Blowing older methods away, which can take hours and even days.
Global data production is estimated to reach 463 exabytes per day by 2025 — which is the equivalent of 212,765,957 DVDs per day, per the World Economic Forum.
Our existing data-storage systems, which can hold only so many 0s and 1s, and consume huge amounts of energy and space, cannot last us forever, putting us on the cusp of a serious data-storage problem that can only worsen over time. DNA-based data storage may come to the rescue as an alternative to hard drives since our genetic code is millions of times more efficient at storing information than current solutions. Now, in a breakthrough development, researchers at Northwestern University have devised a new method for recording information to DNA that takes minutes rather than hours or days.
Tesla’s impressive third-quarter delivery performance overshadowed the automotive industry’s ongoing struggle with the semiconductor chip shortage. Among all of the world’s automakers, Tesla has been basically the only car company to avert the crisis as it has not had any overwhelmingly public stoppages in vehicle production. Morgan Stanley’s new investor note, drafted by lead analyst Adam Jonas, examines Tesla’s ability to avoid detrimental production stoppages, which effectively helped the company capture its best quarter in company history.
Jonas titles Morgan Stanley’s most recent investor note, “How Did Tesla Find Chips?” In all honesty, this riddle was solved during the Q2 2021 Earnings Call, where Tesla stated in its Shareholder Deck that it used a combination of in-house microcontrollers to avoid any major catastrophes in the manufacturing of its vehicles. The company wrote:
“Our team has demonstrated an unparalleled ability to react quickly and mitigate disruptions to manufacturing caused by semiconductor shortages. Our electrical and firmware engineering teams remain hard at work designing, developing and validating 19 new variants of controllers in response to ongoing semiconductor shortages.”
For more than 20 years, D-Wave has been synonymous with quantum annealing. Its early bet on this technology allowed it to become the world’s first company to sell quantum computers, but that also somewhat limited the real-world problems its hardware could solve, given that quantum annealing works especially well for optimization problems like protein folding or route planning. But as the company announced at its Qubits conference today, a superconducting gate-model quantum computer — of the kind IBM and others currently offer — is now also on its roadmap.
D-Wave believes the combination of annealing, gate-model quantum computing and classic machines is what its businesses’ users will need to get the most value from this technology. “Like we did when we initially chose to pursue annealing, we’re looking ahead,” the company notes in today’s announcement. “We’re anticipating what our customers need to drive practical business value, and we know error-corrected gate-model quantum systems with practical application value will be required for another important part of the quantum application market: simulating quantum systems. This is an application that’s particularly useful in fields like materials science and pharmaceutical research.”
Scientists have demonstrated new behaviour, vital for the creation of quantum computers, that marks a major breakthrough.
For the first time, researchers were able to show in an experiment that a variety of quantum computing pieces, taken together, were more accurate than the sum of their parts.
Individually, quantum computers are built out of a range of different pieces, some of which can sometimes break. But in the new experiment, scientists showed that those pieces stuck together can be less prone to error than any particular part.
Proteins are structured like folded chains. These chains are composed of small units of 20 possible amino acids, each labeled by a letter of the alphabet. A protein chain can be represented as a string of these alphabetic letters, very much like a string of music notes in alphabetical notation.
Protein chains can also fold into wavy and curved patterns with ups, downs, turns, and loops. Likewise, music consists of sound waves of higher and lower pitches, with changing tempos and repeating motifs.
Protein-to-music algorithms can thus map the structural and physiochemical features of a string of amino acids onto the musical features of a string of notes.
