Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: information science – Page 24

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.

Participant: Rufin VanRullen.
Moderator: Brian Greene.

00:00 — Introduction.
02:06 — Participant Introduction.
03:12 — VanRullin’s journey from neuroscience to artificial neural networks.
05:25 — Algorithmic approach to neural networks.
08:02 — Simulation of information processing.
09:25 — Global Workspace Theory.
21:33 — Global Workspace providing insight on consciousness.
23:14 — Role of language in consciousness and replicating intelligence.
25:30 — Developing consciousness in AI systems.
31:38 — How to recognize if AI has developed consciousness.
32:32 — Time scale of Global Workspace Theory and emergence of consciousness in AI
34:45 — Credits.

VISIT our Website: http://www.worldsciencefestival.com.
FOLLOW us on Social Media:
Facebook: / worldsciencefestival.
Twitter: / worldscifest.
Instagram: / worldscifest.
TikTok: / worldscifest.
LinkedIn: / world-science-festival.
#worldsciencefestival #briangreene #rufinvanrullen #ai #artificialintelligence #computerscience #consciousness

Read more | >

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 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.

Over the past few years, the concept of symmetries has received generalization in various directions in the theoretical study of particle physics and condensed matter physics, becoming an active area of research. One such generalization is non-invertible symmetry. The operation of conventional symmetries is always invertible. There exists a reverse operation to undo it. Non-invertible symmetry, on the other hand, allows certain non-invertibility in such symmetry operations.

Read more | >

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 lesions in (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).

Read more | >

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.

To say it only chose the catalyst isn’t giving the system the full credit it’s due, though. It accomplished a series of steps, including developing a catalyst formula, pretreating the ore to create the catalyst, synthesizing it, and testing it once it was complete. The authors estimate that the automated process saved over 2,000 years of human labor by completing all of these tasks and point to the exceptional results of the testing to prove it.

Read more | >

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.

Read more | >

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.

Read more | >

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.”

Read more | >

To try everything Brilliant has to offer—free—for a full 30 days, visit https://brilliant.org/ArtemKirsanov. You’ll also get 20% off an annual premium subscription.

Socials:
X/Twitter: https://twitter.com/ArtemKRSV
Patreon: / artemkirsanov.

My name is Artem, I’m a graduate student at NYU Center for Neural Science and researcher at Flatiron Institute (Center for Computational Neuroscience).

In this video, we explore the Nobel Prize-winning Hodgkin-Huxley model, the foundational equation of computational neuroscience that reveals how neurons generate electrical signals. We break down the biophysical principles of neural computation, from membrane voltage to ion channels, showing how mathematical equations capture the elegant dance of charged particles that enables information processing.

Outline:
00:00 Introduction.
01:28 Membrane Voltage.
04:56 Action Potential Overview.
6:24 Equilibrium potential and driving force.
10:11 Voltage-dependent conductance.
16:50 Review.
20:09 Limitations \& Outlook.
21:21 Sponsor: Brilliant.org.
22:44 Outro.

Read more | >

A novel device consisting of metal, dielectric, and metal layers remembers the history of electrical signals sent through it. This device, called a memristor, could serve as the basis for neuromorphic computers-;computers that work in ways similar to human brains. Unlike traditional digital memory, which stores information as 0s and 1s, this device exhibits so-called “analog” behavior. This means the device can store information between 0 and 1, and it can emulate how synapses function in the brain. Researchers found that the interface between metal and dielectric in the novel device is critical for stable switching and enhanced performance. Simulations indicate that circuits built on this device exhibit improved image recognition.

The Impact

Today’s computers are not energy efficient for big data and machine learning tasks. By 2030, experts predict that data centers could consume about 8% of the world’s electricity. To address this challenge, researchers are working to create computers inspired by the human brain, so-called neuromorphic computers. Artificial synapses created with memristor devices are the building blocks of these computers. These artificial synapses can store and process information in the same location, similar to how neurons and synapses work in the brain. Integrating these emergent devices with conventional computer components will reduce power needs and improve performance for tasks such as artificial intelligence and machine learning.

Read more | >

Summary: A new AI algorithm inspired by the genome’s ability to compress vast information offers insights into brain function and potential tech applications. Researchers found that this algorithm performs tasks like image recognition and video games almost as effectively as fully trained AI networks.

By mimicking how genomes encode complex behaviors with limited data, the model highlights the evolutionary advantage of efficient information compression. The findings suggest new pathways for developing advanced, lightweight AI systems capable of running on smaller devices like smartphones.

Read more | >