About us
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
19
Reinforcement Learning: Building an AlphaZero Training Pipeline
·OnlineOnlineLast meeting (see recording), we set up an extended tic-tac-toe game environment and showed how the Monte Carlo tree search algorithm defined in the following paper:
Danihelka, I., Guez, A., Schrittwieser, J., & Silver, D. (2022). Policy Improvement by Planning with Gumbel (ICLR 2022). https://openreview.net/forum?id=bERaNdoegnO
can improve an existing policy/value function combination trained with a traditional RL method like actor-critic policy gradient. We demonstrated the tree statistics collected in the regime where the number of simulations is very low and and saw how the simulations are allocated to actions with sequential-halving when the budget is larger. Finally, we compared the tree derived improved policy from the search and demonstrated how it outperforms the original policy in the environment.
This meeting we will continue using the search algorithm to collect tree statistics over the course of episodes and use those statistics to build a dataset. That dataset can then be used to train an improved policy/value function that we can use to get even better performance when used on its own or in combination with search. We will discuss the factors affecting the rate of data generation and how that compares to the training speed. To build a successful algorithm, we will need to balance the resources allocated to each and decide how much data to save in total. Once we have the ability to collect data and train simultaneously, we can demonstrate a training pipeline that masters performance in an MDP environment.
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 SlidesMARL Links:
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
MARL Summer Course Videos
MARL SlidesSutton and Barto Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Video lectures from a similar course28 attendeesReinforcement Learning: Topic TBA
·OnlineOnlineTypically 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 SlidesMARL Links:
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
MARL Summer Course Videos
MARL SlidesSutton and Barto Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Video lectures from a similar course6 attendeesReinforcement Learning: Topic TBA
·OnlineOnlineTypically 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 SlidesMARL Links:
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
MARL Summer Course Videos
MARL SlidesSutton and Barto Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Video lectures from a similar course6 attendeesReinforcement Learning: Topic TBA
·OnlineOnlineTypically 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 SlidesMARL Links:
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
MARL Summer Course Videos
MARL SlidesSutton and Barto Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Video lectures from a similar course6 attendees
Past events
158
Group links
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