The German scientist argues that information cannot be destroyed and, in principle, it is possible that a higher being, one day, in some way, could reassemble it and bring it back to life.
The foundation of this simulation, as described by the team, is a well-known cosmological model that describes the universe as expanding uniformly over time. The researchers modeled how a quantum field, initially in a vacuum state (meaning no particles are present), responds to this expansion. As spacetime stretches, the field’s oscillations mix in a process that can create particles where none previously existed. This phenomenon is captured by a transformation that relates the field’s behavior before and after the universe expands, showing how vibrations at different momenta become entangled, leading to particle creation.
To understand how many particles are generated, the researchers used a mathematical tool called the Bogoliubov transformation. This approach describes how the field’s vacuum state evolves into a state where particles can be detected. As the expansion rate increases, more particles are produced, aligning with predictions from quantum field theory. By running this simulation on IBM quantum computers, the team was able to estimate the number of particles created and observe how the quantum field behaves during the universe’s expansion, offering a new way to explore complex cosmological phenomena.
According to the team, the most notable result of the study was the ability to estimate the number of particles created as a function of the expansion rate of the universe. By running their quantum circuit on both simulators and IBM’s 127-qubit Eagle quantum processor, the researchers demonstrated that they could successfully simulate particle creation in a cosmological context. While the results were noisy—particularly for low expansion rates—the error mitigation techniques used helped bring the outcomes closer to theoretical predictions.
We tackle the hard problem of consciousness taking the naturally-selected, self-organising, embodied organism as our starting point. We provide a mathematical formalism describing how biological systems self-organise to hierarchically interpret unlabelled sensory information according to valence and specific needs. Such interpretations imply behavioural policies which can only be differentiated from each other by the qualitative aspect of information processing. Selection pressures favour systems that can intervene in the world to achieve homeostatic and reproductive goals. Quality is a property arising in such systems to link cause to affect to motivate real world interventions. This produces a range of qualitative classifiers (interoceptive and exteroceptive) that motivate specific actions and determine priorities and preferences.
I have been thinking for a while about the mathematics used to formulate our physical theories, especially the similarities and differences among different mathematical formulations. This was a focus of my 2021 book, Physics, Structure, and Reality, where I discussed these things in the context of classical and spacetime physics.
Recently this has led me toward thinking about mathematical formulations of quantum mechanics, where an interesting question arises concerning the use of complex numbers. (I recently secured a grant from the National Science Foundation for a project investigating this.)
It is frequently said by physicists that complex numbers are essential to formulating quantum mechanics, and that this is different from the situation in classical physics, where complex numbers appear as a useful but ultimately dispensable calculational tool. It is not often said why, or in what way, complex numbers are supposed to be essential to quantum mechanics as opposed to classical physics.
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In work that has been 30 years in the making, mathematicians have proved a major part of a profound mathematical vision called the Langlands program.
We’re joined by Dr. Denis Noble, Professor Emeritus of Cardiovascular Physiology at the University of Oxford, and the father of ‘systems biology’. He is known for his groundbreaking creation of the first mathematical model of the heart’s electrical activity in the 1960s which radically transformed our understanding of the heart.
Dr. Noble’s contributions have revolutionized our understanding of cardiac function and the broader field of biology. His work continues to challenge long-standing biological concepts, including gene-centric views like Neo-Darwinism.
Continue reading “Denis Noble — Why The Last 80 Years of Biology was Wrong” »
As President, Jimmy Carter established several science-related initiatives and policies.
Carter also sought to promote scientific research and development in a number of areas. He increased funding for basic science research in fields such as physics and chemistry, and established the National Commission on Excellence in Education to promote improvements in science and math education in American schools.
Continue reading “Looking back at President Jimmy Carter’s science policy” »
Vanderbilt University researchers, led by alumnus Bryan Gitschlag, have uncovered groundbreaking insights into the evolution of mitochondrial DNA (mtDNA). In their paper in Nature Communications titled “Multiple distinct evolutionary mechanisms govern the dynamics of selfish mitochondrial genomes in Caenorhabditis elegans,” the team reveals how selfish mtDNA, which can reduce the fitness of its host, manages to persist within cells through aggressive competition or by avoiding traditional selection pressures. The study combines mathematical models and experiments to explain the coexistence of selfish and cooperative mtDNA within the cell, offering new insights into the complex evolutionary dynamics of these essential cellular components.
Gitschlag, an alumnus of Vanderbilt University, conducted the research while in the lab of Maulik Patel, assistant professor of biological sciences. He is now a postdoctoral researcher at Cold Spring Harbor Laboratory in David McCandlish’s lab. Gitschlag collaborated closely with fellow Patel Lab members, including James Held, a recent PhD graduate, and Claudia Pereira, a former staff member of the lab.
What I believe is that symmetry follows everything even mathematics but what explains it is the Fibonacci equation because it seems to show the grand design of everything much like physics has I believe the final parameter of the quantified parameter of infinity.
Recent explorations of unique geometric worlds reveal perplexing patterns, including the Fibonacci sequence and the golden ratio.
The large language models that have increasingly taken over the tech world are not “cheap” in many ways. The most prominent LLMs, such as GPT-4, took some $100 million to build in the form of legal costs of accessing training data, computational power costs for what could be billions or trillions of parameters, the energy and water needed to fuel computation, and the many coders developing the training algorithms that must run cycle after cycle so the machine will “learn.”
But, if a researcher needs to do a specialized task that a machine could do more efficiently and they don’t have access to a large institution that offers access to generative AI tools, what other options are available? Say, a parent wants to prep their child for a difficult test and needs to show many examples of how to solve complicated math problems.
Building their own LLM is an onerous prospect for costs mentioned above, and making direct use of the big models like GPT-4 and Llama 3.1 might not immediately be suited for the complex reasoning in logic and math their task requires.
