Learn AI Together — Towards AI Community Newsletter #4

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, fellow AI enthusiasts! In this issue, we share a new video for our course video series with Activeloop on enhancing the Large Language Model (LLM) performance.

This video shows various approaches, including training from scratch, fine-tuning, advanced prompt engineering, and retrieval augmented generation (RAG). This is an essential watch for developers and AI enthusiasts looking to make significant strides in LLM efficiency and effectiveness.

I also want to highlight this week’s community spotlight, which features an innovative tool by Nkjorg named Wisecraft AI. This new Google Chrome extension provides a way to interact with text online by applying mental models for critical thinking. We love to see new tools created by you guys!

We’d also love to hear your thoughts on this week’s poll to better understand our community’s needs for the upcoming applied course we are building.

What’s AI Weekly

This week in What’s AI, Louis Bouchard shared an in-depth video on enhancing LLM performance and balancing quality, costs, and ease of use. He also focuses on learning to choose between training from scratch, fine-tuning, (advanced) prompt engineering, and Retrieval Augmented Generation (RAG) with Activeloop’s Deep Memory. Watch the full video that guides developers and AI enthusiasts on improving LLMs, offering methods for both minor and significant advancements.

Learn AI Together Community section!

Featured Community post from the Discord

Nkjorg has just launched Wisecraft AI. It allows you to apply mental models to highlighted text in Google Chrome. The primary purpose is to make critical feedback accessible to everyone. It allows users to choose from 6 different mental models that offer different perspectives and enable further thinking and reflection, like First Principles Thinking, Second-Order Thinking, Inversion, and more. Check out the extension here and support a fellow community member. Share more model ideas, feedback, and questions in the thread here!

AI poll of the week!

Do you also believe training on your own GPU is vital? Share your preferred hardware in the thread and join the discussion on Discord.

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. Das_search is working on ideas for giving an LLM more forthright control of a simulated persistent emotional state that incorporates memory with a sub-goal of defining a process that can aid in converting math equations to Alphabetical notation. They are currently looking for a group of individuals to discuss these ideas. If you find this interesting, connect with them in the chat!

2. Afk_legacy is studying to get deeper into Machine Learning by going through math and stats material. They are currently looking for a partner to learn together and create some cool portfolio projects. If you are also looking for extra motivation, reach out in the thread!

3. Vecthor4461 is working on an approach for social media management, potentially solving an open issue in the current market. They are looking for a back-end developer passionate about AI to build an AI-powered app. If you want to try this out, get in touch in the thread!

Meme of the week!

Meme shared by ghost_in_the_machine

TAI Curated section

Article of the week

RLHF Training Pipeline for LLMs Using Huggingface U+1F917 by Marcello Politi

Harnessing the power of Large Language Models (LLMs) just got clearer with a new guide on using the Huggingface library. Domain-specific LLMs are advancing, and using Reinforcement Learning with Human Feedback (RLHF) enhances their accuracy, coherence, and ethical alignment. In a detailed introduction to RLHF, the complete training pipeline is laid out over three phases, enabling professionals and enthusiasts to cultivate models that exceed in delivering quality while steering clear of biases.

Our must-read articles

1. Top Important Computer Vision Papers for the Week from 20/11 to 26/11 by Youssef Hosni

This article provides a comprehensive overview of the most significant papers published in the fourth week of November 2023, highlighting the latest research and advancements in computer vision. Whether you’re a researcher, practitioner, or enthusiast, this article will provide valuable insights into the state-of-the-art techniques and tools in computer vision.

2. Demystifying Time Series Outliers: 2/4 by Andrea Ianni

The author clarifies a complex topic using soccer data. They apply basic statistics to #rovella tweets, easily spotting outliers and exposing social patterns in sports interactions. This case study on time series analysis and outliers teaches practical techniques beneficial for various fields beyond sports analytics, offering a simple entry into data science.

3. Top Important LLM Papers for the Week from 20/11 to 26/11 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.

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