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National identity stereotypes through generative AI

FlowingData

Between this convergence to stereotypes and the forced diversity from Google’s Gemini, has anyone tried coupling models with demographic data to find a place in between? Tags: AI , bias , midjourney , Rest of World

AI 138
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The ultimate LLM showdown begins

Dataconomy

The world of technology is no stranger to showdowns and we have yet another exciting race: Gemini vs ChatGPT. Two heavyweights dominate the scene: ChatGPT, from OpenAI, and Google’s own powerful Gemini. Let’s dive in with a Gemini vs ChatGPT comparison! Who will stand victorious in Gemini vs ChatGPT showdown?

AI 194
professionals

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Five sessions not to miss at Google Cloud Next 24

Snorkel AI

You’ll learn how Fortune 500 enterprises have built production-ready, highly custom AI solutions using Google Gemini and PaLM 2 models, Vertex AI, and Snorkel Flow. You’ll find out how highly accurate product tags can be extracted from supplier-provided labels and product images to clean and enrich online catalogs.

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Five sessions not to miss at Google Cloud Next 24

Snorkel AI

You’ll learn how Fortune 500 enterprises have built production-ready, highly custom AI solutions using Google Gemini and PaLM 2 models, Vertex AI, and Snorkel Flow. You’ll find out how highly accurate product tags can be extracted from supplier-provided labels and product images to clean and enrich online catalogs.

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Introduction to Gemini Pro Vision

PyImageSearch

Home Table of Contents Introduction to Gemini Pro Vision Google AI Studio Creating a Prompt Using Google AI Studio Gemini Pro Vision API Summary Citation Information Introduction to Gemini Pro Vision In this tutorial, you will learn how to get started with Gemini Pro Vision.

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LLM distillation demystified: a complete guide

Snorkel AI

LLMs like GPT-4, Gemini, and Llama demonstrate incredible power, but also suffer notable drawbacks: Cost. In cases where the high-confidence subset of data appears sufficiently robust, data scientists could cast aside all records tagged as likely-incorrect and train their model with the remainder. Why would you use LLM distillation?

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LLM distillation demystified: a complete guide

Snorkel AI

LLMs like GPT-4, Gemini, and Llama demonstrate incredible power, but also suffer notable drawbacks: Cost. In cases where the high-confidence subset of data appears sufficiently robust, data scientists could cast aside all records tagged as likely-incorrect and train their model with the remainder. Why would you use LLM distillation?