Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog – Page 4587

Companies creating lab-grown steak, chicken, and fish see a recent White House announcement as a signal that meat grown without animal slaughter is on the cusp of being legally sold and eaten in the US.

“We are laser focused on commercial-scale production, and for us, that means moving into competing with conventional meat products in scale,” said Eric Schulze, vice president of product and regulation at Upside Foods, a cultivated meat company, as the industry calls itself. The goal is to be selling its meat on the US market within the year.

The traditional meat and poultry industry reacted strongly to President Joe Biden’s executive order last month on biotechnology and biomanufacturing, which observers say could push federal agencies to allow commercial sales of meat grown from an animal’s cells.

Read more | >

Algorithms have helped mathematicians perform fundamental operations for thousands of years. The ancient Egyptians created an algorithm to multiply two numbers without requiring a multiplication table, and Greek mathematician Euclid described an algorithm to compute the greatest common divisor, which is still in use today.

During the Islamic Golden Age, Persian mathematician Muhammad ibn Musa al-Khwarizmi designed new algorithms to solve linear and quadratic equations. In fact, al-Khwarizmi’s name, translated into Latin as Algoritmi, led to the term algorithm. But, despite the familiarity with algorithms today – used throughout society from classroom algebra to cutting edge scientific research – the process of discovering new algorithms is incredibly difficult, and an example of the amazing reasoning abilities of the human mind.

In our paper, published today in Nature, we introduce AlphaTensor, the first artificial intelligence (AI) system for discovering novel, efficient, and provably correct algorithms for fundamental tasks such as matrix multiplication. This sheds light on a 50-year-old open question in mathematics about finding the fastest way to multiply two matrices.

Read more | >

Today, Google announced the development of Imagen Video, a text-to-video AI mode capable of producing 1280×768 videos at 24 frames per second from a written prompt. Currently, it’s in a research phase, but its appearance five months after Google Imagen points to the rapid development of video synthesis models.

According to Google’s research paper, Imagen Video includes several notable stylistic abilities, such as generating videos based on the work of famous painters (the paintings of Vincent van Gogh, for example), generating 3D rotating objects while preserving object structure, and rendering text in a variety of animation styles. Google is hopeful that general-purpose video synthesis models can “significantly decrease the difficulty of high-quality content generation.”

Read more | >

Quantum computing and communication often rely on the entanglement of several photons together. But obtaining these multiphoton states is a bit like playing the lottery, as generating entanglement between photons only succeeds a small fraction of the time. A new experiment shows how to improve one’s odds in this quantum game of chance. The method works like an entanglement assembly line, in which entangled pairs of photons are created in successive order and combined with stored photons.

The traditional method for obtaining multiphoton entanglement requires a large set of photon sources. Each source simultaneously generates an entangled photon pair, and those photons are subsequently interfered with each other. The process is probabilistic in that each step only succeeds in producing pair entanglement, say, once in every 20 tries. The odds become exponentially worse as entanglement of more and more photons is attempted.

Christine Silberhorn from Paderborn University, Germany, and her colleagues have developed a new method that offers a relatively high success rate [1]. They use a single source that generates pairs of polarization-entangled photons in succession. After the first pair is created, one of these photons is stored in an optical loop. When the source creates a new pair (which can take several tries), one of these photons is interfered with the stored photon. If successful, this interference creates a four-photon entangled state. The process can continue—with new pairs being generated and one photon being stored—until the desired multiphoton state is reached.

Read more | >

The rise of quantum computing and its implications for current encryption standards are well known. But why exactly should quantum computers be especially adept at breaking encryption? The answer is a nifty bit of mathematical juggling called Shor’s algorithm. The question that still leaves is: What is it that this algorithm does that causes quantum computers to be so much better at cracking encryption? In this video, YouTuber minutephysics explains it in his traditional whiteboard cartoon style.

“Quantum computation has the potential to make it super, super easy to access encrypted data — like having a lightsaber you can use to cut through any lock or barrier, no matter how strong,” minutephysics says. “Shor’s algorithm is that lightsaber.”

