Lifeboat Foundation

“We grow them in huge bioreactors in just three weeks — while regular cannabis takes 14 to 23 weeks,” Sobel said. “Our tech can also significantly increase the levels of active ingredients, as a percent of the weight, versus what is found normally in the plant.”

An Israeli company has cloned hemp cells and used a bioreactor to grow them into a substance with all the active compounds of cannabis — and 12 times the potency.

BioHarvest Sciences says the breakthrough could make the medical benefits of cannabis available in cheaper, cleaner and greener form. It has started applying for the necessary licenses to manufacture and sell its product for medical use in Israel and the United States.

Continue reading “Cloned cannabis cells with 12 times more potency are grown in Israeli bioreactor” »

A common approach used to control robots is to program them with code to detect objects, sequencing commands to move actuators, and feedback loops to specify how the robot should perform a task. While these programs can be expressive, re-programming policies for each new task can be time consuming, and requires domain expertise.

What if when given instructions from people, robots could autonomously write their own code to interact with the world? It turns out that the latest generation of language models, such as PaLM, are capable of complex reasoning and have also been trained on millions of lines of code. Given natural language instructions, current language models are highly proficient at writing not only generic code but, as we’ve discovered, code that can control robot actions as well. When provided with several example instructions (formatted as comments) paired with corresponding code (via in-context learning), language models can take in new instructions and autonomously generate new code that re-composes API calls, synthesizes new functions, and expresses feedback loops to assemble new behaviors at runtime.

Artificial-intelligence systems are increasingly limited by the hardware used to implement them. Now comes a new superconducting photonic circuit that mimics the links between brain cells—burning just 0.3 percent of the energy of its human counterparts while operating some 30,000 times as fast.

In artificial neural networks, components called neurons are fed data and cooperate to solve a problem, such as recognizing faces. The neural net repeatedly adjusts the synapses—the links between its neurons—and determines whether the resulting patterns of behavior are better at finding a solution. Over time, the network discovers which patterns are best at computing results. It then adopts these patterns as defaults, mimicking the process of learning in the human brain.

We continue our look at possible explanations why life may be very rare in the Universe by examining our planet itself, and looking at which of its characteristic might be important to intelligence developing and how improbable those traits are for a given planet in a given solar system.

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Cover Art by Jakub Grygier: https://www.artstation.com/artist/jakub_grygier.

Continue reading “Fermi Paradox Great Filters: Rare Earth” »

The computing power of quantum machines is currently still very low. Increasing performance is a major challenge. Physicists at the University of Innsbruck, Austria, now present a new architecture for a universal quantum computer that overcomes such limitations and could be the basis of the next generation of quantum computers soon.

Quantum bits (qubits) in a quantum computer serve as a computing unit and memory at the same time. Because quantum information cannot be copied, it cannot be stored in memory as in a classical computer. Due to this limitation, all qubits in a quantum computer must be able to interact with each other.

This is currently still a major challenge for building powerful quantum computers. In 2015, theoretical physicist Wolfgang Lechner, together with Philipp Hauke and Peter Zoller, addressed this difficulty and proposed a new architecture for a quantum computer, now named LHZ architecture after the authors.

CNN

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A single dose of a synthetic version of the mind-altering component of magic mushrooms, psilocybin, improved depression in people with a treatment-resistant form of the disease, a new study found.

Continue reading “Severe depression eased by single dose of synthetic ‘magic mushroom’” »

Visitors in Spatial are invited to a Day of the Dead themed metaverse festival while in Decentraland they can attend a virtual Day of the Dead parade.

Use a portal to move between the Spatial and Decentraland metaverses.

The virtual event celebrates the Day of the Dead festivities during the entire month of November.

New images from scientists at Cold Spring Harbor Laboratory (CSHL) reveal for the first time the three-dimensional structures of a set of molecules critical for healthy brain function.

The molecules are members of a family of proteins in the brain known as NMDA receptors, which mediate the passage of essential signals between neurons. The detailed pictures generated by the CSHL team will serve as a valuable blueprint for drug developers working on new treatments for schizophrenia, depression, and other neuropsychiatric conditions.

“This NMDA receptor is such an important drug target,” says Tsung-Han Chou, a postdoctoral researcher in CSHL Professor Hiro Furukawa’s lab. That’s because dysfunctional NMDA receptors are thought to contribute to a wide range of conditions, including not just depression and schizophrenia, but also Alzheimer’s disease, stroke, and seizures. “We hope our images, which visualize the receptor for the first time, will facilitate drug development across the field based on our structural information,” Chou says.

It’s important in sports and in interpersonal relationships—perfect timing. But how does our brain learn to estimate when events might occur and react accordingly? Scientists at MPI CBS in Leipzig together with colleagues from the Kavli Institute at the Norwegian University of Science and Technology in Trondheim were able to demonstrate in an MRI study that our brain learns best in connection with constructive feedback.

Imagine playing a game with friends, where they throw you a ball that you must catch. The first couple of throws you might miss the ball, but as you keep trying, you become better at estimating the time it takes to reach you and catch it more easily. How does your brain do this? “Fundamental to this process are your abilities to learn from past experiences and to extract time-related information from the environment,” explains Ignacio Polti, who conducted the study now published in the journal eLife together with Matthias Nau and Christian Doeller.

“Every throw of your friend will be slightly different from the previous one. Some balls arrive earlier, some arrive later. During the game, your brain learns the distribution of arrival times, and it uses this information to form expectations for future throws. By combining such prior knowledge with specific information of our friend’s current throw, we can thus improve the timing of our catch attempts.”