Elon Musk reiterated Tesla’s commitment to China, stating that the company will continue to invest in the country. Musk made his announcement at a pre-recorded question-and-answer session at China’s Cyberspace Administration’s Global Digital Conference. Musk has hailed the country that is vital to his electric vehicle company for the second time this month, calling it as a “global leader in digitalization.” As per Mr. Musk’s comments made during another pre-recorded webcast at the World New Energy Vehicle Congress less than two weeks earlier, Chinese automakers were the “most internationally competitive.” Musk was enthusiastic in his praise for the nation that is vital to Tesla’s electric vehicle business, and his remarks came as the company works to repair its image in China. Musk stated, “I have a lot of respect for the various Chinese automakers who are driving these (EV) technologies.” Musk stated Chinese carmakers are among the best at software, which he claims will “shape the future of the vehicle industry” during his three-minute speech. “My honest view is that China invests a lot of resources and efforts adopting the latest digital technologies in various areas, including the automotive industry,” Musk said in a recent video. China has become a global leader in digitalization in the vehicle industry.” “Tesla will continue to boost its investment and research and development efforts in China.” However, negative coverage of Tesla has also grown in China over the last year. In one high-profile case, a woman claiming to be a Tesla customer protested an apparent brake failure in her car at the Shanghai auto show in April. Tesla was accused of having an “arrogant and aggressive approach” in China, according to official media, after a video of the incident went viral on Chinese social media. But now, Tesla has been attempting to repair its image in China following a barrage of negative headlines. The corporation has been under governmental scrutiny for its privacy practises, as well as several recalls in China. Some state and military employees are apparently restricted from driving Tesla electric vehicles. Musk emphasized data protection in his speech and outlined the many types of data that are stored locally. “At Tesla, we’re pleased to see a bustle of fresh laws and regulations targeted at enhancing data handling,” Musk remarked. In the past, the corporation is said to have broken ground on a big Shanghai facility. According to Reports, Tesla sold 44,264 Made-in-China automobiles by August 2021. There were 31,379 for export, which marked an increase over July’s 32,968 made-in-China automobiles sold and June’s 33,155 units sold. Local EV firms like Xpeng Inc., Li Auto Inc., and Nio Inc. are also posing a threat to Tesla in China. Last month, shipments of China-made cars to domestic purchasers increased, and exports from the company’s Shanghai factory — largely to Europe — increased. As a result, Tesla’s overall China shipments increased 34% from July to 44,264 units in August. According to sources, Tesla momentarily suspended some tasks at its Shanghai factory last month due to a global shortfall of semiconductors. Because of a shortage of crucial chips, a portion of a production line at the China plant was shut down for nearly four days in August. Tesla created a data centre in China to contain all of the data generated by our businesses there, which include manufacturing, sales, service, and charging. All personally identifying information is kept secure in China and is never sent abroad. Data is only permitted for international transfer in very rare instances, such as spare parts orders from overseas.” Tesla is acting in response to new Chinese government regulations governing how carmakers with cameras and sensors collect and use data. Tesla also said in a statement that it was “glad to hold discussions with industry experts” regarding new data security requirements for automobiles in the country. “Data security in automobiles is critical. Tesla will make every effort to maintain data security by implementing automotive data security management.”
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Developing drugs for a range of tauopathies — dr leticia toledo-sherman, senior director, drug discovery, tau consortium, rainwater charitable foundation.
Dr. Leticia Toledo-Sherman is Senior Director of Drug Discovery of the Tau Consortium (https://tauconsortium.org/) for The Rainwater Charitable Foundation (https://rainwatercharitablefoundation.org/medical-research) and also holds an appointment as Adjunct Assistant Professor of Neurology at UCLA.
Dr. Toledo-Sherman leads drug discovery activities for an international network of scientists working to develop therapies for Tauopathies, a group of neurodegenerative disorders characterized by the deposition of abnormal Tau protein in the brain.
Previously, Dr. Toledo-Sherman was Director of Medicinal Chemistry and Computer-Aided Drug Design at the CHDI Foundation, leading drug discovery programs for therapeutic development in Huntington’s Disease (HD). At CHDI, she also led a structural biology initiative critical to the understanding of the relationship between structure and biological function of huntingtin, the protein that when mutated causes HD.
Prior to joining CHDI, Dr. Toledo-Sherman was Executive Director of Chemistry at Lymphosign (now part of Pharmascience Inc), a privately held biotechnology company that applied rational design principles to the development of therapeutics for blood cancers. From 2000 to 2,004 she led a multi-site, multidisciplinary team using chemical proteomics and bioinformatics to discover therapeutic targets and to investigate the mechanism of action of drugs.
Dr. Toledo-Sherman started her professional career as a leading scientist at Kinetix Pharmaceuticals (acquired by Amgen), where she implemented an in-silico platform aimed at rational drug design of kinase inhibitors targeting multiple therapeutic areas.
Besides her work in discovery and development of therapies for neurodegenerative diseases, Dr. Toledo-Sherman is interested in the field of Chemical Biology. She has been involved in several initiatives and expert panels on the use of sharp proof-of-concept chemical probes to validate targets and interrogate biological mechanisms. She is also an advocate for open science at the pre-competitive drug discovery stage.
Dr. Toledo-Sherman received a PhD in Organic Chemistry from The State University of New York at Stony Brook and did postdoctoral research at the Massachusetts Institute of Technology and The Skaggs Institute for Chemical Biology at The Scripps Research Institute in La Jolla, California.
Intel today announced a major update to its neuromorphic computing program, including a second-generation chip called Loihi 2 and Lava, an open-source framework for developing “neuro-inspired” applications. The company is now offering two Loihi 2-based neuromorphic systems — Oheo Gulch and Kapoho Point. They will be available through a cloud service to members of the Intel Neuromorphic Research Community (INRC) and Lava via GitHub for free.
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Intel unveiled the second generation of its neuromorphic chip and claims it will be able to solve planning and optimization problems.
