Lifeboat Foundation

Throughout history, humans have gazed at the sky, contemplating the celestial lights, including the sun, the moon, and beyond. In those ancient moments, an insatiable curiosity ignited within them, urging them to seek answers about the origins of the cosmos. Over time, this burning curiosity has been passed down, compelling generations to develop theories in pursuit of one timeless question: Where did it all come from?

One of the most complete and widely accepted theories in this regard is the Big Bang Theory. The Big Bang is a scientific theory that proposes that the birth and development of the universe originated from a point in space-time called the singularity. Think of this in a way that all the matter and energy of the universe were trapped in an inconceivably small point of high density and high temperature (Williams & Today, n.d.). It is theorized to be a colossal release of energy that initiated the rapid expansion of the universe over 13.7 billion years that led to the creation of galaxies, stars, planetary systems and eventually humankind. What happened that led to the sudden expansion? This question continues to puzzle cosmologists, as the answer remains unknown to this day (What Is the Big Bang Theory? n.d.).

In 1915, while developing his General Theory of Relativity, Albert Einstein faced a challenge. If gravity were to solely attract all objects, the universe would ultimately collapse under its overwhelming force. However, observations indicated that the universe was not collapsing. To address this issue, Einstein introduced a cosmological constant into his equations. This constant acted as a counterforce to gravity and proposed a static model of the universe. Little did Einstein know that an astronomer named Edwin Hubble would soon contradict his proposed static model of the universe. Working at Mount Wilson Observatory in California, Hubble made a noteworthy observation in the late 1920s. He noticed a peculiar phenomenon known as redshift, where light emitted by celestial bodies moved toward the red end of the spectrum, indicating that they were moving away from us (Vogel, 2021).

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.

👉 Invest in Blue-chip Art by signing up for Masterworks: https://www.masterworks.art/anastasi.
Purchase shares in great masterpieces from Pablo Picasso, Banksy, Andy Warhol, and more.
See important Masterworks disclosures: https://www.masterworks.com/about/disclaimer?utm_source=anas…subscriber.

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.