Lifeboat Foundation

Researchers at Leipzig University have developed a highly efficient method to investigate systems with long-range interactions that were previously puzzling to experts. These systems can be gases or even solid materials such as magnets whose atoms interact not only with their neighbors but also far beyond.

Professor Wolfhard Janke and his team of researchers use Monte Carlo computer simulations for this purpose. This stochastic process, named after the Monte Carlo casino, generates random system states from which the desired properties of the system can be determined. In this way, Monte Carlo simulations provide deep insights into the physics of phase transitions.

The researchers have developed a new algorithm that can perform these simulations in a matter of days, which would have taken centuries using conventional methods. They have published their new findings in the journal Physical Review X.

We compare the performance of the Quantum Approximate Optimization Algorithm (QAOA) with state-of-the-art classical solvers Gurobi and MQLib to solve the MaxCut problem on 3-regular graphs. We identify the minimum noiseless sampling frequency and depth p required for a quantum device to outperform classical algorithms. There is potential for quantum advantage on hundreds of qubits and moderate depth with a sampling frequency of 10 kHz. We observe, however, that classical heuristic solvers are capable of producing high-quality approximate solutions in linear time complexity. In order to match this quality for large graph sizes N, a quantum device must support depth p > 11. Additionally, multi-shot QAOA is not efficient on large graphs, indicating that QAOA p ≤ 11 does not scale with N. These results limit achieving quantum advantage for QAOA MaxCut on 3-regular graphs.

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Mentioned Videos:
AI designing Computer Chips: https://youtu.be/NeHgMaIkPuY
Deepmind AI made a Breakthrough in Math: https://youtu.be/DU6WINoehrg.

Continue reading “DeepMind’s New AI made a Breakthrough in Computer Science!” »

Measurements conducted over an unprecedented span of conditions uncover universal behavior, but not the kind that theorists expected.

Turbulence is a mesmerizing, chaotic state of fluid motion. It occurs in natural and artificial settings whenever the Reynolds number (quantifying the relative size of inertial to viscous forces in the flow) is large. Through nonlinear coupling, kinetic energy cascades from large scales to ever smaller scales (Fig. 1) until it is dissipated by viscous effects. The fluctuations excited during this process play a crucial role in a diverse range of problems, including planetesimal formation [1], rain initiation in clouds [2], and heat transport within oceans [3]. Remarkably, a new experimental study by Christian Küchler of the Max Planck Institute for Dynamics and Self-Organization in Germany and co-workers provides compelling evidence that current theoretical models for how the fluctuations are distributed across the scales are missing some important ingredients [4].

Turbulent flows are complex. Quantitative predictions of their properties that are derived directly from the Navier-Stokes equation, without ad hoc assumptions, are accordingly scarce. Most theoretical approaches have perforce been phenomenological, the most famous being Andrey Kolmogorov’s groundbreaking 1941 theory, nicknamed K41 [5]. This mean-field theory assumes that the multiscale properties of the turbulent fluctuations are governed by the average cascade of kinetic energy passing through the scales and by the fluid viscosity. In K41 Kolmogorov went on to propose the existence of an inertial range, which corresponds to an intermediate range of scales over which viscous forces could be ignored relative to inertial forces and where the details of the large-scale forcing are unimportant.

I hope this isn’t been posted before especially by me. I do have a bit of pre dementia but it’s not too bad. It’s from my TBI but they’re working on weeding out bias from AI and making it so it’s not bad for us or to us.

Thought-provoking TED Talk on how AI can unintentionally reinforce societal prejudices, perpetuate discrimination, and amplify toxic behaviors online. This talk is a call to action for individuals, tech companies, and policymakers alike. By addressing AI bias and toxicity head-on, we can pave the way for a future where AI systems are truly unbiased, fostering inclusivity and equality for all.

Continue reading “AI Future — unbiased, fostering inclusivity & equality | Priya Bhasin | TEDxBoston” »

A newly described type of chemistry in fungi is both surprisingly common and likely to involve highly reactive enzymes, two traits that make the genes involved useful signposts pointing to a potential treasure trove of biological compounds with medical and chemical applications.

It was also nearly invisible to scientists until now.

In the last 15 years, the hunt for molecules from living organisms—many with promise as drugs, antimicrobial agents, chemical catalysts and even food additives—has relied on computer algorithms trained to search the DNA of bacteria, fungi and plants for genes that produce enzymes known to drive biological processes that result in interesting compounds.

Memories can be as tricky to hold onto for machines as they can be for humans. To help understand why artificial agents develop holes in their own cognitive processes, electrical engineers at The Ohio State University have analyzed how much a process called “continual learning” impacts their overall performance.

Continual learning is when a computer is trained to continuously learn a sequence of tasks, using its accumulated knowledge from old tasks to better learn new tasks.

Yet one major hurdle scientists still need to overcome to achieve such heights is learning how to circumvent the machine learning equivalent of memory loss—a process which in AI agents is known as “catastrophic forgetting.” As artificial neural networks are trained on one new task after another, they tend to lose the information gained from those previous tasks, an issue that could become problematic as society comes to rely on AI systems more and more, said Ness Shroff, an Ohio Eminent Scholar and professor of computer science and engineering at The Ohio State University.

Quantum algorithms can find their way out of mazes exponentially faster than classical ones, at the cost of forgetting the path they took. A new result suggests that the trade-off may be inevitable.

The digital devices that we rely on so heavily in our day-to-day and professional lives today—smartphones, tablets, laptops, fitness trackers, etc.—use traditional computational technology. Traditional computers rely on a series of mathematical equations that use electrical impulses to encode information in a binary system of 1s and 0s. This information is transmitted through quantitative measurements called “bits.”

Unlike traditional computing, quantum computing relies on the principles of quantum theory, which address principles of matter and energy on an atomic and subatomic scale. With quantum computing, equations are no longer limited to 1s and 0s, but instead can transmit information in which particles exist in both states, the 1 and the 0, at the same time.

Quantum computing measures electrons or photons. These subatomic particles are known as quantum bits, or ” qubits.” The more qubits are used in a computational exercise, the more exponentially powerful the scope of the computation can be. Quantum computing has the potential to solve equations in a matter of minutes that would take traditional computers tens of thousands of years to work out.