Lifeboat Foundation

Caltech researchers have discovered Hubbard excitons, which are excitons bound magnetically, offering new avenues for exciton-based technological applications.

In art, the negative space in a painting can be just as important as the painting itself. Something similar is true in insulating materials, where the empty spaces left behind by missing electrons play a crucial role in determining the material’s properties. When a negatively charged electron is excited by light, it leaves behind a positive hole. Because the hole and the electron are oppositely charged, they are attracted to each other and form a bond. The resulting pair, which is short-lived, is known as an exciton [pronounced exit-tawn].

Excitons are integral to many technologies, such as solar panels, photodetectors, and sensors. They are also a key part of light-emitting diodes found in televisions and digital display screens. In most cases, the exciton pairs are bound by electrical, or electrostatic, forces, also known as Coulomb interactions.

With the rise of brain-interface technology and artificial intelligence that can imitate brain functions, understanding the nature of consciousness and how it interacts with reality is not just an age-old philosophical question but also a salient challenge for humanity.

Can AI become conscious, and how would we know? Should we incorporate human or animal cells, such as neurons, into machines and robots? Would they be conscious and have subjective experiences? Does consciousness reduce to physicalism, or is it fundamental? And if machine-brain interaction influenced you to commit a crime, or caused a crime, would you be responsible beyond a reasonable doubt? Do we have a free will?

AI and computer science specialist Dr. Mahendra Samarawickrama, winner of the Australian Computer Society’s Information and Communications Technology (ICT) Professional of the year, has applied his knowledge of physics and artificial neural networks to this thorny topic.

A new model demonstrates that chasing interactions can induce dynamical patterns in the organization of bacterial species. Structural patterns can be created due to the chasing interactions between two bacterial species.

In the new model, scientists from the Max Planck Institute for Dynamics and Self-Organization (MPI-DS) describe how interactions on the individual level can result in a global self-organization of species. Their findings provide insights into general mechanisms of collective behavior. The findings are published in the journal Physical Review Letters.

In a recent study, scientists from the department Living Matter Physics at MPI-DS developed a model describing communication pathways in bacterial populations. Bacteria show an overall organizational pattern by sensing the concentration of chemicals in their environment and adapting their motion.

Physicists have long suspected that hunks of metal could vibrate in a peculiar way that would be all but invisible. Now physicists have spotted these “demon modes.”

A University of Portsmouth physicist has explored whether a new law of physics could support the much-debated theory that we are simply characters in an advanced virtual world.

The simulated universe hypothesis proposes that what humans experience is actually an artificial reality, much like a computer simulation, in which they themselves are constructs.

The theory is popular among a number of well-known figures including Elon Musk, and within a branch of science known as information physics, which suggests physical reality is fundamentally made up of bits of information.

The most widely used machine learning algorithms were designed by humans and thus are hindered by our cognitive biases and limitations. Can we also construct meta-learning algorithms that can learn better learning algorithms so that our self-improving AIs have no limits other than those inherited from computability and physics? This question has been a main driver of my research since I wrote a thesis on it in 1987. In the past decade, it has become a driver of many other people’s research as well. Here I summarize our work starting in 1994 on meta-reinforcement learning with self-modifying policies in a single lifelong trial, and — since 2003 — mathematically optimal meta-learning through the self-referential Gödel Machine. This talk was previously presented at meta-learning workshops at ICML 2020 and NeurIPS 2021. Many additional publications on meta-learning can be found at https://people.idsia.ch/~juergen/metalearning.html.

Jürgen Schmidhuber.
Director, AI Initiative, KAUST
Scientific Director of the Swiss AI Lab IDSIA
Co-Founder & Chief Scientist, NNAISENSE
http://www.idsia.ch/~juergen/blog.html.

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To make its weather predictions, it analyzes 60 million daily observations from satellite, aircraft, and ground-based reports, using what we know about atmospheric physics to determine what the weather is likely to be like across the globe over the next 15 days.

This can literally save lives — if people know in advance that hurricanes or winter storms are heading their way, they can take action to prepare — but because the model is so complex, it must be run on a supercomputer over the course of several hours, which also makes it expensive.

The AIs: AI-based weather forecasting models are starting to catch up with traditional ones, like the European Model.

They announced the Nobel prizes this week! But did any of the recipients teach an AI to play Street Fighter? Here are a few of this week’s stories not yet lauded by international committees of scientists, but which we thought were pretty good:

Even if you think a galaxy is old enough to drink, you should probably go ahead and ask for ID before you serve them. The earliest galaxies in the universe captured by the James Webb Space Telescope appeared too bright, massive and way too old to have formed that soon after the Big Bang, presenting a problem for astronomers and their favorite model, the standard model of cosmology.

Recently, a team of physicists at Northwestern University used computer simulations to model galaxy formation after the Big Bang and demonstrate that (at least in the model universe) stars formed in bursts, producing light of enormously greater intensity than a modern galaxy like, say, Andromeda, where star formation is steady and the number of stars gradually increases over time.

An international team of researchers has developed a new theoretical framework that bridges physics and biology to provide a unified approach for understanding how complexity and evolution emerge in nature.

This new work on “assembly theory,” published today in Nature, represents a major advance in our fundamental comprehension of biological evolution and how it is governed by the physical laws of the universe. The paper is titled “Assembly Theory Explains and Quantifies Selection and Evolution.”

This research builds on the team’s previous work developing assembly theory as an empirically validated approach to life detection, with implications for the search for alien life and efforts to evolve new life forms in the laboratory.