Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: information science – Page 48

The Role Of Quantum Computing In Personalized Medicine

The integration of quantum computing into personalized medicine holds great promise for revolutionizing disease diagnosis, treatment development, and patient outcomes. Quantum computers have the potential to process vast amounts of genetic data much faster than classical computers, enabling researchers to identify patterns and correlations that may not be apparent with current technology. This could lead to breakthroughs in understanding the genetic basis of complex diseases and developing targeted treatments.

Quantum computing also has the potential to revolutionize medical imaging by enabling the simulation of complex magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. Quantum algorithms can efficiently process large-scale imaging data, enabling researchers to reconstruct high-resolution images that reveal subtle details about tissue structure and function. This has significant implications for disease diagnosis and treatment, where accurate imaging is critical for developing effective treatments.

The use of quantum computing in personalized medicine raises important ethical considerations, such as concerns about privacy and informed consent. The ability to rapidly analyze large amounts of genetic data also raises questions about how this information should be used and shared with patients. Regulatory frameworks will play a crucial role in shaping the development and deployment of quantum computing in personalized medicine, balancing the need to promote innovation with the need to protect patient safety and privacy.

Liquid AI’s new STAR model architecture outshines Transformer efficiency

As described in that paper and henceforth, a transformer is a deep learning neural network architecture that processes sequential data, such as text or time-series information.

Now, MIT-birthed startup Liquid AI has introduced STAR (Synthesis of Tailored Architectures), an innovative framework designed to automate the generation and optimization of AI model architectures.

The STAR framework leverages evolutionary algorithms and a numerical encoding system to address the complex challenge of balancing quality and efficiency in deep learning models.

Novel quantum computing algorithm enhances single-cell analysis

A new quantum algorithm developed by University of Georgia statisticians addresses one of the most complex challenges in single-cell analysis, signaling significant impact in both the fields of computational biology and quantum computing.

The study, “Bisection Grover’s Search Algorithm and Its Application in Analyzing CITE-seq Data,” was published in the Journal of the American Statistical Association on Sept. 20.

While traditional approaches struggle to handle the immense amount of data generated from measuring both RNA and in individual cells, the new enables analysis of data from a single-cell technology known as CITE-seq. It allows for selection of the most important markers from billions of possible combinations—a task that would be formidable using classical methods.

Physicists Just Found a Quirk in Einstein’s Predictions of Space-Time

The fabric of space and time is not exempt from the effects of gravity. Plop in a mass and space-time curves around it, not dissimilar to what happens when you put a bowling ball on a trampoline.

This dimple in space-time is the result of what we call a gravity well, and it was first described over 100 years ago by Albert Einstein’s field equations in his theory of general relativity. To this day, those equations have held up. We’d love to know what Einstein was putting in his soup. Whatever it was, general relativity has remained pretty solid.

One of the ways we know this is because when light travels along that curved space-time, it curves along with it. This results in light that reaches us all warped and stretched and replicated and magnified, a phenomenon known as gravitational lensing. This quirk of space-time is not only observable and measurable, it’s an excellent tool for understanding the Universe.

Researchers May Have Solved a Decades-Old Brain Paradox With AI

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.

Can Models of Human Consciousness Enhance AI Capabilities?

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

Researchers uncover link between quantum information theory and particle and condensed matter physics

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.

Algorithms based on deep learning can improve medical image analysis

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).

An AI Chemist Made A Catalyst to Make Oxygen On Mars Using Local Materials

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.

Tiny rotating particles create vorticity in viscous fluids, yielding fascinating new behaviors

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.

/* */