycle radioactive material directly from a former nuclear power plant in Gloucestershire to generate ultra-long-lasting power sources.

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We have the core tech to enable radically more ethical and beneficial social media networks. Here’s what we’ll need.