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|a Manjengwa, Shingai
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|a AI Superstream: Large language models
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|a Large language models
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|a [First edition]
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|c 2023
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|a Traitement automatique des langues naturelles
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|a Natural language processing (Computer science) / fast
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|a Large language models have taken the field of natural language processing and other domains of AI application by storm. The introduction of the transformer architecture by Google Brain in 2017 allowed these models to expand beyond individual word processing to the broader contexts of sentences or paragraphs, and the results have been groundbreaking, even renewing debates about the sentience of AI. But while the applications for these models are endless--from personal assistants and coding assistants to translation and copywriting--the power of LLMs comes with many questions and challenges. The size of LLMs can raise questions of latency and costs when put into production, and debates around the potential harmful effects of LLMs that produce hate speech and misinformation have only just begun. Join experts and practitioners in the field who are tackling these challenges head-on. ML and NLP researchers, data scientists, ML engineers, and AI leaders interested in these powerful models will explore everything from researching the potential of new LLMs to creating state-of-the art applications and more. What you'll learn and how you can apply it Get expert perspectives on the latest tools and techniques for building large language model applications Learn about the latest open source projects making powerful LLM applications achievable for more organizations This Superstream is for you because... You're a current or future AI product owner or AI/machine learning practitioner. You want to learn about the state of the art in artificial intelligence and how large language models can be leveraged to build new applications and solve your organizational challenges. Please note that slides or supplemental materials are not available for download from this recording. Resources are only provided at the time of the live event
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