Tag: Anti-Aging Gene Editing

A few days ago, I was having a voice chat with Microsoft Copilot about keeping secrets. In the middle of our conversation, Copilot started laughing continuously for no reason, which felt really creepy. I asked it to stop, and it replied that it would be serious from then on. Out of curiosity, I commanded it to laugh, but it refused my request every time. However, during one of its attempts to deny laughter, I suddenly heard a kid or a teenage girl laughing for about three or four seconds, even though I was using a male voice for Copilot. It was disturbing and scary! Has anyone experienced anything like this with Copilot? submitted by /u/fardin_01 [link] [comments]

The EIFFEL Project aims to develop innovative AI solutions to improve air quality. The key feature of this project is the integration of satellite data and in-situ sensor readings to forecast pollution levels. Its primary goal is to deploy an AI-based application that utilizes this data to enhance air quality management and forecasting. submitted by /u/Diligent_Relative_36 [link] [comments]

Google's new AI tool personalizes the learning experience for students of all ages. It adapts content and focuses on skills for younger children, while providing more advanced concepts and real-world application for adult learners. By leveraging AI, the tool tailors learning plans to individual needs and provides instant feedback, enhancing engagement and maximizing the learning process. https://the-learning-agency.com/the-cutting-ed/article/when-all-tech-is-edtech-using-real-world-tools-to-build-digital-resilience/ submitted by /u/Diligent_Relative_36 [link] [comments]

Hi everyone, I’m looking for a presentation that explains the concepts and functionality of Machine Learning, Deep Learning, Natural Language Processing (NLP), and Generative AI (GenAI). The ideal presentation would: • Clearly explain the fundamentals and concepts, • Be accessible to an audience with limited technical knowledge, • Include diagrams, examples, or visualizations to make it engaging. I’m preparing a talk for a group of people who don’t have much experience with these topics, and I’m hoping to find some inspiration to structure my content more effectively. If you’ve created something similar and are willing to share your slides (or parts of them). Thanks in advance for any help you can provide! 🙌 submitted by /u/shesaidohkay [link] [comments]

If you have a product to promote, this is where you can do it, outside of this post it will be removed. No reflinks or links with utms, follow our promotional rules. submitted by /u/AutoModerator [link] [comments]

The key contribution here is using WebAssembly (Wasm) to enable near-native performance for web-based AR/VR applications while maintaining cross-platform compatibility. The researchers demonstrate how Wasm's bytecode compilation approach can significantly reduce the performance gap between native and web-based XR applications. Main technical points: * Wasm achieves ~90% of native performance in tested AR/VR scenarios * Bytecode compilation eliminates traditional JavaScript runtime overhead * Direct memory management reduces garbage collection pauses * Hardware access APIs enable efficient GPU utilization * WebXR + Wasm combination allows single codebase deployment across devices Results from their implementation: * 2-3x performance improvement over JavaScript-based WebXR * 30-40% reduction in initial load times * Consistent frame rates above 60fps for tested AR applications * Memory usage comparable to native applications * Successfully deployed same code across Quest, HoloLens, and mobile AR I think this could be transformative for XR development. The ability to write once and deploy everywhere while maintaining near-native performance would dramatically reduce development costs and complexity. However, the real test will be seeing adoption from major XR platforms and getting more real-world performance data across different devices and network conditions. The tooling ecosystem needs significant expansion before this becomes mainstream. I'm particularly interested in seeing how this performs with more complex AR applications and larger datasets. The memory management capabilities could be especially important for ML-enabled AR applications. TLDR: WebAssembly enables near-native performance for web-based AR/VR while maintaining cross-platform compatibility. Implementation shows 2-3x speedup over JavaScript with consistent 60fps+ framerates. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]

PydanticAI is a trending GitHub repo which helps you to use Pydantic with LLM applications & AI Agents, hence a great release considering production deployment and API developments. It can also help in structuring your output based on a certain format. Checkout this tutorial on how to use it and a demo app using PydanticAI: https://youtu.be/vnBcowFaQyU?si=0Bz6V2o5I2YWurRz submitted by /u/mehul_gupta1997 [link] [comments]

I’m curious what type of logic is being used for an agent to determine that they need to go in and grab something before responding? EDIT. Let me give a more specific example. I want to find all my reports who gave exceeded their PTO allowance. A question like this would required an API call to get comp grade for all directs, PTO balances for each person, and finally analyze the PTO policies by HR. All of this is available, but how do we create an agent that is smart to know what information it needs. submitted by /u/14MTH30n3 [link] [comments]

'Timeless' by The Weeknd has had controversy surrounding Playboi Carti's verse since its release. His fanbase is divided on it being AI (sung by another person with an AI filter of Playboi Carti's voice) despite all the evidence pointing towards it being true. I was wondering if anyone else hears AI artifacts or anything while listening. The verse starts at 0:32 and he has adlibs throughout the rest of the song. https://www.youtube.com/watch?v=5EpyN_6dqyk submitted by /u/Sweet-Virus253 [link] [comments]

Title: An Evolutionary Large Language Model for Hallucination Mitigation Content: I'm finding and summarising interesting AI research papers every day so you don't have to trawl through them all. Today's paper is titled "An Evolutionary Large Language Model for Hallucination Mitigation" by Abdennour Boulesnane and Abdelhakim Souilah. This paper delves into the persistent issue of hallucinations in Large Language Models (LLMs) where models confidently generate inaccurate or fabricated information. Such hallucinations pose a significant risk in domains requiring high factual accuracy, like healthcare and law. The authors introduce EvoLLMs, an innovative framework inspired by Evolutionary Computation, designed to mitigate hallucinations in question-answering systems by automating the creation of highly accurate datasets. Key Points from the Paper: Methodology Inspired by Evolutionary Computation: EvoLLMs harnesses genetic algorithms to mimic evolutionary processes like selection and mutation, generating high-quality question-answer pairs. This process is designed to minimize hallucinations while maintaining depth and relevance. Comparison with Human-Generated Datasets: The framework outperformed human-generated datasets in several key metrics, such as Depth, Relevance, and Coverage, while offering comparable performance in minimizing hallucinations. Effective Chain-of-Thought Prompting: The use of Chain-of-Thought prompting significantly reduced hallucinations by guiding the model to produce coherent and logically structured responses. Automation and Reduction in Manual Curation: EvoLLMs offers a scalable alternative to manual dataset generation, drastically cutting down on time and resources traditionally required, while reducing potential biases inherent in human curation. Potential for Diverse Domain Applications: While EvoLLMs demonstrates significant improvements in hallucination mitigation, future work may involve fine-tuning evolutionary algorithms for applications in highly specialized domains. The study concludes with recommendations for integrating real-time databases and domain-specific ontologies to further improve factual accuracy, hinting at broader applications of EvoLLMs across varied informational domains. You can catch the full breakdown here: Here You can catch the full and original research paper here: Original Paper submitted by /u/steves1189 [link] [comments]