Lifeboat Foundation

In a new multidisciplinary study, researchers at Texas A&M University showed how quantum computing—a new kind of computing that can process additional types of data—can assist with genetic research and used it to discover new links between genes that scientists were previously unable to detect.

Their project used the new computing technology to map gene regulatory networks (GRNs), which provide information about how genes can cause each other to activate or deactivate.

As the team published in npj Quantum Information, quantum computing will help scientists more accurately predict relationships between genes, which could have huge implications for both animal and human medicine.

The Polycomb repressive complex 2 (PRC2), initially identified in Drosophila several decades ago, was found to be a key controller of developmental genes. Subsequent research revealed that PRC2 alters chromatin structure to suppress the expression of specific genes.

This initial understanding of PRC2’s ancestral function — functioning primarily to control genes during development — was challenged when it was found to be active in unicellular organisms, in which no development takes place.

Last week, a group of AI and military leaders met in a secretive three-day retreat hosted at a luxury resort in the Utah mountains.

It is estimated that 95% of the planet’s population has access to broadband internet, via cable or a mobile network. However, there are still some places and situations in which staying connected can be very difficult. Quick responses are necessary in emergency situations, such as after an earthquake or during a conflict. So too are reliable telecommunications networks that are not susceptible to outages and damage to infrastructure, networks can be used to share data that is vital for people’s well-being.

A recent article, published in the journal Aerospace, proposes the use of nanosatellites to provide comprehensive and stable coverage in areas that are hard to reach using long-range communications. It is based on the bachelor’s and master’s degree final projects of Universitat Oberta de Catalunya (UOC) graduate David N. Barraca Ibort.

The paper is co-authored by Raúl Parada, a researcher at the Telecommunications Technological Center of Catalonia (CTTC/CERCA) and a course instructor with the UOC’s Faculty of Computer Science, Multimedia and Telecommunications; Carlos Monzo, a researcher and member of the same faculty; and Víctor Monzón, a researcher at the Interdisciplinary Center for Security Reliability and Trust at the University of Luxembourg.

Nov 20 (Reuters) — Following a surprise ouster, OpenAI co-founder and former CEO Sam Altman joined Microsoft (MSFT.O) as the head of artificial intelligence research along with the ChatGPT maker’s former President Greg Brockman and other staff.

The developments come less than a year after OpenAI kicked off the generative AI frenzy with the launch of viral chatbot ChatGPT and bagged Microsoft as an investor, among other big names.

The shakeup is not the first at OpenAI, which was launched in 2015. Tesla CEO Elon Musk, a co-founder of the non-profit, was once its co-chair, and in 2020 other executives departed, going on to found competitor Anthropic, which claims to have a greater focus on AI safety.

As part of pioneering the security of satellite communication in space, NASA is funding a groundbreaking project at the University of Miami’s Frost Institute for Data Science and Computing (IDSC) which will enable augmenting traditional large satellites with nanosatellites or constellations of nanosatellites.

These nanosatellites are designed to accomplish diverse goals, ranging from communication and weather prediction to Earth science research and observational data gathering. Technical innovation is a hallmark of NASA, a global leader in the development of novel technologies that enable US space missions and translate to a wide variety of applications from Space and Earth science to consumer goods and to national and homeland security.

With advances in satellite technology and reduced cost of deployment and operation, nanosatellites also come with significant challenges for the protection of their communication networks. Specifically, small satellites are owned and operated by a wide variety of public and private sector organizations, expanding the attack surface for cyber exploitation. The scenario is similar to Wi-Fi network vulnerabilities. These systems provide an opportunity for adversaries to threaten national security as well as raise economic concerns for satellite companies, operators, and users.

Advancements in genetic engineering, gene therapies, and anti-aging research may eventually allow for age reversal and the restoration of youthful health and longevity.

What is the key idea of the video?
—The key idea is that advancements in genetic engineering and anti-aging research may eventually allow for age reversal and the restoration of youthful health and longevity.

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Large language models (LLMs) are impressive technological creations but they cannot replace all scientific theories of cognition. A science of cognition must focus on humans as embodied, social animals who are embedded in material, cultural and technological contexts.

There is the technological question of whether computers can be intelligent, and also the scientific question of how it is that humans and other animals are intelligent. Answering either question requires an agreement about what the word ‘intelligence’ means. Here, I will both follow common usage and avoid making it a matter of definition that only adult humans could possibly be intelligent by assuming that to be intelligent is to have the ability to solve complex and cognitively demanding problems. If we understand intelligence this way, the question of whether computers can be intelligent has already been answered. With apologies to Dreyfus and Lanier, it has been clear for years that the answer is an emphatic ‘yes’. The recent advances made by ChatGPT and other large language models (LLMs) are the cherry on top of decades of technological innovation.

Researchers confront a formidable challenge within the expansive domain of materials science—efficiently distilling essential insights from densely packed scientific texts. This intricate dance involves navigating complex content and generating coherent question-answer pairs that encapsulate the core of the material. The complexity lies in the substantial task of extracting pivotal information from the dense fabric of scientific texts, requiring researchers to craft meaningful question-answer pairs that capture the essence of the material.

Current methodologies within this domain often lean on general-purpose language models for information extraction. However, these approaches need help with text refinement and the accurate incorporation of equations. In response, a team of MIT researchers introduced MechGPT, a novel model grounded in a pretrained language model. This innovative approach employs a two-step process, utilizing a general-purpose language model to formulate insightful question-answer pairs. Beyond mere extraction, MechGPT enhances the clarity of key facts.

The journey of MechGPT commences with a meticulous training process implemented in PyTorch within the Hugging Face ecosystem. Based on the Llama 2 transformer architecture, the model flaunts 40 transformer layers and leverages rotary positional embedding to facilitate extended context lengths. Employing a paged 32-bit AdamW optimizer, the training process attains a commendable loss of approximately 0.05. The researchers introduce Low-Rank Adaptation (LoRA) during fine-tuning to augment the model’s capabilities. This involves integrating additional trainable layers while freezing the original pretrained model, preventing the model from erasing its initial knowledge base. The result is heightened memory efficiency and accelerated training throughput.