Recapping the AI, Machine Learning and Data Science Meetup — April 18, 2024
Tags: computervision, ai, machinelearning, datascience
Published: 2024-04-18 21:53:26

We just wrapped up the April ‘24 [AI, Machine Learning and Data Science Meetup](https://voxel51.com/computer-vision-ai-meetups/), and if you missed it or want to revisit it, here’s a recap! In this blog post you’ll find the playback recordings, highlights from the presentations and Q&A, as well as the upcoming Meetup schedule so that you can join us at a future event.

## First, Thanks for Voting for Your Favorite Charity!

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/w5ycfbxhsh6do90cpkea.jpeg)

In lieu of swag, we gave Meetup attendees the opportunity to help guide a $200 donation to charitable causes. The charity that received the highest number of votes this month was [Oceana](https://oceana.org/), which is focused on ocean conservation — protecting and restoring marine life and the world’s abundant and biodiverse oceans. We are sending this event’s charitable donation of $200 to Oceana on behalf of the Meetup members!

Missed the Meetup? No problem. Here are playbacks and talk abstracts from the event.

## Towards Resource Efficient Robust Text-to-Image Generative Models

{% embed https://youtu.be/9eRGEwyh2z0 %}

Text-to-image (T2I) diffusion models (such as Stable Diffusion XL, DALL-E 3, etc.) achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks, at the cost of significant computational resources. For instance, the unCLIP (i.e., DALL-E 2) stack comprises T2I prior and diffusion image decoder. The T2I prior model itself adds a billion parameters, increasing the computational and high-quality data requirements. Maitreya propose the [ECLIPSE](https://arxiv.org/abs/2312.04655), a novel contrastive learning method that is both parameter and data-efficient as a way to combat these issues.

**Speaker:** [Maitreya Patel](https://www.linkedin.com/in/maitreya-patel-a37a16139/) is a PHD student studying at Arizona State University focusing on model performance and efficiency. Whether it is model training or inference, Maitreya strives to make optimizations to make AI more accessible and powerful.

### Q&A

- _Has focusing on Projection Priors proven beneficial when building task-specific generative models?_
- _Does this approach replace the concept of LORAs?_
- _Prior to training the ECLIPSE model, does it rely upon the training of the prior model?_
- _If the example was video creation, how might this work?_
- _How modular is it? Could we swap out the CLIP?_
- _What does it mean to have your own dataset with the "anchor", "positive" and "negative" images?_

### Resource links

- [https://maitreyapatel.com/](https://maitreyapatel.com/)
- arVix: [ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations](https://arxiv.org/abs/2312.04655)
- arVix: [λ-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space](https://arxiv.org/abs/2402.05195)

## GraphRAG with a Knowledge Graph

{% embed https://youtu.be/kGNWRAQXWSQ %}

Knowledge Graphs place information in context using graph structures to express local and global semantics. When used in a RAG context, particular access patterns emerge that map natural language to different graph data patterns. We’ll review both the model and the matching code.

**Speaker:** [Andreas Kollegger](https://www.linkedin.com/in/akollegger/?originalSubdomain=uk) is a founding member of Neo4j, now responsible for researching the use of Knowledge Graphs for GenAI applications.

### Q&A

- _How do you manage graph updates?_
- _How is security managed?_
- _Should semantic chunks or the size of chunks be given per node in neo4j?_
- _How do you do the index search or pattern matching after you get the required chunks using distance-based similarity search?_
- _Do you put the embeddings in the nodes that are chunked or do they stay separate?_

### Resource links

- GitHub: [Neo4-sec-edgar](https://github.com/neo4j-partners/neo4j-sec-edgar)
- [Get started with AuraDB](https://neo4j.com/cloud/aura-free/)
- [Free Neo4j & LLMs Courses](https://graphacademy.neo4j.com/categories/llms/)

## Optimizing Training Data with the Voxel51 and V7 Darwin Integration

{% embed https://youtu.be/NifS8Kj3dcQ %}

One of the most expensive parts of a machine learning project is obtaining high quality training data. In this talk, Mark will discuss how the integration between Voxel51 and the V7 Darwin platform can help you optimize the subset of data to be labeled with the goals of reducing costs whilst maintaining quality.

**Speaker:** [Mark Cox-Smith](https://www.linkedin.com/in/mark-cox-smith-aa644a108/) is a Principal Solutions Architect at V7 where he helps customers to connect their labeling workflows into their MLOps stack.

### Resource links

- [Try V7 Darwin](https://www.v7labs.com/darwin)
- [Python Library](https://pypi.org/project/darwin-fiftyone/1.0.1/)
- [V7 + FiftyOne integration Docs](https://docs.voxel51.com/integrations/v7.html)

## Exploring Multimodal Models: Llava-Next and TextQA Dataset

{% embed https://youtu.be/7XXnnMOksbc %}

In this session, you’ll get hands-on with the newest LlaVa model, LlaVa-next! You’ll learn how to use fiftyone to visually vibe check the performance of both the Vicuna-7B and Mistral-7B backbones models on the TextQA dataset.

**Speaker:** [Harpreet Sahota](https://www.linkedin.com/in/harpreetsahota204/) is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.

### Resource links

- Colab notebook
- Connect with Harpreet on [Linkedin](https://www.linkedin.com/in/harpreetsahota204/), [Twitter](https://twitter.com/DataScienceHarp) and [Substack](https://harpreetsahota.substack.com/)

## Join the AI, Machine Learning and Data Science Meetup!

The combined membership of the [Computer Vision and AI, Machine Learning and Data Science Meetups](https://voxel51.com/computer-vision-ai-meetups/) has grown to over 20,000 members! The goal of the Meetups is to bring together communities of data scientists, machine learning engineers, and open source enthusiasts who want to share and expand their knowledge of AI and complementary technologies. 

Join one of the 12 Meetup locations closest to your timezone.

- [Athens](https://www.meetup.com/athens-ai-machine-learning-data-science)
- [Austin](https://www.meetup.com/austin-ai-machine-learning-data-science)
- [Bangalore](https://www.meetup.com/bangalore-ai-machine-learning-data-science)
- [Boston](https://www.meetup.com/boston-ai-machine-learning-data-science)
- [Chicago](https://www.meetup.com/chicago-ai-machine-learning-data-science)
- [London](https://www.meetup.com/london-ai-machine-learning-data-science)
- [New York](https://www.meetup.com/new-york-ai-machine-learning-data-science)
- [Peninsula](https://www.meetup.com/peninsula-ai-machine-learning-data-science)
- [San Francisco](https://www.meetup.com/sf-ai-machine-learning-data-science)
- [Seattle](https://www.meetup.com/seattle-ai-machine-learning-data-science)
- [Silicon Valley](https://www.meetup.com/sv-ai-machine-learning-data-science)
- [Toronto](https://www.meetup.com/toronto-ai-machine-learning-data-science)

## What’s Next?

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/lzriaqmfsdu9c9rbwide.png)

Up next on May 2 at 10 AM India we have two great speakers lined up!

- **Who needs RLHF When You Have SFT?** - [Srishti Gureja](https://www.linkedin.com/in/srishti-gureja-a51841171/) - Georgia Institute of Technology & Writesonic
- **Develop a Legal Search Application from Scratch using Milvus and DSPy!** - [Mert Bozkir](https://www.linkedin.com/in/mertbozkir/) - LLM Engineer

Register for the Zoom [here](https://voxel51.com/computer-vision-events/may-2-2024-ai-machine-learning-data-science-meetup/). You can find a complete schedule of upcoming Meetups on [the Voxel51 Events page](https://voxel51.com/computer-vision-events/).

## Get Involved!

There are a lot of ways to get involved in the Computer Vision Meetups. Reach out if you identify with any of these:

- You’d like to speak at an upcoming Meetup
- You have a physical meeting space in one of the Meetup locations and would like to make it available for a Meetup
- You’d like to co-organize a Meetup
- You’d like to co-sponsor a Meetup

Reach out to Meetup co-organizer Jimmy Guerrero on Meetup.com or ping me over [LinkedIn](https://www.linkedin.com/in/jiguerrero/) to discuss how to get you plugged in.

_These Meetups are sponsored by [Voxel51](https://voxel51.com/), the company behind the open source [FiftyOne](https://github.com/voxel51/fiftyone) computer vision toolset. FiftyOne enables data science teams to improve the performance of their computer vision models by helping them curate high quality datasets, evaluate models, find mistakes, visualize embeddings, and get to production faster. It’s easy to [get started](https://voxel51.com/docs/fiftyone/index.html), in just a few minutes._