According to the video, Shor’s algorithm works off the understanding that for any pair of numbers, eventually multiplying one of them by itself will reach a factor of the other number plus or minus 1. Thus you take a guess at the first number and factor it out, adding and subtracting 1, until you arrive at the second number. That would unlock the encryption (specifically RSA here, but it works on some other types) because we would then have both factors.

Read more | >

Three scientists who laid the groundwork for the understanding of the odd “entangling” behavior of quantum particles have received the 2022 Nobel Prize in Physics.

French physicist Alain Aspect, Austria’s Anton Zeilinger and American John Clauser were honored for their experiments exploring the nature of entangled quantum particles.

Read more | >

This article was originally published at The Conversation. (opens in new tab) The publication contributed the article to Space.com’s Expert Voices: Op-Ed & Insights.

A glowing blob known as “the cocoon,” which appears to be inside one of the enormous gamma-ray emanations from the center of our galaxy dubbed the “Fermi bubbles,” has puzzled astronomers since it was discovered in 2012.

Read more | >

Accurate detection and manipulation of endogenous proteins is essential to understand cell biological processes, which motivated laboratories across cell biology to develop highly efficient CRISPR genome editing methods for endogenous epitope tagging (Auer et al., 2014; Nakade et al., 2014; Lackner et al., 2015; Schmid-Burgk et al., 2016; Suzuki et al., 2016; Nishiyama et al., 2017; Artegiani et al., 2020; Danner et al., 2021). Multiplex editing using NHEJ-based CRISPR/Cas9 methods remains limited because of the high degree of cross talk that occurs between two knock-in loci (Gao et al., 2019; Willems et al., 2020). In the current study we present CAKE, a mechanism to diminish cross talk between NHEJ-based CRISPR/Cas9 knock-ins using sequential activation of gRNA expression. We demonstrate that this mechanism strongly reduces cross talk between knock-in loci, and results in dual knock-ins for a wide variety of genes. Finally, we showed that CAKE can be directly applied to reveal new biological insights. CAKE allowed us to perform two-color super-resolution microscopy and acute manipulation of the dynamics of endogenous proteins in neurons, together revealing new insights in the nanoscale organization of synaptic proteins.

The CAKE mechanism presented here creates a mosaic of CreON and CreOFF knock-ins, and the number of double knock-in cells depends on the efficacy of each knock-in vector. Therefore, to obtain a high number of double knock-in cells, the efficacy of both the CreON and CreOFF knock-in vector must be optimized. We identified three parameters that regulate the efficacy for single and double knock-ins in neurons. First, the efficacy of gRNAs varies widely, and even gRNAs that target sequences a few base pairs apart in the same locus can have dramatically different knock-in rates (Willems et al., 2020; Danner et al., 2021; Fang et al., 2021; Zhong et al., 2021). Thus, the efficacy of each individual gRNA must be optimized to increase the chance of successful multiplex labeling in neurons. gRNA performance is dependent on many factors, including the rate of DNA cleavage and repair (Rose et al., 2017; Liu et al., 2020; Park et al.

Read more | >

PASADENA, Calif. (Reuters)-Fast-food French fries and onion rings are going high-tech, thanks to a company in Southern California.

Miso Robotics Inc in Pasadena has started rolling out its Flippy 2 robot, which automates the process of deep frying potatoes, onions and other foods.

A big robotic arm like those in auto plants — directed by cameras and artificial intelligence — takes frozen French fries and other foods out of a freezer, dips them into hot oil, then deposits the ready-to-serve product into a tray.

Read more | >

Chipmaker Micron Technology revealed on Tuesday ambitious plans to develop a $100-billion computer chip factory complex in upstate New York, in a bid to boost domestic chip manufacturing and possibly deal with a worrying chips shortage. The money will be invested over a 20 year period, according to Reuters.

The world’s largest semiconductor fabrication facility

Micron claims the project will be the world’s largest semiconductor fabrication facility and will create nearly 50,000 jobs in New York alone. Currently, the largest semiconductor manufacturers in the world are: Intel Corp., Samsung, Taiwan Semiconductor Manufacturing Co. Ltd. (TSMC), SK Hynix, Micron Technology Inc., Qualcomm, Broadcom Inc., and Nvidia.

Read more | >