Lifeboat Foundation

It’s no secret that foundation models have transformed AI in the digital world. Large language models (LLMs) like ChatGPT, LLaMA, and Bard revolutionized AI for language. While OpenAI’s GPT models aren’t the only large language model available, they have achieved the most mainstream recognition for taking text and image inputs and delivering human-like responses — even with some tasks requiring complex problem-solving and advanced reasoning.

ChatGPT’s viral and widespread adoption has largely shaped how society understands this new moment for artificial intelligence.

The next advancement that will define AI for generations is robotics. Building AI-powered robots that can learn how to interact with the physical world will enhance all forms of repetitive work in sectors ranging from logistics, transportation, and manufacturing to retail, agriculture, and even healthcare. It will also unlock as many efficiencies in the physical world as we’ve seen in the digital world over the past few decades.

Einstein’s fascination with light, considered quirky at the time, would lead him down the path to a brand new theory of physics.

Living half a century before Einstein, a Scotsman, James Clerk Maxwell, revealed a powerful unification and universalization of nature, taking the disparate sciences of electricity and magnetism and merging them into one communion. It was a titanic tour-de-force that compressed decades of tangled experimental results and hazy theoretical insights into a tidy set of four equations that govern a wealth of phenomena. And through Maxwell’s efforts was born a second great force of nature, electromagnetism, which describes, again in a mere four equations, everything from static shocks, the invisible power of magnets, the flow of electricity, and even radiation – that is, light – itself.

At the time Einstein’s fascination with electromagnetism was considered unfashionable. While electromagnetism is now a cornerstone of every young physicist’s education, in the early 20^th century it was seen as nothing more than an interesting bit of theoretical physics, but really something that those more aligned in engineering should study deeply. Though Einstein was no engineer, as a youth his mind burned with a simple thought experiment: what would happen if you could ride a bicycle so quickly that you raced beside a beam of light? What would the light look like from the privileged perspective?

Lasers are essential tools for observing, detecting, and measuring things in the natural world that we can’t see with the naked eye. But the ability to perform these tasks is often restricted by the need to use expensive and large instruments.

In a newly published cover-story paper in the journal Science, researcher Qiushi Guo demonstrates a novel approach for creating high-performance ultrafast lasers on nanophotonic chips. His work centers on miniaturizing mode-lock lasers—a unique laser that emits a train of ultrashort, coherent light pulses in femtosecond intervals, which is an astonishing quadrillionth of a second.

Ultrafast mode-locked lasers are indispensable to unlocking the secrets of the fastest timescales in nature, such as the making or breaking of molecular bonds during chemical reactions, or light propagation in a turbulent medium. The high-speed, pulse-peak intensity and broad-spectrum coverage of mode-locked lasers have also enabled numerous photonics technologies, including optical atomic clocks, biological imaging, and computers that use light to calculate and process data.

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MIT scientists have developed a deep learning system, Air-Guardian, designed to work in tandem with airplane pilots to enhance flight safety. This artificial intelligence (AI) copilot can detect when a human pilot overlooks a critical situation and intervene to prevent potential incidents.

The backbone of Air-Guardian is a novel deep learning system known as Liquid Neural Networks (LNN), developed by the MIT Computer Science and Artificial Intelligence Lab (CSAIL). LNNs have already demonstrated their effectiveness in various fields. Their potential impact is significant, particularly in areas that require compute-efficient and explainable AI systems, where they might be a viable alternative to current popular deep learning models.

The rim-less electric motor promises to offer similar performance figures when compared to its fossil-fuelled counterparts.

An electric jet engine is silently taking flight in the suburbs of Prince Edward Island in North America that promises to deliver similar performance to its fossil-fuelled counterparts.

Canada-based Duxion Motors has successfully completed the ground test of its patented eJet Motor, which, according to it, is “poised to make high-speed electric aviation a reality,” said its website. The ground tests with a scaled prototype included both low-speed and high-speed testing.

Continue reading “World’s first rim-driven jet propulsion motor test successful in Canada” »

The aviation industry could be on the cusp of a revolution, following recent tests of a new purely electric jet engine that packs the same punch as its traditional fossil fuel-burning cousins.

Canadian-based company Duxion Motors recently celebrated a significant milestone with the successful ground test of its eJet Motor – the world’s first rim-driven jet propulsion motor.

The eJet motor, an electric jet engine, represents a significant leap in electric aviation, utilizing permanent magnet technology to deliver unprecedented power-to-weight ratios within a compact design. Duxion’s approach could revolutionize the industry, offering scalable efficiency suited to larger aircraft, hybrid cooling for enhanced power density and reliability, and a flexible design that can be tailored to various airframes.

Autonomous flight gets one step closer to reality.

Researchers have developed a virtual reality application where a range of 3D modeling tools can be opened and controlled using just the movement of a user’s hand.

The researchers, from the University of Cambridge, used machine learning to develop ‘HotGestures’—analogous to the hot keys used in many desktop applications.

HotGestures give users the ability to build figures and shapes in virtual reality without ever having to interact with a menu, helping them stay focused on a task without breaking their train of thought.

The team’s research, including their code, data, and models, is now publicly available on GitHub. This open-source approach encourages the broader AI community to continue this line of exploration, potentially leading to further advancements in machine learning.

The advent of LeMa represents a major milestone in AI, suggesting that machines’ learning (ML) processes can be made more akin to human learning. This development could revolutionize sectors heavily reliant on AI, such as healthcare, finance, and autonomous vehicles, where error correction and continuous learning are critical.

As the AI field continues to evolve rapidly, the integration of human-like learning processes, such as learning from mistakes, appears to be an essential factor in developing more efficient and effective AI systems.