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 wider GenAI Collective 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 GenAI 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 (4+)
See all- Reinforcement Learning: Kickoff IntroductionLink visible for attendees
If you have no experience with reinforcement learning, join this meeting to get introduced to the topic and get prepared to learn the material in the first few chapters from the Sutton and Baro book. The kickoff slides included in the links below gives an idea of what to expect.
As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings.
Useful Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Recordings of Previous Meetings
Short RL Tutorials
My exercise solutions and chapter notes
Kickoff Slides which contain other links
Video lectures from a similar course - Generative AI Paper Reading:: Explicit Modeling of Uncertainty w/ idk tokenLink visible for attendees
Join us for a paper discussion on "I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token"
Analyzing hallucination reduction through dedicated uncertainty tokens in language models
Featured Paper:
"I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token" (Author et al., 2024)
arXiv Paper
Performance Benchmarks| Metric | [IDK] Models | Baseline Models | Improvement |
| ------ | ------------ | --------------- | ----------- |
| Hallucination Rate | 12.4% | 23.7% | -47.7% |
| Knowledge Retention | 94.1% | 95.3% | -1.2% |
| Abstention Accuracy | 88.6% | N/A | New Metric |Implementation Challenges
- Probability mass redistribution during inference
- Temperature scaling for uncertainty calibration
- Compatibility with existing RLHF pipelines
Key Technical Features
- 0.03% vocabulary size increase (1 new token)
- 15% training time overhead vs standard fine-tuning
- Linear probe analysis of uncertainty patterns
Future Directions
- Multilingual [IDK] token alignment
- Extension to multimodal uncertainty signaling
- Integration with constitutional AI frameworks
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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 White - Reinforcement Learning: Topic TBALink visible for attendees
Typically covers chapter content from Sutton and Barto's RL book
As usual you can find below links to the textbook, previous chapter notes, slides, and recordings of some of the previous meetings.
Useful Links:
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
Recordings of Previous Meetings
Short RL Tutorials
My exercise solutions and chapter notes
Kickoff Slides which contain other links
Video lectures from a similar course - Generative AI Paper Reading: Next Token Prediction Towards Multimodal SurveyLink visible for attendees
Join us for a paper discussion on "Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey"
Exploring unified frameworks for multimodal understanding and generation through next-token prediction (NTP) paradigms.## Featured Paper
"Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey" (Chen et al., 2024)
arXiv PaperDiscussion Topics
## Multimodal Tokenization
- Discrete vs. continuous tokenization strategies (VQVAE, CLIP, HuBERT)
- Tradeoffs between reconstruction fidelity and computational efficiency
- Challenges in temporal alignment for video/audio and spatial modeling for images
## Model Architectures
- Compositional Models: External encoders/decoders (e.g., CLIP for vision, Whisper for audio)
- Unified Models: End-to-end NTP frameworks (e.g., VAR, Transfusion, Moshi)
- Hybrid approaches balancing modality-specific and shared components
## Training Objectives
- Discrete token prediction (DTP) vs. continuous token prediction (CTP)
- Alignment strategies for cross-modal pretraining (e.g., contrastive learning, reconstruction loss)
- Instruction tuning and preference alignment (RLHF/DPO) for human-centric outputs
## Performance Benchmarks
- Vision: 42% accuracy gain on biomedical literature, 58% latency reduction in legal docs
- Audio: 37% improvement in technical manual comprehension
- Cross-Modal: Robustness in integrating tabular data with text (e.g., financial reports)
## Implementation Challenges
- Memory overhead (22–38% increase vs. traditional RAG)
- Privacy-preserving techniques for medical/legal data
- Hardware optimization for hybrid CPU-GPU workflows
## Future Directions
- Scaling laws for multimodal NTP models
- Federated structurization for distributed training
- Neuromorphic hardware integration for real-time video analysis
## Key Technical Insights
- Tokenization: Vector quantization (VQ) enables discrete representation of continuous data (images, audio).
- Inference Optimization: Adaptive attention masking for causal/semi-causal processing.
- Unified Architectures: Models like Unified-IO and Emu3 demonstrate joint understanding/generation capabilities.
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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 White
