Remove Explainability Remove Machine Learning Remove Prompt Engineering
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AI courses to boost your skills and stay ahead

AI News

Business Analyst: Digital Director for AI and Data Science Business Analyst: Digital Director for AI and Data Science is a course designed for business analysts and professionals explaining how to define requirements for data science and artificial intelligence projects.

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How Travelers Insurance classified emails with Amazon Bedrock and prompt engineering

AWS Machine Learning Blog

Increasingly, FMs are completing tasks that were previously solved by supervised learning, which is a subset of machine learning (ML) that involves training algorithms using a labeled dataset. In some cases, smaller supervised models have shown the ability to perform in production environments while meeting latency requirements.

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The Essential Guide to Prompt Engineering in ChatGPT

Unite.AI

The secret sauce to ChatGPT's impressive performance and versatility lies in an art subtly nestled within its programming – prompt engineering. This makes us all prompt engineers to a certain degree. Venture capitalists are pouring funds into startups focusing on prompt engineering, like Vellum AI.

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Image and video prompt engineering for Amazon Nova Canvas and Amazon Nova Reel

AWS Machine Learning Blog

Although these models are powerful tools for creative expression, their effectiveness relies heavily on how well users can communicate their vision through prompts. This post dives deep into prompt engineering for both Nova Canvas and Nova Reel.

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How to Use Prompt Engineering in ChatGPT? Key Insights and Tips

Marktechpost

However, to get the best results from ChatGPT, one must master the art of prompt engineering. Crafting precise and effective prompts is crucial in guiding ChatGPT in generating the desired outputs. This predictive capability is harnessed through prompt engineering, where the prompts guide the model’s predictions.

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#48 Interpretability Might Not Be What Society Is Looking for in AI

Towards AI

It also highlights ways to improve decision-making strategies through techniques like dynamic transition matrices, multi-agent MDPs, and machine learning for prediction. It covers installing Tesseract, Pillow, and Pytesseract for text extraction from images and using the Gemini API for translation with prompt engineering.

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Few-shot prompt engineering and fine-tuning for LLMs in Amazon Bedrock

AWS Machine Learning Blog

Solution overview We apply two methods to generate the first draft of an earnings call script for the new quarter using LLMs: Prompt engineering with few-shot learning – We use examples of the past earnings scripts with Anthropic Claude 3 Sonnet on Amazon Bedrock to generate an earnings call script for a new quarter.