Lifeboat Foundation

By analyzing the data from ESA’s Gaia satellite, astronomers from the Shanghai Astronomical Observatory (SHAO) in China have detected 101 new open clusters in the Milky Way galaxy. The discovery was presented in a paper published December 21 on the arXiv pre-print repository.

Open clusters (OCs), formed from the same giant molecular cloud, are groups of stars loosely gravitationally bound to each other. So far, more than 1,000 of them have been discovered in the Milky Way, and scientists are still looking for more, hoping to find a variety of these stellar groupings. Studying them in detail could be crucial for improving our understanding of the formation and evolution of our galaxy.

Now, a team of astronomers led by SHAO’s Qin Songmei reports the finding of 101 new OCs in the solar neighborhood. The discovery is a result of utilizing clustering algorithms pyUPMASK and HDSBSCAN on the data from Gaia’s Data Release 3 (DR3).

Anatomical decision-making by cellular collectives: Bioelectrical pattern memories, regeneration, and synthetic living organisms.

A key question for basic biology and regenerative medicine concerns the way in which evolution exploits physics toward adaptive form and function. While genomes specify the molecular hardware of cells, what algorithms enable cellular collectives to reliably build specific, complex, target morphologies? Our lab studies the way in which all cells, not just neurons, communicate as electrical networks that enable scaling of single-cell properties into collective intelligences that solve problems in anatomical feature space. By learning to read, interpret, and write bioelectrical information in vivo, we have identified some novel controls of growth and form that enable incredible plasticity and robustness in anatomical homeostasis. In this talk, I will describe the fundamental knowledge gaps with respect to anatomical plasticity and pattern control beyond emergence, and discuss our efforts to understand large-scale morphological control circuits. I will show examples in embryogenesis, regeneration, cancer, and synthetic living machines. I will also discuss the implications of this work for not only regenerative medicine, but also for fundamental understanding of the origin of bodyplans and the relationship between genomes and functional anatomy.

https://www.youtube.com/watch?v=03IE2f8giBw

BlackRock’s Kevin Franklin explains how investors get comfortable with applying these tools to money management.

For all Morningstar videos: http://www.morningstar.com/articles/archive/467/us-videos.html

Researchers have finally succeeded in building a long-sought nanoparticle structure, opening the door to new materials with special properties.

Alex Travesset does not have a sparkling research lab stocked with the most cutting-edge instruments for probing new nanomaterials and measuring their unique properties.

Instead of using traditional laboratory instruments, Alex Travesset, a professor of physics and astronomy at Iowa State University and an affiliate of the U.S. Department of Energy’s Ames National Laboratory, relies on computer models, equations, and figures to understand the behavior of new nanomaterials.

In the midst of the Anti AI Art movement and the ever evolving complexity of the algorithms they are rallying against, this video essay discusses current flaws and future potential of AI Translation technology within Retro Game Emulation. Through rigorous testing of 3 games that never got localizations or fan translations (Tokimeki Memorial 2, Sakura Wars 2 & Boku No Natsuyasami 2), we will see how well Retroarch and ZTranslate’s AI Translator works for the average player. We will also discuss the ways in which this technology could one day be used in more formal localisations by professional teams, and wel will come to understand the nuances of the AI debate.

#AI #FanTranslation #Emulation.

Continue reading “Is AI Translation the Future of Video Games?” »

In particle physics, particles are constantly interacting and interfering with all the other kinds of particles, but the strength of those interactions depend on the particle masses. So, when we try to evaluate anything involving the Higgs boson – like, say, its ability to maintain the separation between the electromagnetic and weak nuclear forces – we also need to pay attention to how the other particles will interfere with that effort. And since the top quark is handily the biggest of the bunch (the next largest, the bottom quark, weighs a mere 5 GeV) it’s essentially the only other particle we need to care about.

When physicists first calculated the stability of the universe, as determined by the Higgs boson’s ability to maintain the separation of the electroweak force, they didn’t know the mass of either the Higgs itself or the top quark. Now we do: The top quark weighs around 175 GeV, and the Higgs around 125 GeV.

Plugging those two numbers into the stability equations reveals that the universe is… metastable. This is different than stable, which would mean that there’s no chance of the universe splitting apart instantly, but also different than unstable, which would mean it already happened.

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers.
❤️ Their mentioned post is available here: http://wandb.me/RLHF-OpenAI

Try #ChatGPT!
https://chat.openai.com/
https://openai.com/blog/chatgpt/

Continue reading “OpenAI ChatGPT: The Future Is Here!” »

PROVIDENCE, R.I. [Brown University] — When it comes to predicting disasters brought on by extreme events (think earthquakes, pandemics or “rogue waves” that could destroy coastal structures), computational modeling faces an almost insurmountable challenge: Statistically speaking, these events are so rare that there’s just not enough data on them to use predictive models to accurately forecast when they’ll happen next.

But a team of researchers from Brown University and Massachusetts Institute of Technology say it doesn’t have to be that way.

In a new study in Nature Computational Science, the scientists describe how they combined statistical algorithms — which need less data to make accurate, efficient predictions — with a powerful machine learning technique developed at Brown and trained it to predict scenarios, probabilities and sometimes even the timeline of rare events despite the lack of historical record on them.

The term AI Art refers to artwork created by computers and algorithms. AI Art is not theft as it does not involve taking or copying someone else’s work without permission. AI Art is an entirely new form of creativity that involves the use of artificial intelligence to create unique and original works of art.

▼ Link(s) From Today’s Video:

Continue reading “AI Art is NOT Theft” »