Lifeboat Foundation

Incidentally, a few days ago I received a message from my paleobiologist colleague Dr. Ken Towe, a retired senior scientist at the Smithsonian Institution.

In a major breakthrough, scientists have built a tool to predict the odor profile of a molecule, just based on its structure. It can identify molecules that look different but smell the same, as well as molecules that look very similar but smell totally different. The research was published in Science.

Professor Jane Parker, University of Reading, said, “Vision research has wavelength, hearing research has frequency—both can be measured and assessed by instruments. But what about smell? We don’t currently have a way to measure or accurately predict the odor of a molecule, based on its molecular structure.”

“You can get so far with current knowledge of the molecular structure, but eventually you are faced with numerous exceptions where the odor and structure don’t match. This is what has stumped previous models of olfaction. The fantastic thing about this new ML generated model is that it correctly predicts the odor of those exceptions.”

Following more than seven years of research, researchers at the University of Seville-IBiS (Institute of Biomedicine of Seville) have identified a new key cell type with a critical role in the developmental processes of memory and learning. This breakthrough has been published in the prestigious journal Nature Neuroscience.

The research, led jointly by the University of Seville-IBiS and Karolinska Institutet, helps to understand how neural systems with decisive functions for human behavior mature. The in-depth study highlights the role of microglia, a group of cells that has been the subject of substantial information in recent years due to its involvement in various brain pathologies such as Alzheimer’s disease.

Summary: Researchers discovered that electrical noise stimulation to the frontal part of the brain can improve mathematical learning.

The study focused on those who initially showed low levels of brain excitation towards math. Unlike in placebo groups, unlike in placebo groups, a significant improvement in math skills was observed after the application of neurostimulation. This novel approach could revolutionize personalized learning.

In a recently published article featured on the cover of the Biophysical Journal, Dr. Rafael Bernardi, assistant professor of biophysics at the Department of Physics at Auburn University, and Dr. Marcelo Melo, a postdoctoral researcher in Dr. Bernardi’s group, shed light on the transformative capabilities of the next generation of supercomputers in reshaping the landscape of biophysics.

The researchers at Auburn delve into the harmonious fusion of computational modeling and experimental biophysics, providing a perspective for a future in which discoveries are made with unparalleled precision. Rather than being mere observers, today’s biophysicists, with the aid of advanced high-performance computing (HPC), are now trailblazers who can challenge longstanding biological assumptions, illuminate intricate details, and even create new proteins or design novel molecular circuits.

One of the most important aspects discussed in their perspective article is the new ability of computational biophysicists to simulate complex biological processes that range from the subatomic to whole-cell models, in extraordinary detail.

Physicists have developed a technique to precisely align supermoiré lattices, revolutionizing the potential for next-generation moiré quantum matter.

National University of Singapore (NUS) physicists have developed a technique to precisely control the alignment of supermoiré lattices by using a set of golden rules, paving the way for the advancement of next-generation moiré quantum matter.

Supermoiré Lattices

We developed a technique to fabricate atomically precise quantum antidots with unprecedented robustness and tunable quantum hole states through self-assembled single vacancies in a two-dimensional transition metal dichalcogenide.

But most deep learning models are loosely based on the brain’s inner workings. AI agents are increasingly endowed with human-like decision-making algorithms. The idea that machine intelligence could become sentient one day no longer seems like science fiction.

How could we tell if machine brains one day gained sentience? The answer may be based on our own brains.

A preprint paper authored by 19 neuroscientists, philosophers, and computer scientists, including Dr. Robert Long from the Center for AI Safety and Dr. Yoshua Bengio from the University of Montreal, argues that the neurobiology of consciousness may be our best bet. Rather than simply studying an AI agent’s behavior or responses—for example, during a chat—matching its responses to theories of human consciousness could provide a more objective ruler.

Is cosmology in crisis?