Methods for global navigation satellite system fault detection using Euclidean Distance Matrices have been presented recently in the literature. Published methods define a test statistic in terms of eigenvalues of a certain matrix, but the distribution of the test statistic was not known, which presented a barrier to practical implementation. This document was a personal correspondence from Beatty to Derek Knowles. It includes a brief derivation of the distribution of the test statistic and a representative case showing that the theoretical distribution closely matches a simulated empirical distribution.

As compelling as the leading large-scale language models may be, the fact remains that only the largest companies have the resources to actually deploy and train them at meaningful scale. For enterprises eager to leverage AI to a competitive advantage, a cheaper, pared-down alternative may be a better fit, especially if it can be tuned to particular industries or domains. That's where an emerging set of AI startups hoping to carve out a niche: by building sparse, tailored models that, maybe not as powerful as GPT-3, are good enough for enterprise use cases and run on hardware that ditches expensive high-bandwidth memory (HBM) for commodity DDR. German AI startup Aleph Alpha is one such example. Founded in 2019, the Heidelberg, Germany-based company's Luminous natural-language model boasts many of the same headline-grabbing features as OpenAI's GPT-3: copywriting, classification, summarization, and translation, to name a few.

The 3D stacking of chips has been the subject of much speculation and innovation in the past decade, and we will be the first to admit that we have been mostly thinking about this as a way to cram more capacity into a given compute engine while at the same time getting components closer together along the Z axis and not just working in 2D anymore down on the X and Y axes. It was extremely interesting to see, then, the 3D wafer-on-wafer stacking that AI chip and system upstart GraphCore has been working on with Taiwan Semiconductor Manufacturing Co had nothing to do making logic circuits more dense within a socket. This will happen over time, of course, but the 3D wafer stacking that GraphCore and TSMC have been exploring together and are delivering in the third generation "Bow" GraphCore IPU – the systems based on them bear the same nickname – is about creating a power delivery die that is bonded to the bottom of the existing compute die. The effect of this innovation is that GraphCore can get a more even power supply to the IPU, and therefore it can drop the voltage on its circuits and therefore increase the clock frequency while at the same time burning less power. The grief and cost of doing this power supply wafer and stacking the IPU wafer on top are outweighed by the performance and thermal benefits on the IPU, and therefore GraphCore and its customers come out ahead on the innovation curve.

Despite all the stories about big companies bailing out of CES 2022 amidst the latest surge in COVID-19 cases, the consumer electronics show in Las Vegas is still the place to be for robots, autonomous vehicles, smart gadgets, and their inventors -- an opportunity to take stock of what's required to build practical machine intelligence into a consumer product. OrCam and Sonatus are among the companies no longer planning to travel to Las Vegas or announce products at CES, and it's possible some of the other vendors VentureBeat interviewed in advance of the event will also be no-shows. Big names like Microsoft, Google, Intel, Amazon, and T-Mobile backed out in recent weeks. Augmented reality, virtual reality, and the metaverse will be topics of discussion that will have to proceed without Meta (the company formerly known as Facebook). Automotive tech will be a big theme of the event, but General Motors, BMW, and Mercedes-Benz decided not to make the drive (GM's all-digital presence is still supposed to include a video keynote from CEO Mary Barra on Wednesday).

The Knowles AISonic IA8201 kit creates a single tool to streamline design, development, and testing of technologies for the IoT and Industry4.0. The kit is built around the Knowles AISonic IA8201 Audio Edge Processor with two Tensilica-based, audio-centric DSP cores; one for high-power compute and AI/ML applications, and the other for very low-power, always-on processing of sensor inputs. The IA8201 has 1MB of RAM on-chip that allows for high bandwidth processing of advanced, always-on contextually aware ML use-cases and memory to support multiple algorithms. Using the Knowles open DSP platform, the kit includes a library of onboard audio algorithms and AI/ML libraries. Far field audio applications can be built using the available ultra-low-power voice wake, beamforming, custom keywords, and background noise elimination algorithms from Knowles algorithm partners such as Amazon Alexa, Sensory, Retune, and Alango to open up the design possibilities and ensure the freedom needed to support a wide range of voice and audio customization.

An initiative by the U.S. military looks to develop what one researcher is calling'Google Earth on steroids' that maps entire landscapes, helping to simulate environments and train soldiers. In a report from National Defense, one researcher working on the project revealed that the system will be granular enough to map the inside of buildings and eventually entire cities which can then be used in simulated training exercises. The military hopes to inform the creation of these realistic simulations, what they call Simulated Training Environments (STE), by building a comprehensive and highly detailed 3D map of locations around the globe -- an initiative dubbed One World Terrain. An initiative by the U.S. military looks to develop what one researcher is calling ' Google Earth on steroids' that maps entire landscapes. Simulations could help the U.S. military train soldiers and glean useful data in the field While the project may sound like a developer's nightmare, recent advances in drone technology and databases of satellite imagery have brought the project firmly into reality.

In the middle of the historic city of Bristol in England, about 150 engineers are currently designing the most sophisticated computer AI chip in the world. The "Colossus" has 1216 processors fitted on a chip characterized by the size of a postage stamp. Designed specifically for artificial intelligence (AI) applications, the AI chip draws its name from the computer that was used by cryptographers at Bletchley Park during World War II. "[Colossus] was all top-secret for decades after the war, so the Americans thought they invented everything first. Now it is clear to the world that they didn't," claimed Simon Knowles, the inventor of the novel AI chip.

What it is: Self-described by its MIT creators as "the world's first cellular health product informed by genomics," Basis by Elysium Health is a mail-order daily supplement that's been making waves in the rapidly emerging field of life-extension science. The claim is not immortality but simply the possibility of extending one's vital years, by putting off of cancer, heart disease, diabetes, Alzheimer's, and other afflictions of age. Hard to say: Though the supplement is some 25 years in the making, it's been human-tested for less than five, including on Elysium Health cofounder Leonard Guarente, who also serves as the director of MIT's center for aging research. That said, the company has an impressive roster of Nobel Prize winners on its scientific advisory board and has attracted more than $25 million in funding. What's the sell: Two pills a day purportedly target DNA repair, cellular detoxification, energy production, and protein function by converting nicotinamide riboside into nicotinamide adenine dinucleotide (NAD), a coenzyme critical to metabolism that diminishes with age.

"What we heard universally was that current hardware was holding developers back," says Nigel Toon, co-founder of Graphcore, the Bristol-based startup behind a new chip to help speed up the process-hogging, resource-intensive deployment of AI. By using cloud computing and vast datasets, some neural networks function sufficiently well. The more powerful AI systems in development, however, struggle to process complex rapid-fire calculations at speed if using computer processing units (CPUs) which work sequentially. Latency, in other words, has slowed. "For 70 years we have programmed computers to work on instructions step-by-step," says Toon, 54.

Knowles said researchers then pulled the charts of those individuals the algorithm identified as likely having FH and found the system performed about as well as a human in diagnosing patients. "You could imagine this happening for … many potentially important conditions, not just FH," Knowles said. At the Cleveland Clinic researchers and doctors are using machine learning to predict the wellbeing of certain patients. "We're doing things to help us identify high-risk patients," explained Cleveland Clinic's Executive director of enterprise information management and analytics Chris Donovan. "So what patients are at risk of being admitted, what patients are at risk of deterioration in their care, or in their clinical condition and how do we intervene on those patients proactively."