Information Science – Lifeboat News: The Blog

Nov 7
2023

Scientists Build Artificial Brain From Silver Nanowires

So-called neuromorphic networks could be a much more efficient way to train and run machine learning algorithms.

Nov 6
2023

Smart Glasses Revolution: Unlocking Human Potential with Phantom Technology’s AR Assistant

The future of smart glasses is about to change drastically with the upcoming release of DigiLens ARGO range next year. These innovative smart glasses will be powered by Phantom Technology’s cutting-edge spatial AI assistant, CASSI. This partnership between DigiLens, based in Silicon Valley, and Phantom Technology, located at St John’s Innovation Centre, brings together the expertise of both companies to create a game-changing wearable device.

Phantom Technology, an AI start-up founded by a group of brilliant minds, has been working diligently over the years to develop advanced human interface technologies for AI wearables. Their breakthrough 3D imaging technology allows users to identify objects in their environment, enhancing their overall experience. With DigiLens incorporating Phantom’s patented optical platform into their consumer product, customers can expect a new era of smart glasses with unparalleled features.

CASSI, the novel spatial AI assistant designed by Phantom Technology, aims to boost productivity and awareness in enterprise settings. This innovative assistant combines computer vision algorithms with a large language model, enabling users to receive step-by-step instructions and assistance for various tasks. Imagine effortlessly locating any physical object or destination in the real world with 3D precision, using your voice to generate instructions, and seamlessly managing tasks using augmented reality.

Nov 4
2023

This Is How AI Can Use Your WI-FI As A Camera To Spy On You

In recent years, the field of artificial intelligence has witnessed remarkable advancements, with researchers exploring innovative ways to utilize existing technology in groundbreaking applications. One such intriguing concept is the use of WiFi routers as virtual cameras to map a home and detect the presence and locations of individuals, akin to an MRI machine. This revolutionary technology harnesses the power of AI algorithms and WiFi signals to create a unique, non-intrusive way of monitoring human presence within indoor spaces. In this article, we will delve into the workings of this technology, its potential capabilities, and the implications it may have on the future of smart homes and security.

The Foundation of WiFi Imaging: WiFi imaging, also known as radio frequency (RF) sensing, revolves around leveraging the signals emitted by WiFi routers. These signals interact with the surrounding environment, reflecting off objects and people within their range. AI algorithms then process the alterations in these signals to form an image of the indoor space, thus providing a representation of the occupants and their movements. Unlike traditional cameras, WiFi imaging is capable of penetrating walls and obstructions, making it particularly valuable for monitoring people without compromising their privacy.

AI Algorithms in WiFi Imaging: The heart of this technology lies in the powerful AI algorithms that interpret the fluctuations in WiFi signals and translate them into meaningful data. Machine learning techniques, such as neural networks, play a pivotal role in recognizing patterns, identifying individuals, and discerning between static objects and moving entities. As the AI model continuously learns from the WiFi data, it enhances its accuracy and adaptability, making it more proficient in detecting and tracking people over time.

Nov 3
2023

Researcher Claims to Crack RSA-2048 With Quantum Computer

A scientist claims to have developed an inexpensive system for using quantum computing to crack RSA, which is the world’s most commonly used public key algorithm.

The response from multiple cryptographers and security experts is: Sounds great if true, but can you prove it? “I would be very surprised if RSA-2048 had been broken,” Alan Woodward, a professor of computer science at England’s University of Surrey, told me.

Nov 2
2023

Late not great—imperfect timekeeping places significant limit on quantum computers

New research from a consortium of quantum physicists, led by Trinity College Dublin’s Dr. Mark Mitchison, shows that imperfect timekeeping places a fundamental limit to quantum computers and their applications. The team claims that even tiny timing errors add up to place a significant impact on any large-scale algorithm, posing another problem that must eventually be solved if quantum computers are to fulfill the lofty aspirations that society has for them.

The paper is published in the journal Physical Review Letters.

It is difficult to imagine modern life without clocks to help organize our daily schedules; with a digital clock in every person’s smartphone or watch, we take precise timekeeping for granted—although that doesn’t stop people from being late.

Oct 31
2023

Like Humans, This Breakthrough AI Makes Concepts Out of the Words It Learns

Even as toddlers, we have an uncanny ability to turn what we learn about the world into concepts. With just a few examples, we form an idea of what makes a “dog” or what it means to “jump” or “skip.” These concepts are effortlessly mixed and matched inside our heads, resulting in a toddler pointing at a prairie dog and screaming, “But that’s not a dog!”

Last week, a team from New York University created an AI model that mimics a toddler’s ability to generalize language learning. In a nutshell, generalization is a sort of flexible thinking that lets us use newly learned words in new contexts—like an older millennial struggling to catch up with Gen Z lingo.

When pitted against adult humans in a language task for generalization, the model matched their performance. It also beat GPT-4, the AI algorithm behind ChatGPT.

Oct 30
2023

Research claims novel algorithm can exactly compute information rate for any system

75 years ago Claude Shannon, the “father of information theory,” showed how information transmission can be quantified mathematically, namely via the so-called information transmission rate.

Yet, until now this quantity could only be computed approximately. AMOLF researchers Manuel Reinhardt and Pieter Rein ten Wolde, together with a collaborator from Vienna, have now developed a simulation technique that—for the first time—makes it possible to compute the information rate exactly for any system. The researchers have published their results in the journal Physical Review X.

To calculate the information rate exactly, the AMOLF researchers developed a novel simulation algorithm. It works by representing a complex physical system as an interconnected network that transmits the information via connections between its nodes. The researchers hypothesized that by looking at all the different paths the information can take through this network, it should be possible to obtain the information rate exactly.

Oct 28
2023

Memes, Genes, and Brain Viruses

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Oct 27
2023

New AI Model Counters Bias In Data With A DEI Lens

AI has exploded onto the scene in recent years, bringing both promise and peril. Systems like ChatGPT and Stable Diffusion showcase the tremendous potential of AI to enhance productivity and creativity. Yet they also reveal a dark reality: the algorithms often reflect the same systemic prejudices and societal biases present in their training data.

While the corporate world has quickly capitalized on integrating generative AI systems, many experts urge caution, considering the critical flaws in how AI represents diversity. Whether it’s text generators reinforcing stereotypes or facial recognition exhibiting racial bias, the ethical challenges cannot be ignored.

From generating text that furthers stereotypes to producing discriminatory facial recognition results, biased AI poses ethical and social challenges.

Oct 27
2023

AI-ready architecture doubles power with FeFETs

Hussam Amrouch has developed an AI-ready architecture that is twice as powerful as comparable in-memory computing approaches. As reported in the journal Nature Communications (“First demonstration of in-memory computing crossbar using multi-level Cell FeFET”), the professor at the Technical University of Munich (TUM) applies a new computational paradigm using special circuits known as ferroelectric field effect transistors (FeFETs). Within a few years, this could prove useful for generative AI, deep learning algorithms and robotic applications.

The new architecture enables both data storage and calculations to be carried out on the same transistors, boosting efficiency and reducing heat.

The chip performs at 885 TOPS/W, significantly outperforming current CMOS chips which operate in the range of 10–20 TOPS/W, making it ideal for applications like real-time drone calculations, generative AI, and deep learning algorithms.