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
6
•OnlineMulti-Agent Reinforcement Learning: Chapter 6 Joint-action learning
OnlineThis meeting will cover the first two sections of Chapter 6 in Multi-Agent Reinforcement Learning: Foundations and Modern Approaches. The algorithms in this chapter incorporate the concept of equilibrium solutions from game theory into reinforcement learning. We will cover the value iteration algorithm for stochastic games which is an exact solution technique. For cases where we do not have access to the full transition function, we will introduce joint-action learning with game theory which is a q-learning style estimation algorithm. Both algorithms can be applied to different game models, but the most straightforward examples are for two-player zero-sum games where the minimax solution concept can be applied. We will discuss this case with a simple soccer game example and then touch on how other solution concepts might be applied for general sum games with more agents.
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 for Sutton-Barto
My MARL repository
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 course23 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 for Sutton-Barto
My MARL repository
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 course9 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 for Sutton-Barto
My MARL repository
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 course5 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 for Sutton-Barto
My MARL repository
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 course4 attendees
Past events
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