Tag: Mirai
What employers are looking for in the age of AI — and four ways to provide it
Artificial intelligence is becoming a must-have skill for both employers and funders. Data compiled by the jobs site Indeed show that, in the United States, science jobs listing ‘AI’ as a required skill are rising sharply, whereas the overall number of science jobs has fallen (see ‘AI in demand’).One example is the Quadram Institute in Norwich, UK, which specializes in food science and gut biology. Despite that core focus, researchers must show some knowledge of machine learning and AI, according to chief executive Daniel Figeys.
“Definitely they should be familiar with it,” he says, adding that a lot of work in Quadram’s field involves machine learning and high-throughput screening, the data from which are often analysed with AI tools. “If they were lacking all those skills, depending on the position, that would be a red flag.”
However, instead of worrying that AI systems will be taking science jobs, researchers can get ahead by using and interpreting AI tools to build their understanding. And they don’t need to know everything. Computer scientist Regina Barzilay runs a specialist course at the Massachusetts Institute of Technology (MIT) in Cambridge to teach scientists AI skills. She likens it to cooking: “You don’t need to learn every recipe on Earth to feel comfortable in the kitchen … but you need to have this very basic understanding,” she says, much like “you don’t need to be a computer scientist to use a computer.”
Nature spoke to nine recruiters and researchers to ask them what the most important skills will be for scientists of all kinds in the AI era. Learn more
One Survivor’s AI Breakthrough Predicts Cancer Years Ahead
AI Decoded focusses on one of the most urgent, tangible uses of artificial intelligence: health care — we speak to Dr Regina Barzilay, an MIT professor who is building machine-learning AI models to predict disease. She herself was diagnosed with breast cancer in 2014, and has used that experience and knowledge to target her research towards prevention — the AI model she and her team built, named MIRAI, is now able to detect a patient’s risk of developing breast cancer within five years. Are we on the brink of a revolution in treating cancer for everyone? Find out on AI Decoded... Joining presenter Christian Fraser is AI Decoded co-host Stephanie Hare and the BBC's AI correspondent Marc Cieslak Learn more
Marketwatch25: She survived breast cancer. Now her AI tool could help you skip annual mammograms.
As an MIT computer-science professor, Regina Barzilay was used to living on the bleeding edge of innovation, teaching computers to understand words in the nascent field of natural language processing. But when she was diagnosed with breast cancer in 2014, she was thrust into a different and, as she describes it, “really backwards” technological world. Learn more
TIME100 AI 2025
Regina Barzilay is in the business of patient future-telling. That is, using machine learning AI models to predict disease—including when and how it will strike, along with how it may behave. Barzilay began pursuing this after being diagnosed with breast cancer in 2014. As a patient, she experienced the frustrating uncertainty surrounding individual prognoses. Her questions about treatments were often answered in reference to what happened to the participants of clinical trials, but she felt those answers gave her little information about her individual situation.As an AI researcher, she knew how to address that uncertainty. “To me it was quite clear,” she says, “That's what machine learning is about.” A decade later, the AI model she and her team built, named MIRAI, is able to detect a patient’s risk of developing breast cancer within five years. By 2025, MIRAI was validated by over 2 million mammograms in 48 hospitals across 22 countries.
And her future-telling continues. In 2024, Barzilay worked on an AI model that estimates the expected effectiveness of candidate flu vaccines by predicting which versions of the flu virus are likely to spread next season. She’s now working on using the same concept on cancer, in order to predict how patients—particularly with advanced cancers—will react to a specific treatment. “We are constantly running behind the disease,” she says. “The idea here is to be able to predict it.” Learn more
