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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.

Continue reading “A software package to ease the use of neural radiance fields in robotics research” »

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?

Continue reading “Will My Son’s Blood Make Me Younger?” »

Alzheimer’s disease (AD) is a complex neurodegenerative illness with genetic and environmental origins. Females experience faster cognitive decline and cerebral atrophy than males, while males have greater mortality rates. Using a new machine-learning method they developed called “Evolutionary Action Machine Learning (EAML),” researchers at Baylor College of Medicine and the Jan and Dan Duncan Neurological Research Institute (Duncan NRI) at Texas Children’s Hospital have discovered sex-specific genes and molecular pathways that contribute to the development and progression of this condition. The study was published in Nature Communications.

“We have developed a unique machine-learning software that uses an advanced computational predictive metric called the evolutionary action (EA) score as a feature to identify genetic factors that influence AD risk separately in males and females,” Dr. Olivier Lichtarge, MD, Ph.D., professor of biochemistry and molecular biology at Baylor College of Medicine, said. “This approach lets us exploit a massive amount of evolutionary data efficiently, so we can now probe with greater accuracy smaller cohorts and identify genes involved in sex-specific differences in AD.”

EAML is an ensemble computational approach that includes nine machine learning algorithms to analyze the functional impact of non-synonymous coding variants, defined as DNA mutations that affect the structure and function of the resulting protein, and estimates their deleterious effect on biological processes using the evolutionary action (EA) score.

A machine learning-based method developed by a Mount Sinai research team allows medical facilities to forecast the mortality risk for certain cardiac surgery patients. The new method is the first institution-specific model for determining the risk of a cardiac patient before surgery and was developed using vast amounts of Electronic Health Data (EHR).

Comparing the data-driven approach to the current population-derived models reveals a considerable performance improvement.

Approximately 27 football fields’ worth of forests are lost every minute around the globe. That’s a massive annual loss of 15 billion trees.

Scientists have unveiled an innovative and comprehensive strategy to effectively detect and track large-scale forest disturbances, according to a new study published in the Journal of Remo.

Approximately 27 football fields’ worth of forests are lost every minute around the globe, resulting in a massive annual loss of 15 billion trees, according to the WWF. Given this concerning context, the new forest monitoring approach could be a valuable tool for effectively monitoring and managing forests as they undergo changes over time.