Tag Archives: AI

Science: Predicting Rain With AI, Map Of The Motor Cortex, 2021 Nobel Prizes

AI weather forecasters, mapping the human brain and the 2021 science Nobel prizes.

In this episode:

00:52 Improving the accuracy of weather forecasts with AI

Short-term rain predictions are a significant challenge for meteorologists. Now, a team of researchers have come up with an artificial-intelligence based system that weather forecasters preferred to other prediction methods.

Research article: Ravuri et al.

08:02 Research Highlights

The vaping robot that could help explain why some e-cigarettes damage lungs, and the sea-slugs that steal chloroplasts to boost egg production.

Research Highlight: This robot vapes for science

Research Highlight: Solar-powered slugs have a bright reproductive future

10:29 A map of the motor cortex

A group of researchers are undertaking an enormous task: to make a cellular atlas of the entire brain. This week, they publish a suite of papers that has accomplished this feat for one part of the brain — the motor cortex.

Research Article: BRAIN Initiative Cell Census Network

News and Views: A census of cell types in the brain’s motor cortex

Editorial: Neuroscientists make strides towards deciphering the human brain

17:58 Nobel News

Flora Graham from the Nature Briefing joins us to talk about the winners of this year’s science Nobels.

News: Medicine Nobel goes to scientists who discovered biology of senses

News: Climate modellers and theorist of complex systems share physics Nobel

News: ‘Elegant’ catalysts that tell left from right scoop chemistry Nobel

Health: Annual Physical Exams Are Going Virtual

Science: Endometriosis Insights, Deep Learning That Predicts RNA Folding

News Intern Rachel Fritts talks with host Sarah Crespi about a new way to think about endometriosis—a painful condition found in one in 10 women in which tissue that normally lines the uterus grows on the outside of the uterus and can bind to other organs.

Next, Raphael Townshend, founder and CEO of Atomic AI, talks about predicting RNA folding using deep learning—a machine learning approach that relies on very few examples and limited data.

Finally, in this month’s edition of our limited series on race and science, guest host and journalist Angela Saini is joined by author Lundy Braun, professor of pathology and laboratory medicine and Africana studies at Brown University, to discuss her book: Breathing Race into the Machine: The Surprising Career of the Spirometer from Plantation to Genetics.

Front Covers: Science Magazine – August 27

Medicine: ‘AI’ Can Predict Rheumatoid Arthritis

Medicine: The Future Of ‘MR Technology’ (Video)

We’re already integrating Adaptive Intelligence-powered applications into our MR systems, improving workflow and patient comfort, increasing diagnostic confidence, and increasing speed.

We’re already integrating Adaptive Intelligence-powered applications into our MR systems, improving workflow and patient comfort, increasing diagnostic confidence, and increasing speed.

Our Ingenia digital MR portfolio integrates Adaptive Intelligence-driven SmartExam analytics for automatic planning, scanning and processing of exams, helping improve the entire MR workflow, from image acquisition to reading preference.

Book Reviews: ‘The Self-Assembling Brain’ – The Future Benefits For AI

As Peter Robin Hiesinger argues, “the information problem” underlies both fields, motivating the questions driving forward the frontiers of research. How does genetic information unfold during the years-long process of human brain development―and is there a quicker path to creating human-level artificial intelligence? Is the biological brain just messy hardware, which scientists can improve upon by running learning algorithms on computers? Can AI bypass the evolutionary programming of “grown” networks? Through a series of fictional discussions between researchers across disciplines, complemented by in-depth seminars, Hiesinger explores these tightly linked questions, highlighting the challenges facing scientists, their different disciplinary perspectives and approaches, as well as the common ground shared by those interested in the development of biological brains and AI systems. In the end, Hiesinger contends that the information content of biological and artificial neural networks must unfold in an algorithmic process requiring time and energy. There is no genome and no blueprint that depicts the final product. The self-assembling brain knows no shortcuts.

Read book review here