Learn AI Together — Towards AI Community Newsletter #9

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, fellow AI enthusiasts!

I really loved this week’s discussion with Jerome about the fuel of AI: Data. He demystified several topics related to building datasets for AI models and had great insights into the field of autonomous vehicles. Jerome is also based in Montreal and has been in the data space for dozens of years. He definitely has a wealth of knowledge to share!

Our community poll showed a strong preference for ChatGPT Plus, highlighting its versatility and dominance in the market. Are you also paying for it or another LLM? I invite you to join the discussion on using multiple LLMs for different applications.

Additionally, check out our curated articles, including insightful pieces on early stopping in ML training, a roundup of significant LLM papers, and an intriguing comparison between Google Gemini and GPT-4, all providing amazing insights from great authors!

Happy reading, and I wish you an amazing weekend!

– Louis-François Bouchard, Towards AI CTO, Head of Community & Co-Founder

What’s AI Weekly

In this week’s What’s AI Podcast episode, Louis-François Bouchard interviewed Jerome Pasquero, Machine Learning Director at Sama, whose expertise in AI, particularly in data, is insightful and timely. They discussed some fascinating facets of AI’s relationship with data and the significant yet often unseen role of human judgment in data annotation. They also explored the ethical challenges in AI. Jerome provided his perspectives on maintaining ethical standards while pushing the boundaries of AI innovation. He also highlighted how AI systems are only as good as the data they’re fed. He stressed the importance of clean, well-structured data, the foundation for robust AI models. If you are curious about how data fuels AI, tune in on YouTube, Spotify, or Apple Podcasts.

Learn AI Together Community section!

Featured Community post from the Discord

Agungpambudi shared a ‘Property Sales Data’ dataset with insights into real estate trends. This dataset provides information about property sales, including property types, tax keys, addresses, and architectural styles. It also helps understand the dynamics of property sales in various districts and neighborhoods, analyzing the number of stories, year built, room counts, square footage, and even the sale prices. Check it out on Kaggle and support a fellow community member. Share your questions and feedback in the thread!

AI poll of the week!

Most of our community members don’t mind paying for ChatGPT Plus over other LLMs, and we agree! Currently, ChatGPT offers diverse capabilities, making it more versatile than other models out there. Share your thoughts on using multiple LLMs for different use cases in the Discord thread!

Collaboration Opportunities

The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week!

1. Usmanyousaaf is looking for a study partner to explore ANN, CNN, RNN, LSTM, GRU, Transformers, pre-trained models, GANs, and more! If you want to work on the math behind these concepts and build projects to boost your resume, reach out to them in the thread!

2. Loccu44 is working on a text-to-motion design web application. They are looking for individuals with expertise in ML/NLP or enthusiastic learners eager to contribute their skills to the project. If you’re interested, get in touch in the thread!

3. Louis-François Bouchard had shared a great opportunity to work at Towards AI as an AI Technical Writer and Developer for LLMs. The opportunity is still open for people in the field to help us build technical & applied learning resources. This would be a paid opportunity, mostly involving LLM-based content creation and the writing of tutorials/course projects. Find more information in the thread here.

Meme of the week!

Meme shared by goust9981

TAI Curated section

Article of the week

Pause for Performance: The Guide to Using Early Stopping in ML and DL Model Training by Shivamshinde

Enhance your ML model with early stopping, a proven method to boost performance and balance training efficiency in this rapidly advancing field. Early stopping avoids overfitting, saves time, and helps accuracy. However, it depends on good validation data and can risk underfitting if halted too soon. Success requires a timely and astute application.

Our must-read articles

1. Top Important LLM Papers for the Week from 08/01 to 14/01 by Youssef Hosni

The papers cover various topics shaping the next generation of language models, from model optimization and scaling to reasoning, benchmarking, and enhancing performance. Keeping up with novel LLM research across these domains will help guide continued progress toward models that are more capable, robust, and aligned with human values.

2. Google Gemini VS GPT-4 by Tim Cvetko

Google’s new Gemini, a Large Language Model, excels in processing diverse data types like images, audio, video, and text, enhancing general and specialized reasoning. Gemini’s innovative design uses a multimodal encoder-decoder to convert various data into a universal format, excelling in benchmarks. Anyone interested in AI can gain clarity from these insights, illuminating the developments shaping tech’s future.

If you want to publish with Towards AI, check our guidelines and sign up. We will publish your work to our network if it meets our editorial policies and standards.

Think a friend would enjoy this too? Share the newsletter and let them join the conversation.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI