Lifeboat Foundation

One way to achieve this is to combine GPTs with causal AI—a precise and trustworthy type of AI that provides rich and accurate context, which is particularly valuable in cloud observability, analytics and automation.

Causal AI observes the actual relationships within a system, such as a multicloud technology stack, and delivers detailed and precise answers in near real time based on these observations. These answers enable users to discern the cause, type, severity, risk, impact and location of any issue flagged by the AI with very high precision based on real-time observed facts and their interdependencies.

In the future, DevOps teams can use automated prompt engineering to feed real-time data and causal AI-derived context to their GPT. As a result, the answers they receive will be more relevant, accurate and actionable.

A new way of 3D printing wood that takes advantage of warping could change how we build things in the future — an innovation that could potentially save us all time and money.

The challenge: Wood is made of fibers that absorb moisture like a sponge. If lumber isn’t dried properly, the wood will eventually shrink — bending or twisting in different directions depending on the orientation of the fibers.

Continue reading “3D-printed wood furniture could ship flat, then dry into shape” »

https://youtube.com/watch?v=K-70S1oycR4

1:33…“10 years”.

Dive into the fascinating world of aging research with this thought-provoking video. Join us as we explore the latest breakthroughs and scientific advancements on the quest to reverse aging. Discover the potential strategies, therapies, and technologies that hold promise for extending healthspan and pushing the boundaries of human longevity. Get ready to embark on an exciting journey towards understanding the future of aging reversal.

Continue reading “How Long Until We Reverse the Clock? | Dr David Sinclair” »

The concept of a computational consciousness and the potential impact it may have on humanity is a topic of ongoing debate and speculation. While Artificial Intelligence (AI) has made significant advancements in recent years, we have not yet achieved a true computational consciousness that can replicate the complexities of the human mind.

It is true that AI technologies are becoming more sophisticated and capable of performing tasks that were previously exclusive to human intelligence. However, there are fundamental differences between Artificial Intelligence and human consciousness. Human consciousness is not solely based on computation; it encompasses emotions, subjective experiences, self-awareness, and other aspects that are not yet fully understood or replicated in machines.

The arrival of advanced AI systems could certainly have transformative effects on society and our understanding of humanity. It may reshape various aspects of our lives, from how we work and communicate to how we approach healthcare and scientific discoveries. AI can enhance our capabilities and provide valuable tools for solving complex problems.

However, it is important to consider the ethical implications and potential risks associated with the development of AI. Ensuring that AI systems are developed and deployed responsibly, with a focus on fairness, transparency, and accountability, is crucial.

Continue reading “AI and Humanity's Future” »

The simplicity of the approach stumped even reviewers of the journal Nature and needed further proof to be believed.

Researchers at the College of Chemistry and Molecular Sciences at Wuhan University in China have achieved a significant ‘breakthrough’ in materials science that allows alloys to be made from a diverse range of metals and at much lower temperatures than conventional methods, the South China Morning Post.

Since the Bronze Age, alloys have contributed to the advancement of our civilization. Modern-day applications of alloys involve creating and manufacturing high-entropy alloys (HEAs) composed of five or more metallic elements.

While autonomous robots have started to move out of the lab and into the real world, they remain fragile. Slight changes in the environment or lighting conditions can easily throw off the AI that controls them, and these models have to be extensively trained on specific hardware configurations before they can carry out useful tasks.

This lies in stark contrast to the latest LLMs, which have proven adept at generalizing their skills to a broad range of tasks, often in unfamiliar contexts. That’s prompted growing interest in seeing whether the underlying technology—an architecture known as a transformer—could lead to breakthroughs in robotics.

In new results, researchers at DeepMind showed that a transformer-based AI called RoboCat can not only learn a wide range of skills, it can also readily switch between different robotic bodies and pick up new skills much faster than normal. Perhaps most significantly, it’s able to accelerate its learning by generating its own training data.

In an organism, different kinds of cells carry out specific, specialized functions. Scientists can grow and study various types of cells in the lab. For a long time, a source of many of those cell lines were cancer samples that could be easily cultured over many generations. But those cells were not always representative of a particular cell type. Now, following huge breakthroughs, scientists learned how to create stem cells from adult skin cells. This has allowed scientists to utilize adult cells like those from the skin to create induced pluripotent cells (iPSCs), which can then be made into virtually any cell type.

The creation of so-called iPSCs was made possible through changing gene expression in cells, often with certain molecules or specialized proteins. Cells can also now be directly reprogrammed in some ways, without needing to bring them to a pluripotent state. The number of cell types that can be generated in this way is also expanding, bringing new insights into how specialized cells function.

Part 10 a series of articles describing the elements of Lawrence Livermore National Laboratory’s fusion breakthrough.

For hundreds of Lawrence Livermore National Laboratory (LLNL) scientists on the design, experimental, and modeling and simulation teams behind inertial confinement fusion (ICF) experiments at the National Ignition Facility (NIF), the results of the now-famous Dec. 5, 2022, ignition shot didn’t come as a complete surprise.

Continue reading “High-performance computing, AI and cognitive simulation helped LLNL conquer fusion ignition” »

Collaboration, innovation, and partnership are key to more efficient, accessible, and scalable mRNA technology.