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Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI…

ODSC - Open Data Science

Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. As LLMs become integral to AI applications, ethical considerations take center stage.

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Top Artificial Intelligence AI Courses from Google

Marktechpost

Inspect Rich Documents with Gemini Multimodality and Multimodal RAG This course covers using multimodal prompts to extract information from text and visual data and generate video descriptions with Gemini. Natural Language Processing on Google Cloud This course introduces Google Cloud products and solutions for solving NLP problems.

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A Guide to Mastering Large Language Models

Unite.AI

Unlike traditional NLP models which rely on rules and annotations, LLMs like GPT-3 learn language skills in an unsupervised, self-supervised manner by predicting masked words in sentences. Their foundational nature allows them to be fine-tuned for a wide variety of downstream NLP tasks. This enables pretraining at scale.

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Google’s Dr. Arsanjani on Enterprise Foundation Model Challenges

Snorkel AI

As we look at the progression, we see that these state-of-the-art NLP models are getting larger and larger over time. The responsible AI measures pertaining to safety and misuse and robustness are elements that need to be additionally taken into consideration. Then comes prompt engineering. Three is the adaptation.

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Google’s Arsanjani on Enterprise Foundation Model Challenges

Snorkel AI

As we look at the progression, we see that these state-of-the-art NLP models are getting larger and larger over time. The responsible AI measures pertaining to safety and misuse and robustness are elements that need to be additionally taken into consideration. Then comes prompt engineering. Three is the adaptation.