Lifeboat Foundation

From repairing deadly brain bleeds to tackling tumors with precise chemotherapy, micro/nano-robots (MNRs) are a promising, up-and-coming tool that have the power to substantially advance health care. However, this tool still has difficulty navigating within the human body—a limitation that has prevented it from entering clinical trials.

Mathematical models are crucial to the optimal design and navigation of MNRs, but the current models are inadequate. Now, new, promising research from the University of Saskatchewan (USask) may allow MNRs to overcome the limitations that previously prevented their widespread use.

USask College of Engineering professor Dr. Chris Zhang (Ph. D.) and two Ph.D. students (Lujia Ding, N.N Hu) along with two USask alumni (Dr. Bing Zhang (Ph. D.), Dr. R. Y. Yin (Ph. D.)) are the first team to develop a highly accurate mathematical model that optimizes the design of MNRs which improves their navigation, allowing them to travel efficiently through the bloodstream. Their work was recently published in Nature Communications.

Team develops simulation algorithms for safer, greener, and more aerodynamic aircraft.

Ice buildup on aircraft wings and fuselage occurs when atmospheric conditions conducive to ice formation are encountered during flight, presenting a critical area of focus for their research endeavors.

Ice accumulation on an aircraft during flight poses a significant risk, potentially impairing its performance and, in severe cases, leading to catastrophic consequences.

Continue reading “Math professor could help answer physics of ice buildup on planes” »

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The parallels between human memory and vector databases go deeper than simple retrieval. Both excel at compression, reducing complex information into manageable patterns. Both organize information hierarchically, from specific instances to general concepts. And both excel at finding similarities and patterns that might not be obvious at first glance.

This isn’t just about professional efficiency — it’s about preparing for a fundamental shift in how we interact with information and technology. Just as literacy transformed human society, these evolved communication skills will be essential for full participation in the AI-augmented economy. But unlike previous technological revolutions that sometimes replaced human capabilities, this one is about enhancement. Vector databases and AI systems, no matter how advanced, lack the uniquely human qualities of creativity, intuition, and emotional intelligence.

The future belongs to those who understand how to think and communicate in vectors — not to replace human thinking, but to enhance it. Just as vector databases combine precise mathematical representation with intuitive pattern matching, successful professionals will blend human creativity with AI’s analytical power. This isn’t about competing with AI or simply learning new tools — it’s about evolving our fundamental communication skills to work in harmony with these new cognitive technologies.

Gregory Chaitin ‘s work will limit the capability of AI.

The rigid structures of language we once clung to with certainty are cracking. Take gender, nationality or religion: these concepts no longer sit comfortably in the stiff linguistic boxes of the last century. Simultaneously, the rise of AI presses upon us the need to understand how words relate to meaning and reasoning.

A global group of philosophers, mathematicians and computer scientists have come up with a new understanding of logic that addresses these concerns, dubbed “inferentialism”

One standard intuition of logic, dating back at least to Aristotle, is that a logical consequence ought to hold by virtue of the content of the propositions involved, not simply by virtue of being “true” or “false”. Recently, the Swedish logician Dag Prawitz observed that, perhaps surprisingly, the traditional treatment of logic entirely fails to capture this intuition.

Researchers at New York University have devised a mathematical approach to predict the structures of crystals—a critical step in developing many medicines and electronic devices—in a matter of hours using only a laptop, a process that previously took a supercomputer weeks or months. Their novel framework is published in the journal Nature Communications.

A team of AI researchers and mathematicians affiliated with several institutions in the U.S. and the U.K. has developed a math benchmark that allows scientists to test the ability of AI systems to solve exceptionally difficult math problems. Their paper is posted on the arXiv preprint server.

The unexpected discovery of a geometric phase shows how math and physics are tightly intertwined.

By Manon Bischoff

I didn’t find math particularly exciting when I was in high school. To be honest, I only studied it when I went to university because it initially seemed quite easy to me. But in my very first math lecture as an undergraduate, I realized that everything I thought I knew about math was wrong. It was anything but easy. Mathematics, I soon discovered, can be really exciting—especially if you go beyond the realm of pure arithmetic.