Recently wrote about how we approach using generative AI to improve the Discord user experience. Starting from ideation and prototyping to deployment at scale, check it out for insights on the challenges we've faced and how we've tackled them: https://lnkd.in/ghYhu9ph
Shannon Phu’s Post
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Generative AI application patterns that we can learn from this blog post: * Limit the scope to solve one problem * Open as a service instead of an interactive interface * Break into subtasks using workflow orchestration * Guard with pre- and post-processing * Involve human during inference
Building Generative AI prompt chaining workflows with human in the loop | Amazon Web Services
aws.amazon.com
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Serving Notice Period | Data Science and ML Engineer | Python | Machine Learning | Deep Learning | GANs | AI Development | Data Science | Computer Vision
🌟 Day 26 of 100 Days of AI Learning: Model Interpretability 🌟 Hello everyone! Today, let's delve deeper into the importance of model interpretability in machine learning and explore key techniques for achieving explainable AI. Importance of Model Interpretability: - Model interpretability plays a critical role in understanding how machine learning models make decisions and predictions. - It enhances trust, transparency, and accountability in AI systems, empowering stakeholders to comprehend the factors influencing model outputs. Techniques for Explainable AI: Several techniques have been developed to enhance the interpretability of complex machine learning models: 1. Local Interpretable Model-agnostic Explanations (LIME): - LIME explains the predictions of any classifier by learning an interpretable model locally around the prediction. - It provides insights into why a particular prediction was made, aiding in understanding black-box models. 2. SHapley Additive exPlanations (SHAP): - SHAP leverages game theory to explain the output of machine learning models. - It assigns importance values to each feature for a specific prediction, offering a comprehensive view of feature contributions. 3. Partial Dependence Plots (PDP): - PDPs show the relationship between a feature and the predicted outcome while marginalizing over all other features. - They reveal how the model's prediction changes with variations in specific input features. Advantages of Explainable AI: - Trust and Transparency: Interpretable models foster trust by allowing users to validate model decisions. - Error Diagnosis: Interpretability helps diagnose model errors and identify bias or unfairness in predictions. - Regulatory Compliance: Explainable AI supports compliance with regulatory requirements, such as GDPR's right to explanation. Real-World Impact of Model Interpretability: - Healthcare: Interpretability enables clinicians to understand why a diagnostic model makes specific recommendations, improving patient care. - Finance: Explainable AI assists in understanding credit scoring models, ensuring fairness and transparency in lending practices. By embracing model interpretability techniques like LIME, SHAP, and PDPs, we can enhance the transparency and accountability of AI systems, fostering responsible AI deployment. Let's prioritize interpretability in AI development to build trustworthy and explainable machine learning models that empower users and promote ethical AI practices. Stay tuned for more insights on model interpretability and its impact on AI applications! 🚀 #InterpretableAI #ExplainableAI #MachineLearning #100DaysOfAI #Day26
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Helping organizations gain competitive advantage with AI, structured content, and contextual delivery, including IA, DITA, and XML
Learn the secrets to crafting effective and impactful AI prompts in this upcoming webinar. Tue, 4/30, 8 AM PDT https://buff.ly/49Ilo3u
Best Practices for Building Useful Generative AI Prompts
brighttalk.com
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Seeing how #ArtificialIntelligence is changing the ways we live and work, it's no surprise it's here to stay. But what should we expect for 2024? Dive into the trends that will shape 2024: from smarter AI with massive datasets to multi-modal models that understand voice and visuals. Find all of the insights here: https://lnkd.in/g4KCtmtz
Generative AI trends to watch out for in 2024
cai.io
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Seeing how #ArtificialIntelligence is changing the ways we live and work, it's no surprise it's here to stay. But what should we expect for 2024? Dive into the trends that will shape 2024: from smarter AI with massive datasets to multi-modal models that understand voice and visuals. Find all of the insights here: https://lnkd.in/gDUJhngT
Generative AI trends to watch out for in 2024
cai.io
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Seeing how #ArtificialIntelligence is changing the ways we live and work, it's no surprise it's here to stay. But what should we expect for 2024? Dive into the trends that will shape 2024: from smarter AI with massive datasets to multi-modal models that understand voice and visuals. Find all of the insights here: https://lnkd.in/dFWY2Mxv
Generative AI trends to watch out for in 2024
cai.io
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Data Science Thought Leader | Innovator in Gen AI & LLM | Award-Winning Educator | Patent Holder | Co-Founder of InstaDataHelp Analytics Services | AI Blogger | Fellow of Prestigious Societies | 16+ Years of Excellence
Google Assistant to Get Generative AI Summarizer
Google Assistant to Get Generative AI Summarizer
https://instadatahelpainews.com
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How to start taking advantage of generative AI in your business NOW!
How To Unlock the Power of Generative AI Without Building Your Own LLM
salesforce.smh.re
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