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Category: information science – Page 116

Dr. Elica Kyoseva, Ph.D. — Quantum for Bio Program Director — Wellcome Leap

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.

Dr. Kyoseva completed her Ph.D. in Quantum Optics and Information, at Sofia University in Bulgaria, and then moved to the Center for Quantum Technologies in Singapore as a postdoc. Three years later, she established her own research group in Quantum Engineering at the Singapore University of Tech & Design and subsequently spent a year at MIT (Cambridge, USA) as a Research Fellow in the Nuclear Science and Engineering Department doing research on quantum control and engineering.

In 2016, Dr. Kyoseva was awarded a Marie Curie fellowship for research excellence by the European Commission with which she relocated to Tel Aviv, Israel and continued her research in robust control methods for Quantum Computing at Tel Aviv University. Since the beginning of 2020 she served as an Entrepreneur in Residence and Advisor at a venture capital firm and was instrumental for their investments in quantum computing startups. In September 2020, she took a senior role with Boehringer Ingelheim to develop applications of quantum algorithms to the drug discovery process working on the cutting edge of applied quantum computing technologies to improve the lives of both humans and animals.

Additionally to her scientific career, Dr. Kyoseva is very passionate about ending gender inequality in the STEM fields and served as a STEM Ambassador to the UN Women Singapore Committee for 2 years. Currently, she is the Managing Director for Israel of the global non-profit organization Girls in Tech and on the Advisory Board of She Quantum and works towards encouraging more girls and women to pursue a career in Quantum Computing.

Vectara aims to ground generative AI conversational search without hallucinations

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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.

Software offers new way to listen for signals from the stars

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 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 , standing out from the background of non-periodic signals, as well as using much less energy than a transmitter that is broadcasting continuously.

A software package to ease the use of neural radiance fields in robotics research

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.

A research team at Stanford University recently introduced NerfBridge, a new open-source software package for training NeRF algorithms that could ultimately enable their use in online robotics experiments, This package, introduced in a paper pre-published on arXiv, is designed to effectively bridge ROS (the operating system), a renowned software library for robotics applications, and Nerfstudio, an open-source library designed to train NeRFs in real-time.

Meta’s open-source speech AI recognizes over 4,000 spoken languages

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.

Will My Son’s Blood Make Me Younger?

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?

Plasma is the yellowish, liquid part of your blood. There is emerging evidence that plasma exchanges may offer various health benefits.

Nervous but excited, we travelled to a transfusion centre in Dallas Texas to make it happen.

🧪 WHAT IS BLUEPRINT
I’ve invested millions of dollars building the world’s leading anti-aging protocol, becoming the most measured human in history. Blueprint is an algorithm, built by science, that takes better care of me than I can myself.

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

Machine-learning program reveals genes responsible for sex-specific differences in Alzheimer’s disease progression

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 that influence AD risk separately in males and females,” Dr. Olivier Lichtarge, MD, Ph.D., professor of biochemistry and 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 involved in 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 using the evolutionary action (EA) score.

New data-driven algorithm can forecast the mortality risk for certain cardiac surgery patients

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.

New algorithm-backed tool offers accurate tracking for deforestation crisis

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.