Lifeboat Foundation

While LLMs are trained on massive, diverse datasets, SLMs concentrate on domain-specific data. In such cases, the data is often from within the enterprise. This makes SLMs tailored to industries or use cases, thereby ensuring both relevance and privacy.

As AI technologies expand, so do concerns about cybersecurity and ethics. The rise of unsanctioned and unmanaged AI applications within organisations, also referred to as ‘Shadow AI’, poses challenges for security leaders in safeguarding against potential vulnerabilities.

Predictions for 2025 suggest that AI will become mainstream, speeding up the adoption of cloud-based solutions across industries. This shift is expected to bring significant operational benefits, including improved risk assessment and enhanced decision-making capabilities.

Whether it’s our phones, cars, televisions, medical devices or even washing machines, we now have computers everywhere.

Using bigger computers, we solve bigger problems like managing the operation of a power grid, designing an aircraft, predicting the weather or providing different types of artificial intelligence (AI).

But all these machines work by manipulating data in the form of ones and zeros (bits) using classical techniques that have not changed since the abacus was invented in antiquity.

Large language models, a type of AI that analyzes text, can predict the results of proposed neuroscience studies more accurately than human experts, finds a study led by UCL (University College London) researchers.

The findings, published in Nature Human Behaviour, demonstrate that large language models (LLMs) trained on vast datasets of text can distill patterns from scientific literature, enabling them to forecast scientific outcomes with superhuman accuracy.

The researchers say this highlights their potential as powerful tools for accelerating research, going far beyond just knowledge retrieval.

A new composite material developed by KIMS researchers absorbs over 99% of electromagnetic waves from different frequencies, improving the performance of devices like smartphones and wearables.

A team of scientists from the Korea Institute of Materials Science (KIMS) has developed the world’s first ultra-thin film composite material capable of absorbing over 99% of electromagnetic waves from various frequency bands, including 5G/6G, WiFi, and autonomous driving radar, using a single material.

This novel electromagnetic wave absorption and shielding material is less than 0.5mm thick and is characterized by its low reflectance of less than 1% and high absorbance of over 99% across three different frequency bands.

Engineers took to a competition pool to test robotic prototypes for an ambitious mission concept—a swarm of underwater explorers seeking signs of life on alien ocean worlds.

NASAs upcoming missions to Europa will deploy advanced robots to probe its icy oceans for life. The robots, part of the SWIM project, have been rigorously tested on Earth and through simulations to handle extraterrestrial conditions.

Continue reading “Could NASA’s Tiny Robots Discover Life on Europa?” »

Trying to understand the makeup and evolution of the solar system’s Kuiper belt has kept researchers busy since it was hypothesized soon after the discovery of Pluto in 1930. In particular, binary pairs of objects there are useful as indicators since their existence today paints a picture of how energetic or violent the evolution of the solar system was in its early days four billion years ago.

Looking closely at the evolution of an ultrawide (in separation) binary object, researchers included more physics that reveals much about their architecture and unfolding. They found that these ultrawide binaries may not have been formed in the primordial solar system as has been thought. Their work has been published in Nature Astronomy.

“In the outer reaches of the solar system, there exists a population of binary systems so widely separated that it seemed worth looking into whether or not they could even survive 4 billion years without being [completely] separated somehow,” said Hunter M. Campbell of the University of Oklahoma in the US.

On a cool morning this summer, I visited a former shopping mall in Mountain View, California, that is now a Google office building. On my way inside, I passed a small museum of the company’s past “moonshots,” including Waymo’s first self-driving cars. Upstairs, Jonathan Tompson and Danny Driess, research scientists in Google DeepMind’s robotics division, stood in the center of what looked like a factory floor, with wires everywhere.

At a couple of dozen stations, operators leaned over tabletops, engaged in various kinds of handicraft. They were not using their own hands—instead, they were puppeteering pairs of metallic robotic arms. The setup, known as ALOHA, “a low-cost open-source hardware system for bimanual teleoperation,” was once Zhao’s Ph.D. project at Stanford. At the end of each arm was a claw that rotated on a wrist joint; it moved like the head of a velociraptor, with a slightly stiff grace. One woman was using her robotic arms to carefully lower a necklace into the open drawer of a jewelry case. Behind her, another woman prized apart the seal on a ziplock bag, and nearby a young man swooped his hands forward as his robotic arms folded a child’s shirt. It was close, careful work, and the room was quiet except for the wheeze of mechanical joints opening and closing. “It’s quite surprising what you can and can’t do with parallel jaw grippers,” Tompson said, as he offered me a seat at an empty station. “I’ll show you how to get started.”

A novel device consisting of metal, dielectric, and metal layers remembers the history of electrical signals sent through it. This device, called a memristor, could serve as the basis for neuromorphic computers-;computers that work in ways similar to human brains. Unlike traditional digital memory, which stores information as 0s and 1s, this device exhibits so-called “analog” behavior. This means the device can store information between 0 and 1, and it can emulate how synapses function in the brain. Researchers found that the interface between metal and dielectric in the novel device is critical for stable switching and enhanced performance. Simulations indicate that circuits built on this device exhibit improved image recognition.

The Impact

Today’s computers are not energy efficient for big data and machine learning tasks. By 2030, experts predict that data centers could consume about 8% of the world’s electricity. To address this challenge, researchers are working to create computers inspired by the human brain, so-called neuromorphic computers. Artificial synapses created with memristor devices are the building blocks of these computers. These artificial synapses can store and process information in the same location, similar to how neurons and synapses work in the brain. Integrating these emergent devices with conventional computer components will reduce power needs and improve performance for tasks such as artificial intelligence and machine learning.

Summary: A new AI algorithm inspired by the genome’s ability to compress vast information offers insights into brain function and potential tech applications. Researchers found that this algorithm performs tasks like image recognition and video games almost as effectively as fully trained AI networks.

By mimicking how genomes encode complex behaviors with limited data, the model highlights the evolutionary advantage of efficient information compression. The findings suggest new pathways for developing advanced, lightweight AI systems capable of running on smaller devices like smartphones.