Michael O'Connell

Causal inference from observational data is growing in importance. In this #ISPORAnnual workshop, Carelon Research’s Michael Grabner will discuss the basics of integrated data, causal inference, and their interplay. Learn more: https://ow.ly/nQWG50RpFYI. #causalinference

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Using deterministic NLP models in conjunction with Phi-3 to summarize long form documents, speeding up the review process and improving accuracy. #NLP #Phi3 #LLM

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Follow along Sean M. Smith's latest tutorial to learn about prediction-powered inference (PPI) and how it can create confidence intervals that are data-efficient and theoretically guaranteed.

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Transformers Unpacked: An In-Depth Guide to Encoder-Decoder Architecture, Positional Encoding, Multi-Head Attention, and Feed-Forward Layers. #generativeai #deeplearning #nlp #transformers #encoderdecoder

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💻 Computational approaches can leverage variant affect data for functional interpretation. If you are interested in Variant Effect Predictors (VEPs)- we have a resource for you! (thanks to Benjamin Livesey, Joseph Marsh and members of the Atlas of Variant Effects Alliance "Analysis, Modeling, and Prediction" workstream) https://lnkd.in/gQYGHGHP #FunctionalGenomics #Collaboration #MachineLearning #VariantEffectPredictor

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Thoughts on this? >> Researchers from Intel and Salesforce Propose SynthKG: A Multi-Step Document-Level Ontology-Free Knowledge Graphs Synthesis Workflow based on LLMs >> Comment below! >>> lqventures.com #healthtech #industry40 #IoT #mhealth #AI

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Thoughts on this? >> This AI Paper from Vectara Evaluates Semantic and Fixed-Size Chunking: Efficiency and Performance in Retrieval-Augmented Generation Systems >> Comment below! >>> lqventures.com #AI #healthtech #IoT #mhealth #industry40

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Building AI Agent Systems with LangGraph The article discusses how LangGraph, a tool within the LangChain ecosystem, simplifies the development of advanced AI applications. Key Takeaways: LangGraph Components: LangGraph uses states, nodes, and edges to manage AI agent workflows. States store context, nodes perform tasks, and edges control the flow of operations. Simplified Development: LangGraph provides a clear structure for building intelligent, context-aware agents capable of handling complex interactions. Practical Example: The article includes a step-by-step guide to creating a customer support agent using OpenAI’s GPT-3.5-Turbo model, demonstrating LangGraph's practical application. Environment Setup: Instructions for setting up the development environment, including installing necessary packages and configuring the OpenAI API key, are provided. #AI #GenAI #AIAgents #LangGraph #Takeda https://lnkd.in/dF4nXVAn

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Fantastic new article from Matthew Haffner demonstrating Replicable and extensible spatial data acquisition, a part of our up coming special issue on Replicability and Reproducibility in CaGIS, https://lnkd.in/egHFPNzu, #GISchat Having the ability to reproduce empirical results is foundational to the scientific process. Increasingly, there is an emphasis within the research community to create workflows which are not only reproducible but also replicable in that analytical approaches can be applied to new data. However, a disproportionate number of technical solutions designed to foster reproducibility and replicability have focused on data analysis, particularly within geography. This paper argues for greater attention on a phase of the research process which precedes analysis and is often taken for granted – that of data acquisition. Through code examples using the R Project for Statistical Computing, this paper demonstrates a path toward replicable spatial data acquisition for both secondary and primary data acquisition, with a focus on crowdsourced geographic information. The modular approach demonstrated is flexible in that it allows for straightforward replication but also enables extension to new spatial questions. Such work is valuable because it conserves labor, promotes research provenance, and allows for more robust analyses.

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🚀 Exciting Advances in Sustainable AI! 🌟 A recent article from VentureBeat highlights a development in AI technology: a new transformer architecture designed to enable powerful large language models (LLMs) without relying on GPUs. This innovation could change the way we deploy and scale AI systems, making them more accessible and efficient. 📝 The referenced paper titled "ECO: Environmentally Conscious Optimizations for Transformer-Based Models" delves into optimizing transformer models to reduce their environmental impact. The authors present methods to significantly lower energy consumption and computational costs while maintaining high performance. 📄 Read the full journal article for an in-depth understanding of these technologies: - [VentureBeat Article](https://lnkd.in/e8QZrwwB) - [ECO Paper on arXiv](https://lnkd.in/eFXGfEgT) #AI #MachineLearning #Innovation #Sustainability #TechNews

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This is a great introduction to the multi-agent system, including the human-in-the-loop concept in the OpenAI cookbook: https://lnkd.in/edqD5vED. I really like this notion of using intent before handoffs, including deciding what context to share or not.

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🚨New Publication Alert : Qwen2 Technial Report The Qwen2 Technical Report introduces a new series of large language models, with sizes ranging from 0.5 to 72 billion parameters. The flagship model, Qwen2-72B, achieves high scores across various benchmarks, demonstrating strong capabilities in language understanding, generation, multilingual proficiency, coding, mathematics, and reasoning. The instruction-tuned variant, Qwen2-72B-Instruct, also excels in specific benchmarks. Qwen2 supports about 30 languages and is openly available for community use, with resources for quantization, fine-tuning, and deployment on Hugging Face and ModelScope. For more details, you can view the [full report](https://lnkd.in/dhiXhE_r).

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🎬 Bend the Cost Curve with ThoughtSphere’s Stats & Programming 🎬 👇 👇 Check out the #video below to see how we’re transforming SDTM,  ADaM, and TLF creation. Our approach combines the power of #Python and #R_Programming with human-in-the-loop #AI, to deliver validation-ready datasets and TLFs, cutting weeks off delivery timelines! ▶️ Click below to watch! #Biostatistics #Programming #ClinicalData #AI #LifeSciences #ThoughtSphere

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👉🏼 Evaluation of Rhinoplasty Information from ChatGPT, Gemini, and Claude for Readability and Accuracy 🤓 Monica K Rossi Meyer 👇🏻 https://lnkd.in/e-6j2hpj 🔍 Focus on data insights: - 📊 ChatGPT demonstrated superior accuracy and overall quality in responses compared to its counterparts Gemini and Claude. - ✍️ All three chatbots provided responses rated as neutral to incomplete, indicating a gap in exhaustive information dissemination. - 🏷️ Use of medical jargon was prevalent across all platforms, with readability scores assessed at a college level. 💡 Main outcomes and implications: - ⚖️ Surgeons must critically evaluate the information provided by chatbot platforms due to potential incompleteness and accuracy issues. - 📚 There is an opportunity for improving chatbot technology through training on evidence-based medical recommendations to enhance educational effectiveness. - 🧩 The findings highlight a need for continuous monitoring and development of AI tools in healthcare to ensure reliable patient education resources. 📚 Field significance: - 🔬 The study underscores the importance of accurate medical information accessibility in the age of AI and digital resources. - 📋 It prompts further exploration into integrating AI into medical education while maintaining rigorous quality control standard practices. - 🛠️ The technology's potential for contribution in education suggests innovative pathways for enhancing surgeon-patient communication through AI-enabled platforms. 🗄️: [#rhinoplasty #AIinHealthcare #medicaleducation #ChatGPT #Gemini #Claude #readability #accuracy #patienteducation #digitalhealth]

Latest blog post featuring a brief walkthrough of zero-inflated models in R and SAS. Come check out the blog and consider subscribing! https://lnkd.in/guRtJkY6

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