Remove BERT Remove Conversational AI Remove Prompt Engineering
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Generative AI use cases for the enterprise

IBM Journey to AI blog

The quality of outputs depends heavily on training data, adjusting the model’s parameters and prompt engineering, so responsible data sourcing and bias mitigation are crucial. Imagine training a generative AI model on a dataset of only romance novels.

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Charting the Impact of ChatGPT: Transforming Human Skills in the Age of Generative AI

Marktechpost

Impact of ChatGPT on Human Skills: The rapid emergence of ChatGPT, a highly advanced conversational AI model developed by OpenAI, has generated significant interest and debate across both scientific and business communities.

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Large Language Models for Product Managers: 5 Things to Know

AssemblyAI

The widespread use of ChatGPT has led to millions embracing Conversational AI tools in their daily routines. This trend started with models like the original GPT and ELMo, which had millions of parameters, and progressed to models like BERT and GPT-2, with hundreds of millions of parameters. months on average.

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Unlocking the Potential: The Fascinating World of Language Model Optimization with ChatGPT

Pickl AI

ChatGPT is not just another AI model; it represents a significant leap forward in conversational AI. With its ability to engage in natural, context-aware conversations, ChatGPT is reshaping how we communicate with machines. Ensuring the safety of the model in real-world applications is paramount.

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Generative AI: The Idea Behind CHATGPT, Dall-E, Midjourney and More

Unite.AI

These advanced AI deep learning models have seamlessly integrated into various applications, from Google's search engine enhancements with BERT to GitHub’s Copilot, which harnesses the capability of Large Language Models (LLMs) to convert simple code snippets into fully functional source codes.

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Creating An Information Edge With Conversational Access To Data

Topbots

While you will absolutely need to go for this approach if you want to use Text2SQL on many different databases, keep in mind that it requires considerable prompt engineering effort. 4] In the open-source camp, initial attempts at solving the Text2SQL puzzle were focussed on auto-encoding models such as BERT, which excel at NLU tasks.[5,

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Four LLM Trends Since ChatGPT And Their Implications For AI Builders

Topbots

Autoencoding models, which are better suited for information extraction, distillation and other analytical tasks, are resting in the background — but let’s not forget that the initial LLM breakthrough in 2018 happened with BERT, an autoencoding model. Developers can now focus on efficient prompt engineering and quick app prototyping.[11]

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