Lifeboat Foundation

Existing computer systems have separate data processing and storage devices, making them inefficient for processing complex data like AI. A KAIST research team has developed a memristor-based integrated system similar to the way our brain processes information. It is now ready for application in various devices, including smart security cameras, allowing them to recognize suspicious activity immediately without having to rely on remote cloud servers, and medical devices with which it can help analyze health data in real time.

The joint research team of Professor Shinhyun Choi and Professor Young-Gyu Yoon of the School of Electrical Engineering has developed the next-generation neuromorphic semiconductor-based ultra-small computing chip that can learn and correct errors on its own. The research is published in the journal Nature Electronics.

What is special about this computing chip is that it can learn and correct errors that occur due to non-ideal characteristics that were difficult to solve in existing neuromorphic devices. For example, when processing a video stream, the chip learns to automatically separate a moving object from the background, and it becomes better at this task over time.

An international team of researchers has made significant progress in understanding how gene expression is regulated across the human genome. In a recent study, they conducted a comprehensive analysis of cis-regulatory elements (CREs)—DNA sequences that control gene transcription. This research provides valuable insights into how CREs drive cell-specific gene expression and how mutations in these regions can impact health and contribute to disease.

CREs, such as enhancers and promoters, play a critical role in determining when and where genes are activated or silenced. Although their importance is well known, analyzing their activity on a large scale has been a longstanding challenge.

“The human genome contains a myriad of CREs, and mutations in these regions are thought to play a major role in human diseases and evolution,” explained Dr. Fumitaka Inoue, one of the co-first authors of the study. “However, it has been very difficult to comprehensively quantify their activity across the genome.”

For over 20 years, the Ihara research group at Ehime University has specialized in developing innovative methods for polymer synthesis using diazocarbonyl compounds as monomers.

They discovered that diazoacetate can be polymerized using a palladium (Pd)-based initiator to produce carbon–carbon (C–C) main-chain polymers, with each carbon atom in the backbone bonded to an alkoxycarbonyl (ester) group. Unlike traditional vinyl polymerization—where the polymer backbone is built from two-carbon units of vinyl monomers like ethylene and styrene—diazoacetate polymerization creates the C–C main chain from single-carbon units. This unique process, known as C1 polymerization, is a distinctive and significant feature of this synthesis method.

A new era in computing is emerging as researchers overcome the limitations of Moore’s Law through photonics.

This cutting-edge approach boosts processing speeds and slashes energy use, potentially revolutionizing AI and machine learning.

Machine learning is a subset of artificial intelligence (AI) that deals with the development of algorithms and statistical models that enable computers to learn from data and make predictions or decisions without being explicitly programmed to do so. Machine learning is used to identify patterns in data, classify data into different categories, or make predictions about future events. It can be categorized into three main types of learning: supervised, unsupervised and reinforcement learning.

The complexity of the human brain—86 billion neurons strong with more than 100 trillion connections—enables abstract thinking, language acquisition, advanced reasoning and problem-solving, and the capacity for creativity and social interaction. Understanding how differences in brain signaling and dynamics produce unique cognition and behavior in individuals has long been a goal of neuroscience research, yet many phenomena remain unexplained.

A study from neuroscientists and engineers at Washington University in St. Louis addresses this knowledge gap with a new method to create personalized brain models, which offer insights into individual neural dynamics. Led by ShiNung Ching, associate professor in the Preston M. Green Department of Electrical & Systems Engineering in the McKelvey School of Engineering, and Todd Braver, professor in the Department of Psychological & Brain Sciences in Arts & Sciences, the work, published Jan. 17 in PNAS, introduces a novel framework that will allow the researchers to create individualized brain models based on detailed data from noninvasive, high-temporal resolution brain scans. Such personalized models have applications in research and clinical settings, where they could support advances in neuroscience and treatment of neurological conditions.

“This research is motivated by our need to understand person-to-person variation in brain dynamics,” said first author Matthew Singh, who conducted the research while a postdoctoral fellow with Braver and Ching at WashU and is now an assistant professor at the University of Illinois Urbana-Champaign. “We’re not explaining the full range of biophysical mechanisms at work in the human brain, but we are able to shed light on why healthy individuals have different brain dynamics with our new modeling framework, which gives us insights into brain mechanics and testable predictions of brain phenomena.”

Job displacement is a serious issue everywhere, but professional computer science majors should get ready for a tightening of the belt in their field.

A Semafor article published this month, written by Reed Albergotti, shows how Amjad Masad, CEO of Replit, is enthusiastic about cutting the firm’s workforce in half, while boosting revenue something like 500% on the back of agenticAI.

Replit’s new tool can reportedly “write a working software application with nothing but a natural language prompt” and that’s going to usher in a new renaissance in computing, while costing some careerists their jobs.

Deep below the surface of our world, far beyond our feeble reach, enigmatic processes grind and roil.

Every now and then, the Earth disgorges clues to their nature: tiny chthonic diamonds encasing skerricks of rare mineral. From these tiny fragments we can glean tidbits of information about our planet’s interior.

A diamond unearthed in a diamond mine in Botswana is just such a stone. It’s riddled with flaws containing traces of ringwoodite, ferropericlase, enstatite, and other minerals that suggest the diamond formed 660 kilometers (410 miles) below Earth’s surface.

Link :-🔗: https://bit.ly/4jligRa.

Believe it or not, humans emit a faint glow all the time—it’s just invisible to the naked eye. This isn’t science fiction; it’s biology at work.

Continue reading “Humans Glow In The Dark, It’s Just Too Weak For Our Eyes To See” »

Cosmic voids, which act as bubbles in the cosmic web, help us read the universe better.

Cosmic voids are regions of space that are almost empty, except for a few galaxies and dark matter. But what do they tell us about the universe?