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Learn AI Together — Towards AI Community Newsletter #3
Artificial Intelligence   Latest   Machine Learning

Learn AI Together — Towards AI Community Newsletter #3

Last Updated on December 11, 2023 by Editorial Team

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! I’m thrilled to share this week’s podcast episode, where I chat with Ken Jee, a famous AI persona in the field. Ken’s journey in data science is super inspiring, especially his take on AI in daily life and sports. It’s a must-listen for anyone curious about real-world AI and building a better portfolio!

For this week’s iteration, we have very cool stuff from our community members, like Rmarquet’s nifty text annotation tool, perfect for NLP enthusiasts. Plus, there are many collaboration opportunities this week — maybe you’ll find your next AI project buddy!

This week’s Towards AI curated section is also fantastic, with some unique topics related to AI. I was excited to see those published. Dive into the newsletter, tune into the podcast, and let’s keep exploring the amazing world of AI together! 😀

Louis-François Bouchard, Towards AI Co-founder and CTO

What’s AI Weekly

In this week’s What’s AI Podcast episode, Louis Bouchard interviewed Ken Jee, a prominent figure in data science and AI, to explore various aspects of these fields. Ken shares his journey into data science, highlighting the practical applications of data analytics in everyday life and sports, setting the stage for discussing the broader implications of AI and data science. They also discuss the world of AI startups, the current trends, and the reliance of new companies on AI technologies, mainly focusing on Large Language Models (LLMs) like ChatGPT. If you are interested in better leveraging AI for your work and productivity, building things (startups), or the world of podcasting, tune in to the episode on Spotify, Apple Podcasts, or YouTube.

Learn AI Together Community section!

Featured Community post from the Discord

Rmarquet has recently released a Streamlit component for text annotation. This text annotation tool allows users to streamline their text analysis and annotation processes. It’s relevant for everyone working on natural language processing, machine learning, or other text-based projects. This component can help annotate and organize your data efficiently. Check it out on GitHub and support a fellow community member! Share your feedback in the thread.

AI poll of the week!

Most community members prefer experiential learning; share all the cool projects you are working on and what you are learning by joining 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. Yash_907 is looking for a study partner. They are currently looking for a partner to study algorithms, mathematics, and other AI and ML-focused topics. If you are interested, connect with them in the thread.

2. Alkoridm_91733 is looking for collaborators to work on an AI/ML Project. They are looking for someone from the DFW Metroplex, TX, or US CST Timezone. If that’s you, connect in the thread here.

3. Anaa1173 is looking for someone to help build an AI chatbot fine-tuned on Neville Goddard lectures. The project also needs a prompt-based website with a text and audio response mechanism. If you can help them with this, reach out in the thread.

Meme of the week!

Meme shared by hitoriarchie

TAI Curated section

Article of the week

Mastering Recommendation Engines with Neural Collaborative Filtering by Priyansh Soni

This article is your go-to manual for crafting a recommendation engine with Neural Collaborative Filtering (NCF). Starting with a swift introduction to recommendation engines, we’ll dance through their different types, focusing primarily on model-based collaborative filtering, leading to the working of neural recommendation engines.

Our must-read articles

Would You Use ANOVA for Feature Selection? by Sai Viswanth

We often forget the most crucial step when developing a Machine learning model — Feature Selection. Not selecting the right features correlated to the target variable can prevent your model from reaching the potential performance. This article focuses on ANOVA, a filter method to select features highly linked to our target variable.

Top Important LLM Papers for the Week from 13/11 to 19/11 by Youssef Hosni

New pivotal papers on LLMs tackle benchmarking, training, and ethics, advancing our understanding. Staying updated is crucial for experts. The papers show progress in improving LLMs, the key to boosting AI reasoning and performance. Prioritizing their alignment with human values is essential for responsible and ethical AI development. These papers keep you well-informed on the fast-evolving AI field, essential for practitioners and enthusiasts to stay ahead in a future where LLMs drive innovation.

How Scientists Are Using AI To Communicate With Other Species by Andrew Akhigbe

Discover the newest computer vision advancements this week with a roundup of November 2023’s scholarly works on image recognition and creating 3D models from text. “MetaDreamer” is a study that merges text with 3D creation, innovating by separating geometry from texture and extending computer vision beyond interpretation to complex generation. It’s part of a wider compendium exploring advanced vision language models and video analysis. Discover key AI and imagery insights for your research or conversation in this curated collection of computer vision papers.

Unboxing Weights, Biases, Loss: Hone in on Deep Learning by Mainak Mitra

Understanding neural networks involves grasping weights, biases, and loss functions, which are crucial for shaping connections and deciphering patterns in data. Neural learning hinges on fine-tuning synapses, bolstering crucial features, and reducing noise, enabling neural networks to recognize core patterns effectively. Dive deeper into these basics to master deep learning.

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