Lifeboat Foundation

Physicists propose an new exponential growth mechanism for dark matter creation.

Astronomers at The University of Texas at Austin’s McDonald Observatory have discovered an unusually massive black hole at the heart of one of the Milky Way ’s dwarf satellite galaxies, called Leo I. Almost as massive as the black hole in our own galaxy, the finding could redefine our understanding of how all galaxies — the building blocks of the universe — evolve. The work is published in a recent issue of The Astrophysical Journal.

The team decided to study Leo I because of its peculiarity. Unlike most dwarf galaxies orbiting the Milky Way, Leo I does not contain much dark matter. Researchers measured Leo I’s dark matter profile — that is, how the density of dark matter changes from the outer edges of the galaxy all the way into its center. They did this by measuring its gravitational pull on the stars: The faster the stars are moving, the more matter there is enclosed in their orbits. In particular, the team wanted to know whether dark matter density increases toward the galaxy’s center. They also wanted to know whether their profile measurement would match previous ones made using older telescope data combined with computer models.

Let me back up a moment. I recently concurred with megapundit Steven Pinker that over the last two centuries we have achieved material, moral and intellectual progress, which should give us hope that we can achieve still more. I expected, and have gotten, pushback. Pessimists argue that our progress will prove to be ephemeral; that we will inevitably succumb to our own nastiness and stupidity and destroy ourselves.

Maybe, maybe not. Just for the sake of argument, let’s say that within the next century or two we solve our biggest problems, including tyranny, injustice, poverty, pandemics, climate change and war. Let’s say we create a world in which we can do pretty much anything we choose. Many will pursue pleasure, finding ever more exciting ways to enjoy themselves. Others may seek spiritual enlightenment or devote themselves to artistic expression.

No matter what our descendants choose to do, some will surely keep investigating the universe and everything in it, including us. How long can the quest for knowledge continue? Not long, I argued 25 years ago this month in The End of Science, which contends that particle physics, cosmology, neuroscience and other fields are bumping into fundamental limits. I still think I’m right, but I could be wrong. Below I describe the views of three physicists—Freeman Dyson, Roger Penrose and David Deutsch—who hold that knowledge seeking can continue for a long, long time, and possibly forever, even in the face of the heat death of the universe.

Because leviathan black holes would never fit in a lab, Jeff Steinhauer and his research team created a mini one right here on Earth.

When something rips physics apart, you cross over into the quantum realm, a place inhabited by black holes, wormholes and other things that have been the stars of multiple sci-fi movies. What lives in the quantum realm either hasn’t been proven to exist (yet) or behaves strangely if it does exist.

Black holes often venture into that realm. With these collapsed stars — at least most of them are — being impossible to fly a spacecraft into (unless you never want to see it again), one physicist decided that the best way to get up close to them was under a literal microscope. Jeff Steinhauer wanted to know whether black holes radiate particles like the late Stephen Hawking theorized they would. Because one of these leviathans would never fit in a lab, he and his research team created one right here on Earth.

Continue reading “Black hole conjured up in a lab does the same weird things Stephen Hawking thought it would do” »

Blackholes are a breakdown in the equations of spacetime. This means both space and time no longer behave the way we would expect of them.
Today we explore the breakdown in time around blackholes and what it means to interact with the event horizon, or the place where time appears to stand still.

Further Reading/Consumption:

Continue reading “Why Time ‘Stops’ in a Black Hole” »

Go to https://NordVPN.com/sabine to get a 2-year plan plus 4 additional months with a huge discount!

At 2 mins 26 seconds when I say “Peter” I meant “Paul”. Sorry!

Continue reading “Physicist Despairs over Vacuum Energy” »

Using the European Southern Observatory’s Very Large Telescope (ESO

Created in 1962, the European Southern Observatory (ESO), is a 16-nation intergovernmental research organization for ground-based astronomy. Its formal name is the European Organisation for Astronomical Research in the Southern Hemisphere.

Astronomers may have seen the light from two black holes smashing into one another for the first time ever.

Black holes are completely dark and therefore invisible to light-detecting telescopes. So far, the only way astronomers have been able to “observe” black holes colliding is by detecting the resulting gravitational waves.

Deep Learning systems can achieve remarkable, even super-human performance through supervised learning on large, labeled datasets. However, there are two problems: First, collecting ever more labeled data is expensive in both time and money. Second, these deep neural networks will be high performers on their task, but cannot easily generalize to other, related tasks, or they need large amounts of data to do so. In this blog post, Yann LeCun and Ishan Misra of Facebook AI Research (FAIR) describe the current state of Self-Supervised Learning (SSL) and argue that it is the next step in the development of AI that uses fewer labels and can transfer knowledge faster than current systems. They suggest as a promising direction to build non-contrastive latent-variable predictive models, like VAEs, but ones that also provide high-quality latent representations for downstream tasks.

OUTLINE:
0:00 — Intro & Overview.
1:15 — Supervised Learning, Self-Supervised Learning, and Common Sense.
7:35 — Predicting Hidden Parts from Observed Parts.
17:50 — Self-Supervised Learning for Language vs Vision.
26:50 — Energy-Based Models.
30:15 — Joint-Embedding Models.
35:45 — Contrastive Methods.
43:45 — Latent-Variable Predictive Models and GANs.
55:00 — Summary & Conclusion.

Continue reading “Yann LeCun — Self-Supervised Learning: The Dark Matter of Intelligence (FAIR Blog Post Explained)” »