Lifeboat Foundation

Everything we know, think and feel—everything!—comes from our brains. But consciousness, our private sense of inner awareness, remains a mystery. Brain activities—spiking of neuronal impulses, sloshing of neurochemicals—are not at all the same thing as sights, sounds, smells, emotions. How on earth can our inner experiences be explained in physical terms?

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Continue reading “Peter Tse — What Makes Brains Conscious?” »

We can model the motions of planets in the Solar System quite accurately using Newton’s laws of physics. But in the early 1970s, scientists noticed that this didn’t work for disk galaxies —stars at their outer edges, far from the gravitational force of all the matter at their center—were moving much faster than Newton’s theory predicted.

This made physicists propose that an invisible substance called “dark matter” was providing extra gravitational pull, causing the stars to speed up—a theory that’s become hugely popular. However, in a recent review my colleagues and I suggest that observations across a vast range of scales are much better explained in an alternative theory of gravity proposed by Israeli physicist Mordehai Milgrom in 1982 called Milgromian dynamics or Mond —requiring no invisible matter.

Mond’s main postulate is that when gravity becomes very weak, as occurs at the edge of galaxies, it starts behaving differently from Newtonian physics. In this way, it is possible to explain why stars, planets and gas in the outskirts of over 150 galaxies rotate faster than expected based on just their visible mass. But Mond doesn’t merely explain such rotation curves, in many cases, it predicts them.

Physicists say the third run will collect more data than the previous two combined.

Hot water really might freeze faster than cold water.

For decades, we’ve dreamed of visiting other star systems. There’s just one problem – they’re so far away, with conventional spaceflight it would take tens of thousands of years to reach even the closest one.

Physicists are not the kind of people who give up easily, though. Give them an impossible dream, and they’ll give you an incredible, hypothetical way of making it a reality. Maybe.

Continue reading “Faster-Than-Light Travel Could Work Within Einstein’s Physics, Astrophysicist Shows” »

Stochastic thermodynamics is an emerging area of physics aimed at better understanding and interpreting thermodynamic concepts away from equilibrium. Over the past few years, findings in these fields have revolutionized the general understanding of different thermodynamic processes operating in finite time.

Adam Frim and Mike DeWeese, two researchers at the University of California, Berkeley (UC Berkeley), have recently carried out a theoretical study exploring the full space of thermodynamic cycles with a continuously changing bath temperature. Their results, presented in a paper published in Physical Review Letters, were obtained using geometric methods. Thermodynamic geometry is an approach to understanding the response of thermodynamic systems by means of studying the geometric space of control.

“For instance, for a gas in a piston, one coordinate in this space of control could correspond to the experimentally controlled volume of the gas and another to the temperature,” DeWeese told Phys.org. “If an experimentalist were to turn those knobs, that plots out some trajectory in this thermodynamic space. What thermodynamic geometry does is assign to each curve a ‘thermodynamic length’ corresponding to the minimum possible dissipated energy of a given path.”

Physicists are building neural networks out of vibrations, voltages and lasers, arguing that the future of computing lies in exploiting the universe’s complex physical behaviors.

Physicists find that causal loops, where two events separated in time influence each other in paradoxical ways, are allowed in many theoretical universes, some of which share features with our own.

Large language models are widely adopted in a range of natural language tasks, such as question-answering, common sense reasoning, and summarization. These models, however, have had difficulty with tasks requiring quantitative reasoning, such as resolving issues in mathematics, physics, and engineering.

Researchers find quantitative reasoning an intriguing application for language models as they put language models to the test in various ways. The ability to accurately parse a query with normal language and mathematical notation, remember pertinent formulas and constants and produce step-by-step answers requiring numerical computations and symbolic manipulation are necessary for solving mathematical and scientific problems. Therefore, scientists have believed that machine learning models will require significant improvements in model architecture and training methods to solve such reasoning problems.

A new Google research introduces Minerva, a language model that uses sequential reasoning to answer mathematical and scientific problems. Minerva resolves such problems by providing solutions incorporating numerical computations and symbolic manipulation.