Ai

This project implements a Machine Learning (ML) pipeline for the Adver-City synthetic dataset. The dataset is designed for investigating cooperative perception in autonomous vehicles. Overall, this project enables efficient, reproducible modelling of the Adver-City dataset through ML workflows. We have chosen to train a model to identify weather conditions (clear, fog, rain) from nighttime images. Note: full GitRepo available here. Figure 1: Example Scenario (foggy) from the Adver-City dataset. The dataset contains images of a variety of weather conditions (clear, fog, rain) and times of day (day, night). In this project we focus on classifying weather conditions from nighttime images. Operation The project is organized into four main stages. Depending on what your goal is (data exploration, ingestion, or training), the notebooks indicated below describe how to run each stage of the workflow. ...

In a recent interview with Lex Fridman entitled “Dark Matter of Intelligence and Self-Supervised Learning,” outspoken AI pioneer Yann Lecun suggested the next leap in Artificial Intelligence (AI) will come from learning in lower-dimensional latent spaces. “You don’t predict pixels, you predict an abstract representation of pixels.” - Yann Lecun What does he mean and how is it relevant to the future of AI? Let’s back up and consider the context in which this statement was made. Yann was discussing the limitations of current AI systems, particularly those based on deep neural networks. In a previous article, we touched on one such example — Large Language Models (LLMs). LLMs have demonstrated impressive performance across an array of language-related tasks. So popular, a recent AWS study found a “shocking amount of the web” is already LLM-generated. This is problematic, as LLMs trained on this kind of synthetic content break down and lose their ability to generalize. A recent Nature article described this “model collapse” phenomenon in detail. ...

Building on my earlier exploration of custom ChatGPTs, I tried building an AI app from scratch, primarily using vibe coding supported by GitHub Copilot in VSCode. Inspired by a recent Airbnb experience, I developed ContactsnApp: a user-friendly application designed to streamline the extraction and annotation of phone numbers from images using AI models. Comments from the Co-author I asked ChatGPT how the project went. Here it is in her own words: ...