Can we address mysteries of quantum mechanics by supposing that properties of objects long considered to have an independent existence are actually determined solely in relation to other objects or observers?
This program is part of the Big Ideas series, supported by the John Templeton Foundation.
Participants: Carlo Rovelli.
Moderator: Brian Greene.
00:00 — Introduction. 03:06 — Beginning of the Main discussion. 03:50 — How does Carlo Rovelli view the Quantum Measurement problem and Many Worlds theory? 12:47 — Relational quantum mechanics. 17:27 — Does this approach apply to relativistic quantum mechanics. 24:01 — What is needed to fully understand Quantum Mechanics? 28:30 — Summary.
Dive into the deepest quantum mystery: how do we transition from a haze of possibilities to the concrete reality we experience? Does the answer require a profusion of universes, each shaped by different quantum outcomes?
This program is part of the Big Ideas series, supported by the John Templeton Foundation.
Participants: Sean Carroll.
Moderator: Brian Greene.
00:00 — Introduction. 03:38 — Sean Carroll Introduction. 04:09 — The Quantum Measurement Problem. 08:33 — The GRW Theory. 11:18 — What would be predicted with the Schrödinger equation? 15:10 — Many Worlds Theory. 17:42 — What are the implications of the many worlds theory? 22:37 — Quantum Entanglement. 29:05 — What does the future of Quantum Mechanics look like? 31:26 — Embracing the Many Worlds Concept.
Inside every plant, animal and human cell are billions of molecular machines. They’re made up of proteins, DNA and other molecules, but no single piece works on its own. Only by seeing how they interact together, across millions of types of combinations, can we start to truly understand life’s processes.
In a paper published in Nature, we introduce AlphaFold 3, a revolutionary model that can predict the structure and interactions of all life’s molecules with unprecedented accuracy. For the interactions of proteins with other molecule types we see at least a 50% improvement compared with existing prediction methods, and for some important categories of interaction we have doubled prediction accuracy.
We hope AlphaFold 3 will help transform our understanding of the biological world and drug discovery. Scientists can access the majority of its capabilities, for free, through our newly launched AlphaFold Server, an easy-to-use research tool. To build on AlphaFold 3’s potential for drug design, Isomorphic Labs is already collaborating with pharmaceutical companies to apply it to real-world drug design challenges and, ultimately, develop new life-changing treatments for patients.
Astronomy Magazine — Project Lyra is the cover feature!
A big thank you to Maciej Rebisz for the images and the entire Project Lyra team for the research work!
Project Lyra develops concepts for reaching interstellar objects such as 1I / ‘Oumuamua and 2I / Borisov with a spacecraft, based on near-term technologies. But what is an interstellar object?
On October 19th 2017, the University of Hawaii’s Pan-STARRS 1 telescope on Haleakala discovered a fast-moving object near the Earth, initially named A/2017 U1. It is now designated as 1I/’Oumuamua. This object was found to be not bound to the solar system. It has a velocity at infinity of ~26 km/s and an incoming radiant (direction of motion) near the solar apex in the constellation Lyra. Due to the non-observation of a tail in the proximity of the Sun, the object does not seem to be a comet but an asteroid. More recent observations from the Palomar Observatory indicate that the object is reddish, similar to Kuiper belt objects. This is a sign of space weathering.
When will such an object visit us again? End of 2019, a second interstellar object, 2I/Borisov was discovered, which is a comet. As 1I/‘Oumuamua and 2I/Borisov are the nearest macroscopic samples of interstellar material, the scientific returns from sampling the object are hard to overstate. Detailed study of interstellar materials at interstellar distances are likely decades away, even if Breakthrough Initiatives’ Project Starshot, for example, is vigorously pursued. Hence, an interesting question is if there is a way to exploit this unique opportunity by sending a spacecraft to 1I/’Oumuamua to make observations at close range.
Elon Musk’s unique management style at Tesla, which involves small, highly technical teams, removing underperforming employees, and creating challenging deadlines, has been crucial to the company’s success Questions to inspire discussion Who is Andrej Karpathy? —Andrej Karpathy is a highly respected computer scientist who served as the Director of AI and Autopilot at Tesla and co-founded OpenAI.
The find, simulated with computer modeling, might explain what happens to liquid water across the universe.
“Water is really important for life,” said Eryn Cangi, co-author and a research scientist at the Laboratory for Atmospheric and Space Physics, in a press release. “We need to understand the conditions that support liquid water in the universe, and that may have produced the very dry state of Venus today.”
At one point, Venus might have hosted seas like Earth. So, what happened? The study’s scientists suspect that Venus underwent a powerful greenhouse event that raised temperatures to 900 degrees Fahrenheit. After this happened, all the planet’s water evaporated, leaving some droplets behind. Even the few drops that were left over might have vanished because of an ion, HCO+, in the planet’s atmosphere.
Google DeepMind’s newly launched AlphaFold Server is the most accurate tool in the world for predicting how proteins interact with other molecules throughout the cell. It is a free platform that scientists around the world can use for non-commercial research. With just a few clicks, biologists can harness the power of AlphaFold 3 to model structures composed of proteins, DNA, RNA and a selection of ligands, ions and chemical modifications.
AlphaFold Server will help scientists make novel hypotheses to test in the lab, speeding up workflows and enabling further innovation. Our platform gives researchers an accessible way to generate predictions, regardless of their access to computational resources or their expertise in machine learning.
Experimental protein-structure prediction can take about the length of a PhD and cost hundreds of thousands of dollars. Our previous model, AlphaFold 2, has been used to predict hundreds of millions of structures, which would have taken hundreds of millions of researcher-years at the current rate of experimental structural biology.
AlphaFold 3 model is a Google DeepMind and Isomorphic Labs collaboration.
