Lifeboat Foundation

Cold Spring Harbor Laboratory scientists developed an AI algorithm inspired by the genome’s efficiency, achieving remarkable data compression and task performance.

In a sense, each of us begins life ready for action. Many animals perform amazing feats soon after they’re born. Spiders spin webs. Whales swim. But where do these innate abilities come from? Obviously, the brain plays a key role as it contains the trillions of neural connections needed to control complex behaviors.

However, the genome has space for only a small fraction of that information. This paradox has stumped scientists for decades. Now, Cold Spring Harbor Laboratory (CSHL) Professors Anthony Zador and Alexei Koulakov have devised a potential solution using artificial intelligence.

Some researchers propose that advancing AI to the next level will require an internal architecture that more closely mirrors the human mind. Rufin VanRullen joins Brian Greene to discuss early results from one such approach, based on the Global Workspace Theory of consciousness.

This program is part of the Big Ideas series, supported by the John Templeton Foundation.

Continue reading “Can Models of Human Consciousness Enhance AI Capabilities?” »

Theoretical physicists have established a close connection between the two rapidly developing fields in theoretical physics, quantum information theory and non-invertible symmetries in particle and condensed matter theories, after proving that any non-invertible symmetry operation in theoretical physics is a quantum operation. The study was published in Physical Review Letters as an Editors’ Suggestion on November 6.

In physics, symmetry provides an important clue to the properties of a theory. For example, if the N-poles in a magnetic field are replaced by the S-poles, and the S-poles by the N-poles all at once, the forces on objects and the energy stored in the magnetic field remain the same, even though the direction of the magnetic field has now become reversed. This is because the equations describing the magnetic field are symmetric with respect to the operation of swapping the N and S poles.

Continue reading “Researchers uncover link between quantum information theory and particle and condensed matter physics” »

Artificial intelligence has the potential to improve the analysis of medical image data. For example, algorithms based on deep learning can determine the location and size of tumors. This is the result of AutoPET, an international competition in medical image analysis, where researchers of Karlsruhe Institute of Technology (KIT) were ranked fifth.

The seven best autoPET teams report in the journal Nature Machine Intelligence on how algorithms can detect tumor lesions in positron emission tomography (PET) and computed tomography (CT).

Imaging techniques play a key role in the diagnosis of cancer. Precisely determining the location, size, and type of tumor is essential for choosing the right therapy. The most important imaging techniques include positron emission tomography (PET) and computer tomography (CT).

Breaking oxygen out of a water molecule is a relatively simple process, at least chemically. Even so, it does require components, one of the most important of which is a catalyst. Catalysts enable reactions and are linearly scalable, so if you want more reactions quickly, you need a bigger catalyst. In space exploration, bigger means heavier, which translates into more expensive. So, when humanity is looking for a catalyst to split water into oxygen and hydrogen on Mars, creating one from local Martian materials would be worthwhile. That is precisely what a team from Hefei, China, did by using what they called an “AI Chemist.”

Unfortunately, the name “AIChemist” didn’t stick, though that joke might vary depending on the font you read it in. Whatever its name, the team’s work was some serious science. It specifically applied machine learning algorithms that have become all the rage lately to selecting an effective catalyst for an “oxygen evolution reaction” by utilizing materials native to Mars.

Continue reading “An AI Chemist Made A Catalyst to Make Oxygen On Mars Using Local Materials” »

Vorticity, a measure of the local rotation or swirling motion in a fluid, has long been studied by physicists and mathematicians. The dynamics of vorticity is governed by the famed Navier-Stokes equations, which tell us that vorticity is produced by the passage of fluid past walls. Moreover, due to their internal resistance to being sheared, viscous fluids will diffuse the vorticity within them and so any persistent swirling motions will require a constant resupply of vorticity.

Physicists at the University of Chicago and applied mathematicians at the Flatiron Institute recently carried out a study exploring the behavior of viscous fluids in which tiny rotating particles were suspended, acting as local, mobile sources of vorticity. Their paper, published in Nature Physics, outlines fluid behaviors that were never observed before, characterized by self-propulsion, flocking and the emergence of chiral active phases.

“This experiment was a confluence of three curiosities,” William T.M. Irvine, a corresponding author of the paper, told Phys.org. “We had been studying and engineering parity-breaking meta-fluids with fundamentally new properties in 2D and were interested to see how a three-dimensional analog would behave.

Quantum computers operate using quantum gates, but the complexity and large number of these gates can diminish their efficiency. A new “hybrid” approach reduces this complexity by utilizing natural system interactions, making quantum algorithms easier to execute.

This innovation helps manage the inherent “noise” issues of current quantum systems, enhancing their practical use. The approach has been effectively demonstrated with Grover’s algorithm, enabling efficient searches of large datasets without extensive error correction.

Challenges of Quantum Computing.

Every day, researchers at the Department of Energy’s SLAC National Accelerator Laboratory tackle some of the biggest questions in science and technology—from laying the foundations for new drugs to developing new battery materials and solving big data challenges associated with particle physics and cosmology.

To get a hand with that work, they are increasingly turning to artificial intelligence. “AI will help accelerate our science and technology further,” said Ryan Coffee, a SLAC senior scientist. “I am really excited about that.”

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