H/T Stephen Wolfram.
Particularly given its recent surprise successes, there’s a somewhat widespread belief that eventually AI will be able to “do everything”, or at least everything we currently do.
Stephen Wolfram explores the potential—and limitations—of AI in science. See cases in which AI will be a useful tool, and in others a less ideal tool.
This year, Rockefeller scientists plumbed the depths of wound repair and tackled how songbirds solve problems; they used microchips to grow mini-lungs and proposed an environmental trigger for multiple sclerosis. Efforts to combat COVID, Hepatitis B, and other infections bore fruit, and countless papers shed light on basic research, answering questions that have long baffled biologists. Here are some of the intriguing discoveries that came out of Rockefeller in 2023.
As the male reproductive system ages, it becomes more and more susceptible to mutations. New research from the laboratory of Li Zhao explored this phenomenon in fruit flies, by focusing on how mutations arise during the formation of sperm. The team found that, while mutations are common in the testes of both young and old flies, the repair mechanisms that remove those mutations and maintain genomic integrity during spermatogenesis become less efficient in older individuals, leading to the accumulation and persistence of more mutations in older flies.
Argonne’s Science 101 series takes you back to the basics, with plain-language explanations of the scientific concepts behind our pivotal discoveries and our biggest innovations.
In this Science 101 video, postdoctoral researchers Gillian Beltz-Mohrmann and Florian Kéruzoré explore two of the biggest mysteries in science: dark matter and dark energy. These strange influences seem to be stretching the universe apart and clumping stuff together in unexpected ways. Together, they make up a whopping 95% of the universe, but because we can’t see or touch them, we don’t know what they are.
Continue reading “Science 101: What are Dark Matter and Dark Energy?” »
Mónica Bello, Curator and Head of Arts at CERN talks about the programmes that have been fostering the dialogue between artists and physicists for over a decade with the aim of exploring the cultural significance of fundamental research.
In paid collaboration with reMarkable, I am happy to recommend this paper tablet which has made my workflow much more efficient and enjoyable.
Science and Technology: I don’t want to die.
A friend of the academic, who died in the US in 2021, says his brain has been cryonically preserved in accordance with his final wishes.
For years, niobium was considered an underperformer when it came to superconducting qubits. Now, scientists supported by Q-NEXT have found a way to engineer a high-performing niobium-based qubit and take advantage of niobium’s superior qualities.
When it comes to quantum technology, niobium is making a comeback.
For the past 15 years, niobium has been sitting on the bench after experiencing a few mediocre at-bats as a core qubit material.
In this first article in a series on philosophy and science, we take a look at materialism and why it is fundamental to science.
A short disclaimer before we read further: I’m a materialist. Materialism is a branch of philosophy to which the sciences, particularly the physical and life sciences, owe a lot. Materialism posits that the material world — matter — exists, and everything in the Universe, including consciousness, is made from or is a product of matter. An objective reality exists and we can understand it. Without materialism, physics, chemistry, and biology as we know it wouldn’t exist.
Another branch of philosophy, idealism, is in direct contradiction to materialism. Idealism states that, instead of matter, the mind and consciousness are fundamental to reality; that they are immaterial and therefore independent of the material world.
Speaker’s Bio: Catherine (Katie) Schuman is a research scientist at Oak Ridge National Laboratory (ORNL). She received her Ph.D. in Computer Science from the University of Tennessee (UT) in 2015, where she completed her dissertation on the use of evolutionary algorithms to train spiking neural networks for neuromorphic systems. She is continuing her study of algorithms for neuromorphic computing at ORNL. Katie has an adjunct faculty appointment with the Department of Electrical Engineering and Computer Science at UT, where she co-leads the TENNLab neuromorphic computing research group. Katie received the U.S. Department of Energy Early Career Award in 2019.
Talk Abstract: Neuromorphic computing is a popular technology for the future of computing. Much of the focus in neuromorphic computing research and development has focused on new architectures, devices, and materials, rather than in the software, algorithms, and applications of these systems. In this talk, I will overview the field of neuromorphic from the computer science perspective. I will give an introduction to spiking neural networks, as well as some of the most common algorithms used in the field. Finally, I will discuss the potential for using neuromorphic systems in real-world applications from scientific data analysis to autonomous vehicles.
Researchers await the outcome of an ongoing investigation into dozens of instances of alleged image problems spanning 29 publications over a period of 23 years.
