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EVENT DETAILS |
Description:
Welcome to our in-person AI meetup in New York. Join us for deep dive tech talks on AI, GenAI, LLMs & ML, hands-on workshops, food/drink, networking with speakers & fellow developers.
Tech Talk: Build Lightweight LLMs Applications with Open Source Tools
Speaker: Amogh Kokari (Con Edison)
Abstract: ChatGPT is awesome, but developing with its API comes at a cost. Fortunately, there are open-source alternatives like Google Gemini, Streamlit, & Python APIs that can fetch prompt results using an API key. In this presentation, I'll explore how to create a lightweight, self-service end-to-end LLMs application using prompt engineering & fine-tuning based on user requests. Additionally, I'll demonstrate how to build a food suggestion application based on ingredients or food names.
Tech Talk: MultiModal Learning & Synthetic AI
Speaker: Shafik Quoraishee (The New York Times)
Abstract: This talk will be a Deep Dive into Multi-Modal AI Models, the powerful AI systems that are behind the functioning of applications such GPT Vision, DALL-E, & even Sora. We will go through the core theory of how intelligence from different sources of data (text, AI, & Vision) are combined to together in order help build capabilities like real-time Image/Video analysis, image hyper segmentation, & image to text extraction, & more.
Speakers/Topics:
Stay tuned as we are updating speakers & 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 our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsor. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 20,000+ AI developers in New York or 350K+ worldwide.
Community on Slack/Discord
Event chat: chat & connect with speakers & attendees
Sharing blogs, events, job openings, projects collaborations
Join Slack/Discord (scroll down to the bottom)
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