Lifeboat Foundation

Though almost every cell in your body contains a copy of each of your genes, only a small fraction of these genes will be expressed, or turned on. These activations are controlled by specialized snippets of DNA called enhancers, which act like skillful on-off switches. This selective activation allows cells to adopt specific functions in the body, determining whether they become—for example—heart cells, muscle cells, or brain cells.

However, these enhancers don’t always turn on the right genes at the right time, contributing to the development of genetic diseases like cancer and diabetes. A team of Johns Hopkins biomedical engineers has developed a machine-learning model that can predict which enhancers play a role in normal development and disease—an innovation that could someday power the development of enhancer-targeted therapies to treat diseases by turning genes on and off at will. The study results appeared in Nature Genetics.

“We’ve known that enhancers control transitions between cell types for a long time, but what is exciting about this work is that mathematical modeling is showing us how they might be controlled,” said study leader Michael Beer, a professor of biomedical engineering and genetic medicine at Johns Hopkins University.

The human brain is the most complex and powerful computer in the world — and, as far as we know, the universe.

Today’s most sophisticated artificial intelligence (AI) algorithms are only just beginning to offer a partial simulation of a very limited number of the brain’s functions. AI is, however, much faster when it comes to certain operations like mathematics and language.

This means it comes as no surprise that a great deal of thought and research has gone into combining the two. The idea is to use AI to better understand the workings of the brain and eventually create more accurate simulations of it. One day, it may also help us to create systems with the complexity and diversity of… More.

Continue reading “AI’s Next Frontier: Are Brain-Computer Interfaces The Future Of Communication?” »

We often believe computers are more efficient than humans. After all, computers can complete a complex math equation in a moment and can also recall the name of that one actor we keep forgetting. However, human brains can process complicated layers of information quickly, accurately, and with almost no energy input: recognizing a face after only seeing it once or instantly knowing the difference between a mountain and the ocean.

These simple human tasks require enormous processing and energy input from computers, and even then, with varying degrees of accuracy.

Creating brain-like computers with minimal energy requirements would revolutionize nearly every aspect of modern life. Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C)—a nationwide consortium led by the University of California San Diego—has been at the forefront of this research.

An interdisciplinary team of mathematicians, engineers, physicists, and medical scientists have uncovered an unexpected link between pure mathematics and genetics, that reveals key insights into the structure of neutral mutations and the evolution of organisms.

Number theory, the study of the properties of positive integers, is perhaps the purest form of mathematics. At first sight, it may seem far too abstract to apply to the natural world. In fact, the influential American number theorist Leonard Dickson wrote ‘Thank God that number theory is unsullied by any application.’

And yet, again and again, number theory finds unexpected applications in science and engineering, from leaf angles that (almost) universally follow the Fibonacci sequence, to modern encryption techniques based on factoring prime numbers. Now, researchers have demonstrated an unexpected link between number theory and evolutionary genetics.

There is increasing talk of quantum computers and how they will allow us to solve problems that traditional computers cannot solve. It’s important to note that quantum computers will not replace traditional computers: they are only intended to solve problems other than those that can be solved with classical mainframe computers and supercomputers. And any problem that is impossible to solve with classical computers will also be impossible with quantum computers. And traditional computers will always be more adept than quantum computers at memory-intensive tasks such as sending and receiving e-mail messages, managing documents and spreadsheets, desktop publishing, and so on.

There is nothing “magic” about quantum computers. Still, the mathematics and physics that govern their operation are more complex and reside in quantum physics.

The idea of quantum physics is still surrounded by an aura of great intellectual distance from the vast majority of us. It is a subject associated with the great minds of the 20^th century such as Karl Heisenberg, Niels Bohr, Max Planck, Wolfgang Pauli, and Erwin Schrodinger, whose famous hypothetical cat experiment was popularized in an episode of the hit TV show ‘The Big Bang Theory’. As for Schrodinger, his observations of the uncertainty principle, serve as a reminder of the enigmatic nature of quantum mechanics. The uncertainty principle holds that the observer determines the characteristics of an examined particle (charge, spin, position) only at the moment of detection. Schrödinger explained this using the theoretical experiment, known as the paradox of Schrödinger’s cat. The experiment’s worth mentioning, as it describes one of the most important aspects of quantum computing.

