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Mar 3, 2024
Adamlui/chatgpt-infinity: ∞ Generate endless answers from all-knowing ChatGPT (in any language!)
Posted by Quinn Sena in category: futurism
Infinite chat solver for chat gpt.
∞ Generate endless answers from all-knowing ChatGPT (in any language!) — adamlui/chatgpt-infinity.
Mar 3, 2024
Suddenly, It Looks Like We’re in a Golden Age for Medicine
Posted by Shubham Ghosh Roy in categories: biotech/medical, innovation
We may be on the cusp of an era of astonishing innovation — the limits of which aren’t even clear yet.
Mar 3, 2024
A Persistent Lightning Mystery Has Finally Been Solved
Posted by Genevieve Klien in category: climatology
Mar 3, 2024
AI vs. Cancer: A Game-Changer!
Posted by Dan Breeden in categories: biotech/medical, robotics/AI
MIT and Dana-Farber Cancer Institute have teamed up to create an AI model that CRACKS the code of mysterious cancer origins! No more guesswork-this model predicts where tumors come from with up to 95% accuracy. For more insight, visit https://www.channelchek.com #Cancer #CancerBreakthrough #AIinMedicine #MedicalScience #BioTech #FutureOfHealthcare #FightCancer #HealthTech #CancerResearch #PrecisionMedicine
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Continue reading “The 5 Possible Fates of Humanity Explained” »
Mar 3, 2024
Major discovery in the genetics of Down syndrome
Posted by Shubham Ghosh Roy in categories: biological, genetics, neuroscience
Researchers at CHU Sainte-Justine and Université de Montréal have discovered a new mechanism involved in the expression of Down syndrome, one of the main causes of intellectual disability and congenital heart defects in children. The study’s findings were published today in Current Biology.
Down syndrome (SD), also called trisomy 21 syndrome, is a genetic condition that affects approximately one in every 800 children born in Canada. In these individuals, many genes are expressed abnormally at the same time, making it difficult to determine which genes contribute to which differences.
Professor Jannic Boehm’s research team focused on RCAN1, a gene that is overexpressed in the brains of fetuses with Down syndrome. The team’s work provides insights into how the gene influences the way the condition manifests itself.
Mar 3, 2024
New research shows how child-like language learning is possible using AI tools
Posted by Dan Breeden in categories: internet, robotics/AI
AI systems, such as GPT-4, can now learn and use human language, but they learn from astronomical amounts of language input—much more than children receive when learning how to understand and speak a language. The best AI systems train on text with a word count in the trillions, whereas children receive just millions per year.
Due to this enormous data gap, researchers have been skeptical that recent AI advances can tell us much about human learning and development. An ideal test for demonstrating a connection would involve training an AI model, not on massive data from the web, but on only the input that a single child receives. What would the model be able to learn then?
Continue reading “New research shows how child-like language learning is possible using AI tools” »
Mar 3, 2024
Using large language models to accurately analyze doctors’ notes
Posted by Dan Breeden in categories: biotech/medical, health, robotics/AI
The amount of digital data available is greater than ever before, including in health care, where doctors’ notes are routinely entered into electronic health record systems. Manually reviewing, analyzing, and sorting all these notes requires a vast amount of time and effort, which is exactly why computer scientists have developed artificial intelligence and machine learning techniques to infer medical conditions, demographic traits, and other key information from this written text.
However, safety concerns limit the deployment of such models in practice. One key challenge is that the medical notes used to train and validate these models may differ greatly across hospitals, providers, and time. As a result, models trained at one hospital may not perform reliably when they’re deployed elsewhere.
Previous seminal works by Johns Hopkins University’s Suchi Saria—an associate professor of computer science at the Whiting School of Engineering—and researchers from other top institutions recognize these “dataset shifts” as a major concern in the safety of AI deployment.