October AI, Machine Learning & Data Science Meetup

**Zoom Link**

[https://voxel51.com/computer-vision-events/october-ai-machine-learning-data-science-meetup/](https://voxel51.com/computer-vision-events/october-ai-machine-learning-data-science-meetup/)

**Glacier Monitoring with Computer Vision Models**
The temporal variability of marine-terminating glacier front positions provides valuable information on the state of the glaciers. Therefore, the position of these fronts is an important parameter influencing the accuracy of climate models. To obtain the position, satellite imagery has traditionally been analyzed by hand. As the amount of satellite imagery and the need for accurate climate models is increasing, deep learning techniques are applied to extract the glacier front position from satellite images. In this talk, state-of-the-art models for this purpose will be discussed.

[Nora Gourmelon](https://www.linkedin.com/in/nora-gourmelon/) is a PhD candidate in Computer Science at the Friedrich-Alexander-Universität Erlangen-Nürnberg working on AI for Earth. Her main focus lies on the segmentation of glacier calving fronts in Synthetic Aperture Radar (SAR) satellite imagery.

**Automatic Prompt Optimization with “Gradient Descent” and Beam Search**
Large Language Models (LLMs) have shown impressive performance but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort. We propose a simple and nonparametric solution to this problem, Automatic Prompt Optimization (APO), which is inspired by numerical gradient descent to automatically improve prompts, assuming access to training data and an LLM API. Our experiments suggest this method can outperform prior prompt editing techniques and improve an initial prompt’s performance by up to 31%, by using data to rewrite vague task descriptions into more precise annotation instructions.

[Reid Pryzant](https://www.linkedin.com/in/rpryzant/) is a Senior Research Scientist at Microsoft, and former Computer Science PhD at Stanford University advised by Dan Jurafsky. His work has won outstanding research awards from CVPR, AAAI, and the National Science Foundation.

**Build Natural Language Applications with txtai**
This talk will introduce [txtai](https://github.com/neuml/txtai) and show how it can be used for semantic search, LLM orchestration and language model workflows. An overview of the embeddings database architecture will be discussed along with how vector indexes (sparse and dense), graph networks and relational databases connect together. Example use cases will cover SQL-driven vector search, topic modeling and retrieval augmented generation.

[David Mezzetti](https://www.linkedin.com/in/davidmezzetti/) is the founder of NeuML, the company behind txtai. He is building a suite of open-source, easy-to-use, semantic search and workflow applications. Dave previously co-founded and built Data Works into a 50+ person well-respected software services company leading to a successful acquisition.