Structured light waves with spiral phase fronts carry orbital angular momentum (OAM), attributed to the rotational motion of photons. Recently, scientists have been using light waves with OAM, and these special “helical” light beams have become very important in various advanced technologies like communication, imaging, and quantum information processing. In these technologies, it’s crucial to know the exact structure of these special light beams. However, this has proven to be quite tricky.

Interferometry—superimposing a light field with a known reference field to extract information from the interference—can retrieve OAM spectrum information using a camera. As the camera only records the intensity of the interference, the measurement technique encounters additional crosstalk known as “signal-signal beat interference” (SSBI), which complicates the retrieval process. It’s like hearing multiple overlapping sounds, making it difficult to distinguish the original notes.

In a recent breakthrough reported in Advanced Photonics, researchers from Sun Yat-sen University and École Polytechnique Fédérale de Lausanne (EPFL) used a powerful mathematical tool called the Kramers-Kronig (KK) relation, which helps with understanding and solving the problem. This tool enabled them to untangle the complex helical light pattern from the camera’s intensity-only measurements for single-shot retrieval in simple on-axis interferometry. Exploring the duality between the time-frequency and azimuth-OAM domains, they apply the KK approach to investigate various OAM fields, including Talbot self-imaged petals and fractional OAM modes.

David Chalmers is a philosopher at New York University and the Australian National University. He is Professor of Philosophy and co-director of the Center for Mind, Brain, and Consciousness at NYU, and also Professor of Philosophy at ANU.

Chalmers works in the philosophy of mind and in related areas of philosophy and cognitive science. He is especially interested in consciousness, but am also interested in all sorts of other issues in the philosophy of mind and language, metaphysics and epistemology, and the foundations of cognitive science.

From an early age, he excelled at mathematics, eventually completing his undergraduate education at the University of Adelaide with a Bachelor’s degree in Mathematics and Computer Science. He then briefly studied at Lincoln College at the University of Oxford as a Rhodes Scholar before receiving his PhD at Indiana University Bloomington under Douglas Hofstadter. He was a Postdoctoral Fellow in the Philosophy-Neuroscience-Psychology program directed by Andy Clark at Washington University in St. Louis from 1993 to 1995, and his first professorship was at UC Santa Cruz, from August 1995 to December 1998.

An international collaboration between researchers at the RIKEN Center for Brain Science (CBS) in Japan, the University of Tokyo, and University College London has demonstrated that self-organization of neurons as they learn follows a mathematical theory called the free energy principle.

The principle accurately predicted how real neural networks spontaneously reorganize to distinguish incoming information, as well as how altering neural excitability can disrupt the process. The findings thus have implications for building animal-like artificial intelligences and for understanding cases of impaired learning. The study was published August 7 in Nature Communications.

When we learn to tell the difference between voices, faces, or smells, networks of neurons in our brains automatically organize themselves so that they can distinguish between the different sources of incoming information. This process involves changing the strength of connections between neurons, and is the basis of all learning in the brain.

An interdisciplinary team of mathematicians, engineers, physicists, and medical scientists has discovered a surprising connection between pure mathematics and genetics. This connection sheds light on the structure of neutral mutations and the evolution of organisms.

Number theory, the study of the properties of positive integers, is perhaps the purest form of mathematics. At first sight, it may seem far too abstract to apply to the natural world. In fact, the influential American number theorist Leonard Dickson wrote “Thank God that number theory is unsullied by any application.”

And yet, again and again, number theory finds unexpected applications in science and engineering, from leaf angles that (almost) universally follow the Fibonacci sequence, to modern encryption techniques based on factoring prime numbers. Now, researchers have demonstrated an unexpected link between number theory and evolutionary genetics.