Lifeboat Foundation

Why the recent surge in jaw-dropping announcements? Why are neutral atoms seeming to leapfrog other qubit modalities? Keep reading to find out.

The table below highlights the companies working to make Quantum Computers using neutral atoms as qubits:

And as an added feature I am writing this post to be “entangled” with the posts of Brian Siegelwax, a respected colleague and quantum algorithm designer. My focus will be on the hardware and corporate details about the companies involved, while Brian’s focus will be on actual implementation of the platforms and what it is like to program on their devices. Unfortunately, most of the systems created by the companies noted in this post are not yet available (other than QuEra’s), so I will update this post along with the applicable hot links to Brian’s companion articles, as they become available.

Computers and information technologies were once hailed as a revolution in education. Their benefits are undeniable. They can provide students with far more information than a mere textbook. They can make educational resources more flexible, tailored to individual needs, and they can render interactions between students, parents, and teachers fast and convenient. And what would schools have done during the pandemic lockdowns without video conferencing?

The advent of AI chatbots and large language models such as OpenAI’s ChatGPT, launched last November, create even more new opportunities. They can give students practice questions and answers as well as feedback, and assess their work, lightening the load on teachers. Their interactive nature is more motivating to students than the imprecise and often confusing information dumps elicited by Google searches, and they can address specific questions.

The algorithm has no sense that “love” and “embrace” are semantically related.

Artificial neural networks that are inspired by natural nerve circuits in the human body give primates faster, more accurate control of brain-controlled prosthetic hands and fingers, researchers at the University of Michigan have shown. The finding could lead to more natural control over advanced prostheses for those dealing with the loss of a limb or paralysis.

The team of engineers and doctors found that a feed-forward neural network improved peak finger velocity by 45% during control of robotic fingers when compared to traditional algorithms not using neural networks. This overturned an assumption that more complex neural networks, like those used in other fields of machine learning, would be needed to achieve this level of performance improvement.

Continue reading “Simple neural networks outperform more complex systems for controlling robotic prosthetics” »

Making predictions is never easy, but it is agreed that cryptography will be altered by the advent of quantum computers.

Thirteen, 53, and 433. That’s the size of quantum computers.

Hh5800/iStock.

Continue reading “Quantum computers: How scientists can shield against cyber attacks” »

Check out all the on-demand sessions from the Intelligent Security Summit here.

Classical machine learning (ML) algorithms have proven to be powerful tools for a wide range of tasks, including image and speech recognition, natural language processing (NLP) and predictive modeling. However, classical algorithms are limited by the constraints of classical computing and can struggle to process large and complex datasets or to achieve high levels of accuracy and precision.

Enter quantum machine learning (QML).

This might be important. It might not be over for metformin just yet though as a mice study showed that rapamycin combined with metformin removed each other’s side effects.

If you are a non-diabetic who takes metformin for longevity, I highly recommend you stop immediately. Hear me out, and at the end of the video I’ll share what to do instead.

Continue reading “Metformin For Anti-Aging: Disproved & Dangerous!” »

Scientists at the University of Cambridge have successfully trialed an artificial pancreas for use by patients living with type 2 diabetes. The device – powered by an algorithm developed at the University of Cambridge – doubled the amount of time patients were in the target range for glucose compared to standard treatment and halved the time spent experiencing high glucose levels.

Around 415 million people worldwide are estimated to be living with type 2 diabetes, which costs around $760 billion in annual global health expenditure. According to Diabetes UK, more than 4.9 million people have diabetes in the UK alone, of whom 90% have type 2 diabetes, and this is estimated to cost the NHS £10 billion per year.

“Many people with type 2 diabetes struggle to manage their blood sugar levels using the currently available treatments, such as insulin injections. The artificial pancreas can provide a safe and effective approach to help them, and the technology is simple to use and can be implemented safely at home.” —

A common idea that our creativity is what makes us uniquely human has shaped society but strides of progress made in the domain of Generative Artificial Intelligence question this very notion. Generative AI is an emerging field that involves the creation of original content or data using machine learning algorithms.

As we think about a future where humans and AI partner in iterative creative cycles, we consider how generative AI could impact current businesses and possibly create new ones. Up until recently, machines were relegated to analysis and cognitive roles, but today algorithms are improving at generating original content. These technologies are iterative in principle, one is built on top of the last one, and each new iteration enhances the algorithm and increases the potential for discovery exponentially.

The technology presents itself as a more refined and mature breed of AI that has sent investors into a frenzy and among all this emerges a clear market leader — OpenAI. Its flagship products-ChatGPT and DALL-E proved to be industry disruptors and brought generative AI tools to the masses. DALL-E allows people to generate and edit photo-realistic images simply by describing what they want to see, while ChatGPT does the same through a text medium.

The early 20^th century saw the advent of quantum mechanics to describe the properties of small particles, such as electrons or atoms. Schrödinger’s equation in quantum mechanics can successfully predict the electronic structure of atoms or molecules. However, the “duality” of matter, referring to the dual “particle” and “wave” nature of electrons, remained a controversial issue. Physicists use a complex wavefunction to represent the wave nature of an electron.

“Complex” numbers are those that have both “real” and “imaginary” parts—the ratio of which is referred to as the “phase.” However, all directly measurable quantities must be “real”. This leads to the following challenge: when the electron hits a detector, the “complex” phase information of the wavefunction disappears, leaving only the square of the amplitude of the wavefunction (a “real” value) to be recorded. This means that electrons are detected only as particles, which makes it difficult to explain their dual properties in atoms.

The ensuing century witnessed a new, evolving era of physics, namely, attosecond physics. The attosecond is a very short time scale, a billionth of a billionth of a second. “Attosecond physics opens a way to measure the phase of electrons. Achieving attosecond time-resolution, electron dynamics can be observed while freezing molecular motion,” explains Professor Hiromichi Niikura from the Department of Applied Physics, Waseda University, Japan, who, along with Professor D. M. Villeneuve—a principal research scientist at the Joint Attosecond Science Laboratory, National Research Council, and adjunct professor at University of Ottawa—pioneered the field of attosecond physics.