Lifeboat Foundation

Cancer cells send out nanotubes to suck mitochondria from immune cells, finds a November 18 study in Nature Nanotechnology. The pilfered organelles allow the cancer cells to replenish their power while weakening T cells—a finding that could lead to new avenues for assailing tumors.

“It’s surprising that the transfer of mitochondria happened between different cell types, intriguingly between immune cells and cancer cells,” writes cancer biologist Ming Tan of China Medical University in Taiwan, who was not involved in this study, in an email to The Scientist. While researchers have observed mitochondrial transfer between cells before, most cases occurred between two cells of the same type. “Moreover, the mitochondrial transfer appears to have a significant impact on tumor cells escaping from immune surveillance,” Tan adds. “This is exciting because [of] its potential therapeutic implications.”

See “Nanotubes Link Immune Cells”.

https://youtube.com/watch?v=4BswkMC9Yl8

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The ability to 3D print integrated circuits and other semiconductor devices directly into PCBs allows low-volume fabrication of highly specialized devices.

Pfizer CEO Albert Bourla on Monday said a vaccine that targets the omicron variant of Covid will be ready in March, and the company’s already begun manufacturing the doses.

“This vaccine will be ready in March,” Bourla told CNBC’s “Squawk Box.” “We [are] already starting manufacturing some of these quantities at risk.”

Bourla said the vaccine will also target the other variants that are circulating. He said it is still not clear whether or not an omicron vaccine is needed or how it would be used, but Pfizer will have some doses ready since some countries want it ready as soon as possible.

BERKELEY, Calif. 0, Jan. 20, 2022 — Atom Computing, the creators of the first quantum computer made of nuclear-spin qubits from optically-trapped neutral atoms, today announced closure of a $60M Series B round. Third Point Ventures led the round, followed by Primer Movers Lab and insiders including Innovation Endeavors, Venrock and Prelude Ventures. Following the completion of their first 100-qubit quantum computing system with world-record 40 second coherence times, Atom Computing will use this new investment to build their second-generation quantum computing systems and commercialize the technology.

“Atom Computing designed and built our first-generation machine, Phoenix 0, in less than two years and our team was the fastest to deliver a 100-qubit system,” said Rob Hays 0, CEO and President, Atom Computing. “We gained valuable learnings from the system and have proven the technology. The investment announced today accelerates the commercialization opportunities and we look forward to bringing this to market.”

With this new level of investment, the company will turn its focus to developing much larger systems that are required to run commercial use-cases with paradigm-shifting compute performance.

Mock seemed pleased with the outcome. “You could look at this and say, ‘O.K., the A.I. got five, our human got zero,’” he told viewers. “From the fighter-pilot world, we trust what works, and what we saw was that in this limited area, this specific scenario, we’ve got A.I. that works.” (A YouTube video of the trials has since garnered half a million views.)

Brett Darcey, who runs Heron, told me that the company has used Falco to fly drones, completing seventy-four flights with zero crashes. But it’s still unclear how the technology will react to the infinite possibilities of real-world conditions. The human mind processes more slowly than a computer, but it has the cognitive flexibility to adapt to unimagined circumstances; artificial intelligence, so far, does not. Anna Skinner, a human-factors psychologist, and another science adviser to the ACE program, told me, “Humans are able to draw on their experience and take reasonable actions in the face of uncertainty. And, especially in a combat situation, uncertainty is always going to be present.”

We search for the signature of parity-violating physics in the cosmic microwave background, called cosmic birefringence, using the Planck data release 4. We initially find a birefringence angle of β=0.30±0.11 (68% C.L.) for nearly full-sky data. The values of β decrease as we enlarge the Galactic mask, which can be interpreted as the effect of polarized foreground emission. Two independent ways to model this effect are used to mitigate the systematic impact on β for differen… See more.

We search for the signature of parity-violating physics in the cosmic.

Microwave background, called cosmic birefringence, using the Planck data.
release 4. We initially find a birefringence angle of $\beta=0.30\pm0.11$ (68%

Continue reading “Cosmic Birefringence from Planck Data Release 4” »

What’s next? Human brain-scale AI.

Funded by the Slovakian government using funds allocated by the EU, the I4DI consortium is behind the initiative to build a 64 AI exaflop machine (that’s 64 billion, billion AI operations per second) on our platform by the end of 2022. This will enable Slovakia and the EU to deliver for the first time in the history of humanity a human brain-scale AI supercomputer. Meanwhile, almost a dozen other countries are watching this project closely, with interest in replicating this supercomputer in their own countries.

There are multiple approaches to achieve human brain-like AI. These include machine learning, spiking neural networks like SpiNNaker, neuromorphic computing, bio AI, explainable AI and general AI. Multiple AI approaches require universal supercomputers with universal processors for humanity to deliver human brain-scale AI.

Advances in the AI realm are constantly coming out, but they tend to be limited to a single domain: For instance, a cool new method for producing synthetic speech isn’t also a way to recognize expressions on human faces. Meta (AKA Facebook) researchers are working on something a little more versatile: an AI that can learn capably on its own whether it does so in spoken, written or visual materials.

The traditional way of training an AI model to correctly interpret something is to give it lots and lots (like millions) of labeled examples. A picture of a cat with the cat part labeled, a conversation with the speakers and words transcribed, etc. But that approach is no longer in vogue as researchers found that it was no longer feasible to manually create databases of the sizes needed to train next-gen AIs. Who wants to label 50 million cat pictures? Okay, a few people probably — but who wants to label 50 million pictures of common fruits and vegetables?

Currently some of the most promising AI systems are what are called self-supervised: models that can work from large quantities of unlabeled data, like books or video of people interacting, and build their own structured understanding of what the rules are of the system. For instance, by reading a thousand books it will learn the relative positions of words and ideas about grammatical structure without anyone telling it what objects or articles or commas are — it got it by drawing inferences from lots of examples.