Lifeboat Foundation

On Monday, Apple is more than likely going to reveal its long-awaited augmented or mixed reality Reality Pro headset during the keynote of its annual WWDC developer conference in California. It’s an announcement that has been tipped or teased for years now, and reporting on the topic has suggested that at various times, the project has been subject to delays, internal skepticism and debate, technical challenges and more. Leaving anything within Apple’s sphere of influence aside, the world’s overall attitude toward AR and VR has shifted considerably — from optimism, to skepticism.

Part of that trajectory is just the natural progression of any major tech hype cycle, and you could easily argue that the time to make the most significant impact in any such cycle is after the spike of undue optimism and energy has subsided. But in the case of AR and VR, we’ve actually already seen some of the tech giants with the deepest pockets take their best shots and come up wanting — not for lack of trying, but because of limitations in terms of what’s possible even at the bleeding edge of available tech. Some of those limits might actually be endemic to AR and VR, too, because of variances in the human side of the equation required to make mixed reality magic happen.

The virtual elephant in the room is, of course, Meta. The name itself pretty much sums up the situation: Facebook founder Mark Zuckerberg read a bad book and decided that VR was the inevitable end state of human endeavor — the mobile moment he essentially missed out on, but even bigger and better. Zuckerberg grew enamored by his delusion, first acquiring crowdfunded VR darling Oculus, then eventually commandeering the sobriquet for a shared virtual universe from the dystopian predictions of a better book and renaming all of Facebook after it.

Perfect recall, computational wizardry and rapier wit: That’s the brain we all want, but how does one design such a brain? The real thing is comprised of ~80 billion neurons that coordinate with one another through tens of thousands of connections in the form of synapses. The human brain has no centralized processor, the way a standard laptop does.

Instead, many calculations are run in parallel, and outcomes are compared. While the operating principles of the human brain are not fully understood, existing mathematical algorithms can be used to rework deep learning principles into systems more like a human brain would. This brain-inspired computing paradigm—spiking neural networks (SNN)—provides a computing architecture well-aligned with the potential advantages of systems using both optical and electronic components.

In SNNs, information is processed in the form of spikes or action potentials, which are the electrical impulses that occur in real neurons when they fire. One of their key features is that they use asynchronous processing, meaning that spikes are processed as they occur in time, rather than being processed in a batch like in traditional neural networks. This allows SNNs to react quickly to changes in their inputs, and to perform certain types of computations more efficiently than traditional neural networks.

Is the Quantum for Bio Program Director, at Wellcome Leap (https://wellcomeleap.org/our-team/elicakyoseva/), a $40M +$10M program focused on identifying, developing, and demonstrating biology and healthcare applications that will benefit from the quantum computers expected to emerge in the next 3–5 years.

Wellcome Leap was established with $300 million in initial funding from the Wellcome Trust, the UK charitable foundation, to accelerate discovery and innovation for the benefit of human health, focusing on build bold, unconventional programs and fund them at scale—specifically programs that target global human health challenges, with the goal of achieving breakthrough scientific and technological solutions.

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Vectara is continuing to grow as an AI powered conversational search platform with new capabilities announced today that aim to improve generative AI for business data.

The Santa Clara, Calif.- based startup emerged from stealth in Oct. 2022, led by the former CTO and founder of big data vendor Cloudera. Vectara originally branded its platform as a neural search-as-a-service technology. This approach combines AI-based large language models (LLMs), natural language processing (NLP), data integration pipelines and vector techniques to create a neural network that can be optimized for search.

The Breakthrough Listen Investigation for Periodic Spectral Signals (BLIPSS), led by Akshay Suresh, Cornell doctoral candidate in astronomy, is pioneering a search for periodic signals emanating from the core of our galaxy, the Milky Way. The research aims to detect repetitive patterns, a way to search for extraterrestrial intelligence (SETI) within our cosmic neighborhood.

The researchers developed software based on a Fast Folding Algorithm (FFA), an efficient search method offering enhanced sensitivity to periodic sequences of narrow pulses. Their paper, “A 4–8 GHz Galactic Center Search for Periodic Technosignatures,” was published May 30 in The Astronomical Journal.

Pulsars—rapidly rotating neutron stars that sweep beams of radio energy across the Earth—are natural astrophysical objects that generate periodic signals but humans also use directed periodic transmissions for a variety of applications, including radar. Such signals would be a good way to get someone’s attention across interstellar space, standing out from the background of non-periodic signals, as well as using much less energy than a transmitter that is broadcasting continuously.

Neural radiance fields (NeRFs) are advanced machine learning techniques that can generate three-dimensional (3D) representations of objects or environments from two-dimensional (2D) images. As these techniques can model complex real-world environments realistically and in detail, they could greatly support robotics research.

Most existing datasets and platforms for training NeRFs, however, are designed to be used offline, as they require the completion of a pose optimization step that significantly delays the creation of photo realistic representations. This has so far prevented most roboticists from using these techniques to test their algorithms on physical robots in real-time.

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Researchers in the US have developed a machine learning algorithm that accurately reconstructs the s.

Meta has created an AI language model that (in a refreshing change of pace) isn’t a ChatGPT clone. The company’s Massively Multilingual Speech (MMS) project can recognize over 4,000 spoken languages and produce speech (text-to-speech) in over 1,100. Like most of its other publicly announced AI projects, Meta is open-sourcing MMS today to help preserve language diversity and encourage researchers to build on its foundation. “Today, we are publicly sharing our models and code so that others in the research community can build upon our work,” the company wrote. “Through this work, we hope to make a small contribution to preserve the incredible language diversity of the world.”

Speech recognition and text-to-speech models typically require training on thousands of hours of audio with accompanying transcription labels. (Labels are crucial to machine learning, allowing the algorithms to correctly categorize and “understand” the data.) But for languages that aren’t widely used in industrialized nations — many of which are in danger of disappearing in the coming decades — “this data simply does not exist,” as Meta puts it.

Meta used an unconventional approach to collecting audio data: tapping into audio recordings of translated religious texts. “We turned to religious texts, such as the Bible, that have been translated in many different languages and whose translations have been widely studied for text-based language translation research,” the company said. “These translations have publicly available audio recordings of people reading these texts in different languages.” Incorporating the unlabeled recordings of the Bible and similar texts, Meta’s researchers increased the model’s available languages to over 4,000.

At Blueprint we’ve explored and evaluated hundreds of anti-aging therapies.

Recently, we had a daring idea: what if my father, son and I completed the world’s first ever multi-generational plasma exchange?

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