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Title & Speakers	Event
Retrieval and Adaptive In-Context Learning 2024-10-29 · 19:00 Organizations develop feedback loops to continuously enhance quality. One such loop is the learning from user interactions with your data, retraining models, deploying new models and learning again. The learning curve to create a loop like this is steep, it requires ML experience and tools. However, most teams can easily provide labeled examples. In-Context Learning (ICL) is a method to add classification examples as input to foundation models (like LLMs).\nThis talk defines an Adaptive ICL strategy using Retrieval for Examples, where the output is used for content retrieval, example set expansion for future model training and real-time user behaviour tracking. Adaptive ICL is hence an easy way for teams to get immediate results with AI, while laying the foundation for more advanced ML loops in the future. in-context learning adaptive icl retrieval llms	AI meetup (with AWS): Generative AI and LLMs in Action
AI meetup (with AWS): Generative AI and LLMs in Action 2024-10-29 · 17:30 Important RSVP HERE (Due to room capacity and venue security, it is required to pre-register at the link for admission) Description: Welcome to the monthly AI meetup in Paris. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers. Agenda: - 6:30pm\~7:00pm: Checkin\, food and networking - 7:00pm\~9:00pm: Tech talks and Q&A - 9:00pm: Open discussion\, Mixer and Closing. Tech Talk: Retrieval and Adaptive In-Context Learning Speaker: Kristian Aune (Vespa AI) Abstract: Organizations develop feedback loops to continuously enhance quality. One such loop is the learning from user interactions with your data, retraining models, deploying new models and learning again. The learning curve to create a loop like this is steep, it requires ML experience and tools. However, most teams can easily provide labeled examples. In-Context Learning (ICL) is a method to add classification examples as input to foundation models (like LLMs). This talk defines an Adaptive ICL strategy using Retrieval for Examples, where the output is used for content retrieval, example set expansion for future model training and real-time user behaviour tracking. Adaptive ICL is hence an easy way for teams to get immediate results with AI, while laying the foundation for more advanced ML loops in the future. Topics/Speakers: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will have the chance to speak at the meetups, receive prominent recognition, and gain exposure to our extensive membership base of 10,000+ AI developers in Paris or 400K+ worldwide. Community on Slack/Discord** Event chat: chat and connect with speakers and attendees Sharing blogs, events, job openings, projects collaborations *	AI meetup (with AWS): Generative AI and LLMs in Action

Organizations develop feedback loops to continuously enhance quality. One such loop is the learning from user interactions with your data, retraining models, deploying new models and learning again. The learning curve to create a loop like this is steep, it requires ML experience and tools. However, most teams can easily provide labeled examples. In-Context Learning (ICL) is a method to add classification examples as input to foundation models (like LLMs).\nThis talk defines an Adaptive ICL strategy using Retrieval for Examples, where the output is used for content retrieval, example set expansion for future model training and real-time user behaviour tracking. Adaptive ICL is hence an easy way for teams to get immediate results with AI, while laying the foundation for more advanced ML loops in the future.

** Important RSVP HERE (Due to room capacity and venue security, it is required to pre-register at the link for admission)

Description: Welcome to the monthly AI meetup in Paris. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers.

Agenda: - 6:30pm\~7:00pm: Checkin\, food and networking - 7:00pm\~9:00pm: Tech talks and Q&A - 9:00pm: Open discussion\, Mixer and Closing.

Tech Talk: Retrieval and Adaptive In-Context Learning Speaker: Kristian Aune (Vespa AI) Abstract: Organizations develop feedback loops to continuously enhance quality. One such loop is the learning from user interactions with your data, retraining models, deploying new models and learning again. The learning curve to create a loop like this is steep, it requires ML experience and tools. However, most teams can easily provide labeled examples. In-Context Learning (ICL) is a method to add classification examples as input to foundation models (like LLMs). This talk defines an Adaptive ICL strategy using Retrieval for Examples, where the output is used for content retrieval, example set expansion for future model training and real-time user behaviour tracking. Adaptive ICL is hence an easy way for teams to get immediate results with AI, while laying the foundation for more advanced ML loops in the future.

Topics/Speakers: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will have the chance to speak at the meetups, receive prominent recognition, and gain exposure to our extensive membership base of 10,000+ AI developers in Paris or 400K+ worldwide.

Community on Slack/Discord

Event chat: chat and connect with speakers and attendees
Sharing blogs, events, job openings, projects collaborations *

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