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Exploring the most important questions we face as we age.


Dr. Debra Whitman, Ph.D. is Executive Vice President and Chief Public Policy Officer, at AARP (https://www.aarp.org/) where she leads policy development, analysis and research, as well as global thought leadership supporting and advancing the interests of individuals age 50-plus and their families. She oversees AARP’s Public Policy Institute, AARP Research, Office of Policy Development and Integration, Thought Leadership, and AARP International.

Dr. Whitman is an authority on aging issues with extensive experience in national policy making, domestic and international research, and the political process. An economist, she is a strategic thinker whose career has been dedicated to solving problems affecting economic and health security, and other issues related to population aging.

As staff director for the U.S. Senate Special Committee on Aging, Dr. Whitman worked across the aisle to increase retirement security, lower the cost of health care, protect vulnerable seniors, safeguard consumers, make the pharmaceutical industry more transparent, and improve our nation’s long term care system.

Before that, Dr. Whitman worked for the Congressional Research Service as a specialist in the economics of aging. She provided members of Congress and their staff with research and advice, and authored analytical reports on the economic impacts of current policies affecting older Americans, as well as the distributional and intergenerational effects of legislative proposals.

A team of math and AI researchers at Microsoft Asia has designed and developed a small language model (SLM) that can be used to solve math problems. The group has posted a paper on the arXiv preprint server outlining the technology and math behind the new tool and how well it has performed on standard benchmarks.

Over the past several years, multiple have been working hard to steadily improve their LLMs, resulting in AI products that have in a very short time become mainstream. Unfortunately, such tools require massive amounts of computer power, which means they consume a lot of electricity, making them expensive to maintain.

Because of that, some in the field have been turning to SLMs, which as their name implies, are smaller and thus far less resource intensive. Some are small enough to run on a local device. One of the main ways AI researchers make the best use of SLMs is by narrowing their focus—instead of trying to answer any question about anything, they are designed to answer questions about something much more specific—like math. In this new effort, Microsoft has focused its efforts on not just solving , but also in teaching an SLM how to reason its way through a problem.

Quantum mechanics has long classified particles into just two distinct types: fermions and bosons.

Now physicists from Rice University in the US have found a third type might be possible after all, at least mathematically speaking. Known as a paraparticles, their behavior could imply the existence of elementary particles nobody has ever considered.

“We determined that new types of particles we never knew of before are possible,” says Kaden Hazzard, who with co-author Zhiyuan Wang formulated a theory to demonstrate how objects that weren’t fermions or bosons could exist in physical reality without breaking any known laws.

Students from the Toms River School District in New Jersey will have the chance to connect with NASA astronauts Don Pettit and Butch Wilmore as they answer prerecorded science, technology, engineering, and mathematics (STEM) related questions from aboard the International Space Station.

Watch the 20-minute space-to-Earth call in collaboration with Science Friday at 10 a.m. EST on Tuesday, Jan. 14, on NASA+ and learn how to watch NASA content on various platforms, including social media.

Science Friday is a nonprofit dedicated to sharing science with the public through storytelling, educational programs, and connections with audiences. Middle school students will use their knowledge from the educational downlink to address environmental problems in their communities.

From the early days of quantum mechanics, scientists have thought that all particles can be categorized into one of two groups—bosons or fermions—based on their behavior.

However, new research by Rice University physicist Kaden Hazzard and former Rice graduate student Zhiyuan Wang shows the possibility of particles that are neither bosons nor fermions. Their study, published in Nature, mathematically demonstrates the potential existence of paraparticles that have long been thought impossible.

“We determined that new types of particles we never knew of before are possible,” said Hazzard, associate professor of physics and astronomy.

Beyond fermions and bosons: unveiling new particle behaviors in mechanics.

In the world, particles traditionally fall into two categories: fermions (like electrons) and bosons (like photons), each obeying distinct exchange rules. These “exchange statistics” shape the behaviors of particles, from the structure of atoms to the glow of lasers. In two dimensions, a peculiar third type, called anyons, has been theorized and observed, adding a twist to this framework. But could there be even more possibilities?

This study ventures into uncharted territory by revisiting “parastatistics,” an idea from theory that goes beyond fermions and bosons. Previously dismissed as merely theoretical and equivalent to the known particle types, parastatistics now emerges in a new light. The researchers reveal that particles obeying non-trivial parastatistics can exist in real physical systems and behave in fundamentally different ways. These “paraparticles” follow unique rules of exclusion, resulting in strange and exotic thermodynamic behaviors unlike any seen in fermions or bosons.

To bring this concept to life, the team developed a mathematical framework for paraparticles, showing how they naturally fit within the broader universe. They designed solvable models where paraparticles arise as quasiparticles—tiny, particle-like excitations in materials—observable through their distinct exchange behavior. Remarkably, these models work in both one and two dimensions, demonstrating the tangible potential of paraparticles in real-world systems.

The findings hint at exciting possibilities: a new class of quasiparticles in condensed matter physics and, perhaps more provocatively, the existence of elementary particles governed by entirely novel statistics. This discovery could expand our understanding of the world and open the door to unimagined phenomena in both theory and experiment.


Explore #quantum at Facebook.

proudly announces the top 300 scholars in the Regeneron Science Talent Search 2025, the nation’s oldest and most prestigious science and math competition for high school seniors. The Regeneron Science Talent Search provides students a national stage to present original research and celebrates the hard work and novel discoveries of young scientists who are bringing a fresh perspective to significant global challenges. The 300 scholars and their schools will be awarded $2,000 each.

Scholars were chosen based on their outstanding research, leadership skills, community involvement, commitment to academics, creativity in asking scientific questions and exceptional promise as STEM leaders demonstrated through the submission of their original, independent research projects, essays and recommendations. The 300 scholars hail from 200 American and international high schools and homeschools in 33 states, Washington D.C., Hong Kong, Malaysia, and Switzerland.

A breakthrough in artificial intelligence.

Artificial Intelligence (AI) is a branch of computer science focused on creating systems that can perform tasks typically requiring human intelligence. These tasks include understanding natural language, recognizing patterns, solving problems, and learning from experience. AI technologies use algorithms and massive amounts of data to train models that can make decisions, automate processes, and improve over time through machine learning. The applications of AI are diverse, impacting fields such as healthcare, finance, automotive, and entertainment, fundamentally changing the way we interact with technology.

Quantum physics is a very diverse field: it describes particle collisions shortly after the Big Bang as well as electrons in solid materials or atoms far out in space. But not all quantum objects are equally easy to study. For some—such as the early universe—direct experiments are not possible at all.

However, in many cases, quantum simulators can be used instead: one quantum system (for example, a cloud of ultracold atoms) is studied in order to learn something about another system that looks physically very different, but still follows the same laws, i.e. adheres to the same mathematical equations.

It is often difficult to find out which equations determine a particular quantum system. Normally, one first has to make theoretical assumptions and then conduct experiments to check whether these assumptions prove correct.

The relationship between brain and computer is a perennial theme in theoretical neuroscience, but it has received relatively little attention in the philosophy of neuroscience. This paper argues that much of the popularity of the brain-computer comparison (e.g. circuit models of neurons and brain areas since McCulloch and Pitts, Bull Math Biophys 5: 115–33, 1943) can be explained by their utility as ways of simplifying the brain. More specifically, by justifying a sharp distinction between aspects of neural anatomy and physiology that serve information-processing, and those that are ‘mere metabolic support,’ the computational framework provides a means of abstracting away from the complexities of cellular neurobiology, as those details come to be classified as irrelevant to the (computational) functions of the system.