What we’re about
Silicon Valley Generative AI is a dynamic community of professionals, researchers, startup founders, and enthusiasts who share a passion for generative AI technology. As part of The AI Collective's broader network, the group provides a fertile ground for the exploration of cutting-edge research, applications, and discussions on all things related to generative AI.
Our community thrives on two main types of engagement. Firstly, in partnership with Boulder Data Science, we host bi-weekly "Paper Reading" sessions. These meetings are designed for deep-dives into the latest machine learning papers, fostering a culture of continuous learning and collaborative research. It's an excellent opportunity for anyone looking to understand the nitty-gritty scientific advancements propelling the field forward.
Secondly, we organize monthly "Talks" that offer a broader range of insights into the world of generative AI. These sessions feature presentations by an eclectic mix of speakers, from industry pioneers and esteemed researchers to emergent startup founders and subject matter experts. Unlike the paper reading sessions, which are more academically inclined, the talks are tailored to appeal to a more general audience. Topics can span the gamut from the technical intricacies of the latest generative models to their real-world applications, startup pitches, and even discussions on the legal and ethical implications of AI.
Whether you're a seasoned professional or merely curious about generative AI, Silicon Valley Generative AI provides a comprehensive platform to learn, discuss, and network.
We strive to be an inclusive community that fosters innovation, knowledge-sharing, and a collective drive to shape the future of AI responsibly. Join us to stay at the forefront of generative AI research, news, and applications.
For those eager to dive deeper into the technical aspects, you can join us on the AI Collective Slack, specifically the #discuss-technical channel, to keep the conversations flowing between meetups.
We are also looking for the following:
• Readers: people who are willing to read papers and speak about them.
• Speakers and presenters: who will put together educational materials and present to the group as well as answer questions.
• Industry events: if you have a generative AI event like a hackathon, lunch and learn or an information session on your product, we would be happy to include in the calendar.
Please contact Matt White here or at contact@matt-white.com
Upcoming events
8
•OnlineGenerative AI Paper Reading: Agent Lightning: Train ANY AI Agents w RL
OnlineJoin us for a paper discussion on Agent Lightning: Train ANY AI Agents with
Reinforcement Learning presented by Evelyn
https://arxiv.org/pdf/2508.03680
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Silicon Valley Generative AI has two meeting formats.
1. Paper Reading - Every second week we meet to discuss machine learning papers. This is a collaboration between Silicon Valley Generative AI and Boulder Data Science.
2. Talks - Once a month we meet to have someone present on a topic related to generative AI. Speakers can range from industry leaders, researchers, startup founders, subject matter experts and those with an interest in a topic and would like to share. Topics vary from technical to business focused. They can be on how the latest in generative models work and how they can be used, applications and adoption of generative AI, demos of projects and startup pitches or legal and ethical topics. The talks are meant to be inclusive and for a more general audience compared to the paper readings.
If you would like to be a speaker please contact:
Matt White5 attendees
•OnlineMulti-Agent Reinforcement Learning: Chapter 5 First Steps for MARL in Games
OnlineThis meeting we will begin Chapter 5 in Multi-Agent Reinforcement Learning: Foundations and Modern Approaches which introduces the first algorithms designed to compute game solutions. We will define convergence criteria for algorithms and then discuss the differences between central learning and independent learning. Central learning, in particular, has a clear connection to ordinary reinforcement learning, so reviewing the material and videos from the Sutton and Barto book would be helpful.
As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings.
Meetup Links:
Recordings of Previous RL Meetings
Recordings of Previous MARL Meetings
Short RL Tutorials
My exercise solutions and chapter notes
Kickoff Slides which contain other links
MARL Kickoff Slides
MARL Links:
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
MARL Summer Course Videos
MARL Slides
Sutton and Barto Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Video lectures from a similar course32 attendees- •Online
Reinforcement Learning: Topic TBA
OnlineTypically covers material from the following textbook: Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings.
Meetup Links:
Recordings of Previous RL Meetings
Recordings of Previous MARL Meetings
Short RL Tutorials
My exercise solutions and chapter notes
Kickoff Slides which contain other links
MARL Kickoff Slides
MARL Links:
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
MARL Summer Course Videos
MARL Slides
Sutton and Barto Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Video lectures from a similar course10 attendees - •Online
Reinforcement Learning: Topic TBA
OnlineTypically covers material from the following textbook: Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings.
Meetup Links:
Recordings of Previous RL Meetings
Recordings of Previous MARL Meetings
Short RL Tutorials
My exercise solutions and chapter notes
Kickoff Slides which contain other links
MARL Kickoff Slides
MARL Links:
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
MARL Summer Course Videos
MARL Slides
Sutton and Barto Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Video lectures from a similar course7 attendees
Past events
127
Group links
Organizers
Members
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