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Category: information science – Page 53

Discover the V-Score: The Secret Weapon in Quantum Problem Solving

Predicting the behavior of many interacting quantum particles is a complex task, but it’s essential for unlocking the potential of quantum computing in real-world applications. A team of researchers, led by EPFL, has developed a new method to compare quantum algorithms and identify the most challenging quantum problems to solve.

Quantum systems, from subatomic particles to complex molecules, hold the key to understanding the workings of the universe. However, modeling these systems quickly becomes overwhelming due to their immense complexity. It’s like trying to predict the behavior of a massive crowd where everyone constantly influences everyone else. When you replace the crowd with quantum particles, you encounter what’s known as the “quantum many-body problem.”

Quantum many-body problems involve predicting the behavior of numerous interacting quantum particles. Solving these problems could lead to major breakthroughs in fields like chemistry and materials science, and even accelerate the development of technologies like quantum computers.

The Singularity Is Coming Soon. Here’s What It May Mean

In 2005, the futurist Ray Kurzweil predicted that by 2045, machines would become smarter than humans. He called this inflection point the “singularity,” and it struck a chord. Kurzweil, who’s been tracking artificial intelligence since 1963, gained a fanatical following, especially in Silicon Valley.

Now comes The Singularity is Nearer: When We Merge with A.I. where Kurzweil steps up the Singularity’s arrival timeline to 2029. “Algorithmic innovations and the emergence of big data have allowed AI to achieve startling breakthroughs sooner than expected,” reports Kurzweil. From winning at games like Jeopardy! and Go to driving automobiles, writing essays, passing bar exams, and diagnosing cancer, chunks of the Singularity are arriving daily, and there’s more good news just ahead.

Very soon, predicts Kurzweil, artificial general intelligence will be able to do anything a human can do, only better. Expect 3D printed clothing and houses by the end of this decade. Look for medical cures that will “add decades to human life spans” just ahead. “These are the most exciting and momentous years in all of history,” Kurzweil noted in an interview with Boston Globe science writer Brian Bergstein.

In a global first, quantum computers crack RSA and AES data encryption

A team of Chinese researchers, led by Wang Chao from Shanghai University, has demonstrated that D-Wave’s quantum annealing computers can crack encryption methods that safeguard sensitive global data.

This breakthrough, published in the Chinese Journal of Computers, emphasizes that quantum machines are closer than expected to threatening widely used cryptographic systems, including RSA and Advanced Encryption Standard (AES).

The research team’s experiments focused on leveraging D-Wave’s quantum technology to solve cryptographic problems. In their paper, titled “Quantum Annealing Public Key Cryptographic Attack Algorithm Based on D-Wave Advantage,” the researchers explained how quantum annealing could transform cryptographic attacks into combinatorial optimization problems, making them more manageable for quantum systems.

Assessing quantum advantage for ground state problems

How do we assess quantum advantage when exact classical solutions are not available?

A quantum advantage is a demonstration of a solution for a problem for which a quantum computer can provide a demonstrable improvement over any classical method and classical resources in terms of accuracy, runtime…

Today, algorithms designed to solve this problem mostly rely on what we call variational methods, which are algorithms guaranteed to output an energy for a target system which cannot be lower than the exact solution — or the deepest valley — up to statistical uncertainties. An ideal quality metric for the ground state problem would not only allow the user to benchmark different methods against the same problem, but also different target problems when tackled by the same method.

So, how can such an absolute metric be defined? And what would be the consequences of finding this absolute accuracy metric?

We construct our accuracy metric from an estimation of the energy and its variance for any specific algorithm used to solve the ground state problem, with additional parameters of the system such as the size and the nature of its interactions. We call this metric “variational-score,” or and show that it is an absolute metric for this benchmark.

AI misinformation detectors can’t save us from tyranny—at least not yet

AI-powered misinformation detectors—artificial intelligence tools that identify false or inaccurate online content—have emerged as a potential intervention for helping internet users understand the veracity of the content they view. However, the algorithms used to create these detectors are experimental and largely untested at the scale necessary to be effective on a social media platform.

Billionaire Drools That “Citizens Will Be on Their Best Behavior” Under Constant AI Surveillance

If it were up to Larry Ellison, the exorbitantly rich cofounder of software outfit Oracle, all of us will soon be smiling for the camera — constantly. Not for a cheery photograph, but to appease our super-invasive, if not totally omnipresent, algorithmic overseers.

As Business Insider reports, the tech centibillionaire glibly predicts that the wonders of AI will bring about a new paradigm of supercharged surveillance, guaranteeing that the proles — excuse us, “citizens” — all behave and stay in line.

“We’re going to have supervision,” Ellison said this week at an Oracle financial analysts meeting, per BI. “Every police officer is going to be supervised at all times, and if there’s a problem, AI will report that problem and report it to the appropriate person.”

Decoding top quarks with precision: Experiment at Large Hadron Collider reveals how pairs of top quarks are produced

The second ATLAS study, presented recently at the 17th International Workshop on Top Quark Physics, broke new ground by providing the first dedicated ATLAS measurement of how often top-quark pairs are produced along with jets originating from charm quarks (c-jets).

ATLAS physicists analyzed events with one or two leptons (electrons and muons), using a custom flavor-tagging algorithm developed specifically for this study to distinguish c-jets from b-jets and other jets. This algorithm was essential because c-jets are even more challenging to identify than b-jets, as they have shorter lifetimes and produce less distinct signatures in the ATLAS detector.

The study found that most theoretical models provided reasonable agreement with the data, though they generally underpredicted the production rates of c-jets. These results, which for the first time separately determined the cross-sections for single and multiple charm-quark production in top-quark-pair events, highlight the need for refined simulations of these processes to improve future measurements.

Compact ‘Gene Scissors’ enable Effective Genome Editing, may offer Future Treatment of High Cholesterol Gene Defect

CRISPR-Cas is used broadly in research and medicine to edit, insert, delete or regulate genes in organisms. TnpB is an ancestor of this well-known “gene scissors” but is much smaller and thus easier to transport into cells.

Using protein engineering and AI algorithms, University of Zurich researchers have now enhanced TnpB capabilities to make DNA editing more efficient and versatile, paving the way for treating a genetic defect for high cholesterol in the future. The work has been published in Nature Methods.

CRISPR-Cas systems, which consist of protein and RNA components, were originally developed as a natural defense mechanism of bacteria to fend off intruding viruses. Over the last decade, re-engineering these so-called “gene scissors” has revolutionized genetic engineering in science and medicine.

Chemistry Nobel Awarded for an AI System That Predicts Protein Structures

All proteins are composed of chains of amino acids, which generally fold up into compact globules with specific shapes. The folding process is governed by interactions between the different amino acids—for example, some of them carry electrical charges—so the sequence determines the structure. Because the structure in turn defines a protein’s function, deducing a protein’s structure is vital for understanding many processes in molecular biology, as well as for identifying drug molecules that might bind to and alter a protein’s activity.

Protein structures have traditionally been determined by experimental methods such as x-ray crystallography and electron microscopy. But researchers have long wished to be able to predict a structure purely from its sequence—in other words, to understand and predict the process of protein folding.

For many years, computational methods such as molecular dynamics simulations struggled with the complexity of that problem. But AlphaFold bypassed the need to simulate the folding process. Instead, the algorithm could be trained to recognize correlations between sequence and structure in known protein structures and then to generalize those relationships to predict unknown structures.

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