Sun, Sep 11 at 12 PM CDT.
This is an invitation to the Annual General Meeting of the Cryonics Institute & the Immortalist Society.
The Cryonics Institute’s Annual General Meeting (AGM) will be held on Sunday, September 11th 2022 from 3:00pm to 6:30pm at the Infinity Hall & Sidebar 16,650 E. 14 Mile Rd, Fraser, MI 48,026 (USA). For more information visit www.infinityhallsidebar.com
Or call (586) 879‑6157.
Monkeys, mice, and sea sponges: these creatures are new to science, but they’ve been living right under our noses. Here are 7 spectacular tales of discovery.
Some frequent travelers noticed they got sick less often during the federal mask mandate for air travel. Despite the end of the mandate, some fliers say they will continue to wear masks.
The end, when it came, came suddenly. An asteroid or comet 10 kilometres across slammed into the Gulf of Mexico, gouging a 180-kilometre crater and unleashing firestorms, eruptions and mega-tsunamis across the globe. The debris blocked out the Sun for years. The dinosaurs – and the other 75 per cent of life that went down with them – didn’t stand a chance.
The story of the demise of the dinosaurs 65 million years ago is well known. But that of their origin is less so. Dinosaurs were the dominant animals on land for at least 135 million years, the longest reign of any group. Had the impact not happened, they might still be in control. Where did these magnificent beasts come from?
Upgraded Dragonfly will study how dark matter shapes diffuse galaxies and faint tendrils of gas.
April, 2022
Sean Carroll (Caltech and Santa Fe Institute)
https://simons.berkeley.edu/events/causality-program-externa…-institute.
Causality.
Abstract:
A macroscopic arrow of time can be derived from reversible and time-symmetric fundamental laws if we assume an appropriate notion of coarse-graining and a Past Hypothesis of low entropy at early times. It is an ongoing project to show how familiar aspects of time’s arrow, such as the fact that causes precede effects, can be derived from such a formalism. I will argue that the causal arrow arises naturally when we describe macroscopic systems in terms of a causal network, and make some suggestions about how to fit prediction and memory into this framework.
Sean Carroll is a Research Professor of theoretical physics at the California Institute of Technology, and Fractal Faculty at the Santa Fe Institute. He received his Ph.D. in 1993 from Harvard University. His research focuses on foundational questions in quantum mechanics, spacetime, cosmology, emergence, entropy, and complexity, occasionally touching on issues of dark matter, dark energy, symmetry, and the origin of the universe. Carroll is the author of Something Deeply Hidden, The Big Picture, The Particle at the End of the Universe, From Eternity to Here, and Spacetime and Geometry: An Introduction to General Relativity. He has been awarded prizes and fellowships by the National Science Foundation, NASA, the Sloan Foundation, the Packard Foundation, the American Physical Society, the American Institute of Physics, the American Association for the Advancement of Science, the Freedom From Religion Foundation, the Royal Society of London, and the Guggenheim Foundation. Carroll has appeared on TV shows such as The Colbert Report, PBS’s NOVA, and Through the Wormhole with Morgan Freeman, and frequently serves as a science consultant for film and television. He is host of the weekly Mindscape podcast. He lives in Los Angeles with his wife, writer Jennifer Ouellette.
Discovering a system’s causal relationships and structure is a crucial yet challenging problem in scientific disciplines ranging from medicine and biology to economics. While researchers typically adopt the graphical formalism of causal Bayesian networks (CBNs) to induce a graph structure that best describes these relationships, such unsupervised score-based approaches can quickly lead to prohibitively heavy computation burdens.
A research team from DeepMind, Mila – University of Montreal and Google Brain challenges the conventional causal induction approach in their new paper Learning to Induce Causal Structure, proposing a neural network architecture that learns the graph structure of observational and/or interventional data via supervised training on synthetic graphs. The team’s proposed Causal Structure Induction via Attention (CSIvA) method effectively makes causal induction a black-box problem and generalizes favourably to new synthetic and naturalistic graphs.
The team summarizes their main contributions as:
And an AI could generate a picture of a person from scratch if it wanted or needed to. its only a matter of time before someone puts it all together. 1. AI writes a script. 2. AI generates pictures of a cast (face/&body). 3. AI animates pictures of the cast into scenes. 4. it cant create voices from scratch yet, but 10 second audio sample of a voice is enough for it to make voices say anything; AI voices all the dialog. And, viola, you ve reduced TV and movie production costs by 99.99%. Will take place by 2030.
Google’s PHORUM AI shows how impressive 3D avatars can be created just from a single photo.
Until now, however, such models have relied on complex automatic scanning by a multi-camera system, manual creation by artists, or a combination of both. Even the best camera systems still produce artifacts that must be cleaned up manually.
As the early universe cooled shortly after the Big Bang, bubbles formed in its hot plasma, triggering gravitational waves that could be detectable even today, a new study suggests.
For some time, physicists have speculated that a phase transition took place in the early universe shortly after the Big Bang. Phase transition is a change of form and properties of matter that usually accompanies temperature changes such as the evaporation of water into vapor or the melting of metal. In the young and fast expanding universe, something similar likely took place as the plasma, which was filling the space at that time, cooled down.
