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Mar 16, 2023

Quantum Light Could Probe Chemical Reactions in Real Time

Posted by in categories: chemistry, mathematics, particle physics, quantum physics

For their new study, the researchers aimed to understand how quantum correlations inside a source material, be it a gas or a mineral, would impact the quantum properties of the light bursts coming out, if at all. “High harmonic generation is a very important area. And still, until recently, it was described by a classical picture of light,” Kaminer says.

In quantum mechanics, figuring out what’s going on with more than a few particles at the same time is notoriously difficult. Kaminer and Alexey Gorlach, a graduate student in his lab, used their COVID-imposed isolation to try to make progress on a fully quantum description of light emitted in high harmonics. “It’s really crazy; Alexey built a super complex mathematical description on a scale that we’ve never had before,” Kaminer says.

Next, to fully incorporate the quantum properties of the material used to generate this light, Kaminer and Gorlach teamed up with Andrea Pizzi, then a graduate student at the University of Cambridge and now a postdoctoral fellow at Harvard University.

Mar 16, 2023

First Stars In the Universe Were Ultra Massive Existing Only 1000s of Years

Posted by in categories: bitcoin, cryptocurrencies

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Hello and welcome! My name is Anton and in this video, we will talk about theories potentially explaining early stars and what they were like.
Links:
https://arxiv.org/abs/2301.

Continue reading “First Stars In the Universe Were Ultra Massive Existing Only 1000s of Years” »

Mar 16, 2023

How Big Is a Proton? Neutrinos Weigh In

Posted by in category: particle physics

The team’s measurement of the proton’s radius was 0.73 femtometer, even smaller than the 0.84-femtometer electric charge radius. In either case, it is almost 10,000 times smaller than a hydrogen atom.

To be clear, this apparent 13 percent shrinkage is not a blow to the electric charge radius measurements and not as shocking as it may seem. The two measurements are complementary and work together to offer a big picture view of the little proton. Because they measure different distributions of matter, the discrepancy does not challenge our understanding of the proton the same way its previous 4 percent shrinkage did. Instead it adds to that understanding.

“The thing that makes this measurement really interesting is not whether or not it agrees with the electron measurements of the electromagnetic proton radius but the fact that it didn’t have to agree at all,” says Deborah Harris, co-spokesperson for the MINERvA experiment. This is because the way neutrinos interact with up quarks versus down quarks is very different from how quarks interact with electrons. Instead of an electromagnetic interaction, neutrinos interact via a different force called the weak force. (But don’t let its name fool you—the weak force is quite strong across subatomic distances!)

Mar 16, 2023

Faint gravitational waves may be from primordial fractures time

Posted by in categories: particle physics, quantum physics

With each of these splittings, the universe completely remolded itself. New particles arose to replace ones that could exist only in extreme conditions previously. The fundamental quantum fields of space-time that dictate how particles and forces interact with each other reconfigured themselves. We do not know how smoothly or roughly these phase transitions took place, but it’s perfectly possible that with each splitting, the universe settled into multiple identities at once.

This fracturing isn’t as exotic as it sounds. It happens with all kinds of phase transitions, like water turning into ice. Different patches of water can form ice molecules with different orientations. No matter what, all the water turns into ice, but different domains can have differing molecular arrangements. Where those domains meet walls, or imperfections, fracturing will appear.

Physicists are especially interested in the so-called GUT phase transition of our universe. GUT is short for “grand unified theory,” a hypothetical model of physics that merges the strong nuclear force with electromagnetism and the weak nuclear force. These theories are just beyond the reach of current experiments, so physicists and astronomers turn to the conditions of the early universe to study this important transition.

Mar 16, 2023

Sentience: The Invention of Consciousness

Posted by in categories: innovation, neuroscience

Author Nick Humphrey shares 5 key insights from his new book, Sentience: The Invention of Consciousness.

Mar 16, 2023

A Growing Number of Scientists Are Convinced the Future Influences the Past

Posted by in categories: futurism, physics

“Our instincts of time and causation are our deepest, strongest instincts that physicists and philosophers—and humans—are loath to give up,” said one scientist.

Mar 16, 2023

The Reality Of Deep Sea Mining Is Getting Closer As Are The Consequences

Posted by in category: futurism

A two-week meeting starting in Kingston, Jamaica, today could lead to the beginning of deep-sea mining of the ocean floor this year.


A meeting of 167 nations of the International Seabed Authority in Jamaica this week plans to finalize regulating ocean seafloor mining.

Mar 16, 2023

Substitution or Silent or Neutral Mutations

Posted by in category: futurism

This video explains substitution or silent or neutral mutations.

Thank You For Watching.

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Mar 16, 2023

AI Image Generation Using DALL-E 2 Has Promising Future in Radiology

Posted by in categories: biotech/medical, health, internet, robotics/AI

Summary: Text-to-image generation deep learning models like OpenAI’s DALL-E 2 can be a promising new tool for image augmentation, generation, and manipulation in a healthcare setting.

Source: JMIR Publications

A new paper published in the Journal of Medical Internet Research describes how generative models such as DALL-E 2, a novel deep learning model for text-to-image generation, could represent a promising future tool for image generation, augmentation, and manipulation in health care.

Mar 16, 2023

Meet Petals: An Open-Source Artificial Intelligence (AI) System That Can Run 100B+ Language Models At Home Bit-Torrent Style

Posted by in category: robotics/AI

The NLP community has recently discovered that pretrained language models may accomplish various real-world activities with the help of minor adjustments or direct assistance. Additionally, performance usually becomes better as the size grows. Modern language models often include hundreds of billions of parameters, continuing this trend. Several research groups published pretrained LLMs with more than 100B parameters. The BigScience project most recently made BLOOM available, a 176 billion parameter model that supports 46 natural and 13 computer languages. The public availability of 100B+ parameter models makes them more accessible, yet due to memory and computational expenses, most academics and practitioners still find it challenging to use them. For inference, OPT-175B and BLOOM-176B require more than 350GB of accelerator RAM and even more for finetuning.

As a result, running these LLMs typically requires several powerful GPUs or multi-node clusters. These two alternatives are relatively inexpensive, restricting the potential study topics and language model applications. By “offloading” model parameters to slower but more affordable memory and executing them on the accelerator layer by layer, several recent efforts seek to democratize LLMs. By loading parameters from RAM just in time for each forward pass, this technique enables executing LLMs with a single low-end accelerator. Although offloading has high latency, it can process several tokens in parallel. For instance, they are producing one token with BLOOM-176B requires at least 5.5 seconds for the fastest RAM offloading system and 22 seconds for the quickest SSD offloading arrangement.

Additionally, many machines lack sufficient RAM to unload 175B parameters. LLMs may be made more widely available through public inference APIs, where one party hosts the model and allows others to query it online. This is a fairly user-friendly choice because the API owner handles most of the engineering effort. However, APIs are frequently too rigid to be used in research since they cannot alter the model’s control structure or have access to its internal states. Additionally, the cost of some research initiatives may be exorbitant, given the current API price. In this study, they investigate a different approach motivated by widespread crowdsourcing training of neural networks from scratch.