It’s lunchtime in El Segundo, a small coastal town in Los Angeles County, around 130km west of where the McDonald brothers opened their first burger stand in 1948. Burgers are on the menu today. They come three to a tray, glistening in their brioche buns, piled high with lettuce, tomatoes, cheese and a creamsicle-orange sauce that tastes like mayonnaise-mellowed ketchup. Alongside them are other greatest hits from American fast-food menus: sausages nestled into long hot-dog buns with sautéed bell peppers and onions; sausage patties on flat English muffins; deep-fried chunks of white meat that look and taste like chicken nuggets.
Testy ties with U.S. and Australia could be prodding China to boost food reserves.
DALIAN, China/TOKYO — Less than 20% of the world’s population has managed to stockpile more than half of the globe’s maize and other grains, leading to steep price increases across the planet and dropping more countries into famine.
Last month, our Azure Cognitive Services team, comprising researchers and engineers with expertise in AI, achieved a groundbreaking milestone by advancing commonsense language understanding. When given a question that requires drawing on prior knowledge and five answer choices, our latest model— KEAR, Knowledgeable External Attention for commonsense Reasoning —performs better than people answering the same question, calculated as the majority vote among five individuals. KEAR reaches an accuracy of 89.4 percent on the CommonsenseQA leaderboard compared with 88.9 percent human accuracy. While the CommonsenseQA benchmark is in English, we follow a similar technique for multilingual commonsense reasoning and topped the X-CSR leaderboard.
Although recent large deep learning models trained with big data have made significant breakthroughs in natural language understanding, they still struggle with commonsense knowledge about the world, information that we, as people, have gathered in our day-to-day lives over time. Commonsense knowledge is often absent from task input but is crucial for language understanding. For example, take the question “What is a treat that your dog will enjoy?” To select an answer from the choices salad, petted, affection, bone, and lots of attention, we need to know that dogs generally enjoy food such as bones for a treat. Thus, the best answer would be “bone.” Without this external knowledge, even large-scale models may generate incorrect answers. For example, the DeBERTa language model selects “lots of attention,” which is not as good an answer as “bone.”
Biotechnology is a curious marriage of two seemingly disparate worlds. On one end, we have living organisms—wild, unpredictable celestial creations that can probably never be understood or appreciated enough, while on the other is technology—a cold, artificial entity that exists to bring convenience, structure and mathematical certainty in human lives. The contrast works well in combination, though, with biotechnology being an indispensable part of both healthcare and medicine. In addition to those two, there are several other applications in which biotechnology plays a central role—deep-sea exploration, protein synthesis, food quality regulation and preventing environmental degradation. The increasing involvement of AI in biotechnology is one of the main reasons for its growing scope of applications.
So, how exactly does AI impact biotechnology? For starters, AI fits in neatly with the dichotomous nature of biotechnology. After all, the technology contains a duality of its own—machine-like efficiency combined with the quaintly animalistic unpredictability in the way it works. In general terms, businesses and experts involved in biotechnology use AI to improve the quality of research and for improving compliance with regulatory standards.
More specifically, AI improves data capturing, analysis and pattern recognition in the following biotechnology-based applications:
In this video Dr. Lustgarten goes into more detail on what he tracks and how he does the analysis of the results. He emphasizes the importance of running your own tests, to not only look at one biomarker but to then combine that marker with other biomarkers, looking for what is optimal for you.
Dr. Michael Lustgarten is a scientist at the Tufts University Human Nutrition Research Center on Aging in Boston, Massachusetts. His research currently focuses on the role of the gut microbiome and serum metabolome on muscle mass and function in older adults. In this series of interviews Dr Lustgarten shares his experience with his rigorous n of 1 experiment over the last 7 years and shows how we or anyone can conduct a similar trial by tracking food, exercise and sleep, measure results and derive relationships between them, with a goal of extending our healthspan.
Dr Lustgarten’s channel on YouTube: https://www.youtube.com/channel/UCT1UMLpZ_CrQ_8I431K0b-g.
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