Lifeboat Foundation

An interview with J. Storrs Hall, author of the epic book “Where is My Flying Car — A Memoir of Future Past”: “The book starts as an examination of the technical limitations of building flying cars and evolves into an investigation of the scientific, technological, and social roots of the economic…

J. Storrs Hall or Josh is an independent researcher and author.

Continue reading “Ep. 20: J. Storrs Hall — Bringing Back A Future Past With Flying Cars, Nano-Robots and Multi-Level Cities By Nurturing A Techno-Optimist Culture and a Unleashing Second Nuclear Age” »

It would be a tangible demonstration of Einstein’s famous E = mc^2 equation.

Large language models (LLMs) are advanced deep learning algorithms that can process written or spoken prompts and generate texts in response to these prompts. These models have recently become increasingly popular and are now helping many users to create summaries of long documents, gain inspiration for brand names, find quick answers to simple queries, and generate various other types of texts.

Researchers at the University of Georgia and Mayo Clinic recently set out to assess the biological knowledge and reasoning skills of different LLMs. Their paper, pre-published on the arXiv server, suggests that OpenAI’s model GPT-4 performs better than the other predominant LLMs on the market on reasoning biology problems.

“Our recent publication is a testament to the significant impact of AI on biological research,” Zhengliang Liu, co-author of the recent paper, told Tech Xplore. “This study was born out of the rapid adoption and evolution of LLMs, especially following the notable introduction of ChatGPT in November 2022. These advancements, perceived as critical steps towards Artificial General Intelligence (AGI), marked a shift from traditional biotechnological approaches to an AI-focused methodology in the realm of biology.”

The tool — dubbed ‘AI-Descartes’ by the researchers — aims to speed up scientific discovery by leveraging symbolic regression, which finds equations to fit data.

Given basic operators, such as addition, multiplication, and division, the systems can generate hundreds to millions of candidate equations, searching for the ones that most accurately describe the relationships in the data.

Using this technique, the AI tool has been able to re-discover, by itself, fundamental equations, including Kepler’s third law of planetary motion; Einstein’s relativistic time-dilation law, and Langmuir’s equation of gas adsorption.

Harvard’s breakthrough in quantum computing features a new logical quantum processor with 48 logical qubits, enabling large-scale algorithm execution on an error-corrected system. This development, led by Mikhail Lukin, represents a major advance towards practical, fault-tolerant quantum computers.

In quantum computing, a quantum bit or “qubit” is one unit of information, just like a binary bit in classical computing. For more than two decades, physicists and engineers have shown the world that quantum computing is, in principle, possible by manipulating quantum particles ­– be they atoms, ions or photons – to create physical qubits.

But successfully exploiting the weirdness of quantum mechanics for computation is more complicated than simply amassing a large-enough number of physical qubits, which are inherently unstable and prone to collapse out of their quantum states.

The release of Transformers has marked a significant advancement in the field of Artificial Intelligence (AI) and neural network topologies. Understanding the workings of these complex neural network architectures requires an understanding of transformers. What distinguishes transformers from conventional architectures is the concept of self-attention, which describes a transformer model’s capacity to focus on distinct segments of the input sequence during prediction. Self-attention greatly enhances the performance of transformers in real-world applications, including computer vision and Natural Language Processing (NLP).

In a recent study, researchers have provided a mathematical model that can be used to perceive Transformers as particle systems in interaction. The mathematical framework offers a methodical way to analyze Transformers’ internal operations. In an interacting particle system, the behavior of the individual particles influences that of the other parts, resulting in a complex network of interconnected systems.

The study explores the finding that Transformers can be thought of as flow maps on the space of probability measures. In this sense, transformers generate a mean-field interacting particle system in which every particle, called a token, follows the vector field flow defined by the empirical measure of all particles. The continuity equation governs the evolution of the empirical measure, and the long-term behavior of this system, which is typified by particle clustering, becomes an object of study.

That’s in large part due to these tools’ ability to churn out content at much faster rates than human writers — and at a fraction of the cost.

Given the biblical flood of bottom-shelf AI-generated content polluting the internet today, it’s clear that everyday internet users are not going to benefit.

However, some entrepreneurs are hellbent on making a buck by repurposing existing content, laundering it through an AI algorithm, and passing it off as their own.

Computer-generated holography (CGH) represents a cutting-edge technology that employs computer algorithms to dynamically reconstruct virtual objects. This technology has found extensive applications across diverse fields such as three-dimensional display, optical information storage and processing, entertainment, and encryption.

Despite the broad application spectrum of CGH, contemporary techniques predominantly rely on projection devices like spatial light modulators (SLMs) and digital micromirror devices (DMDs). These devices inherently face limitations in display capabilities, often resulting in narrow field-of-view and multilevel diffraction in projected images.

In recent developments, metasurfaces composed of an array of subwavelength nanostructures have demonstrated exceptional capabilities in modulating electromagnetic waves. By introducing abrupt changes to fundamental wave properties like amplitude and phase through nanostructuring at subwavelength scales, metasurfaces enable modulation effects that are challenging to achieve with traditional devices.

As object identification and three-dimensional (3D) reconstruction techniques become essential in various reverse engineering, artificial intelligence, medical diagnosis, and industrial production fields, there is an increasing focus on seeking vastly efficient, faster speed, and more integrated methods that can simplify processing.

In the current field of object identification and 3D reconstruction, extracting sample contour information is primarily accomplished by various computer algorithms. Traditional computer processors suffer from multiple constraints, such as high-power consumption, low-speed operation, and complex algorithms. In this regard, there has recently been growing attention in searching for alternative optical methods to perform those techniques.

The development of optical computing theory and image processing has provided a more complete theoretical basis for object identification and 3D reconstruction techniques. Optical methods have received more attention as an alternative paradigm than traditional mechanisms in recent years due to their enormous advantages of ultra-fast operation speed, high integration, and low latency.