#41 OpenAI’s “innovation,” LLM Quantization, Feature Selection, and more!

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! This week, we are sharing lots of resources covering some developments in the AI landscape. Today’s articles cover everything from the speed issues with Open AI’s new model to incremental learning for keeping your systems up to date. We also have an interesting app from the community and more!

What’s AI Weekly

This week in What’s AI, I am diving into OpenAI’s latest model, o1. It has some exciting new features that set it apart from previous models like GPT-4o and GPT -4 and even models like Claude, Gemini, and LLaMA. I will break down what o1 does differently, how it works, and what makes it both powerful and, well…a bit slow. Read the full article here or watch the video on my YouTube channel.

— Louis-François Bouchard, Towards AI Co-founder & Head of Community

In collaboration with Bright Data:

Learn How You Can Leverage Web Data To Power Your AI Innovations

Artificial intelligence models, particularly large language models (LLMs), thrive on vast, diverse, and real-time datasets to improve their predictions, learning, and decision-making capabilities. However, traditional datasets are often too static or limited in scope to support the constantly evolving demands of AI systems. This is where web data plays a critical role.

This article explores how leading companies are leveraging web data to power their AI innovations and how Bright Data is helping businesses access data more efficiently, ethically, and elastically.

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Learn AI Together Community section!

Featured Community post from the Discord

Dbdp12 created Problem.ae, a platform that combines the power of AI with expert consultancy to help you find answers to questions about family, health, finances, work, education, and more. Check out the app here and support a fellow community member. If you have any feedback, share it in the thread!

AI poll of the week!

Tell us your experience building AI products using these machines in the thread!

Meme of the week!

Meme shared by hitoriarchie

TAI Curated section

Article of the week

Let’s Build GPT from Scratch for Text Generator by Asad Iqbal

This article walks you through building your own text generator, like GPT, from scratch. It uses KerasNLP to build the model and trains it on the simplebooks-92 corpus, a dataset from several novels.

Our must-read articles

1. Optical Character Recognition (OCR) with CNN-LSTM Attention Seq2Seq by Tan Pengshi Alvin

This article explores an interesting deep learning application called Optical Character Recognition (OCR), which is the reading of text images into binary text information or computer text data. It uses advanced techniques like CNN, LSTM, and attention mechanisms in a seq2seq framework. It breaks down the complex processes into easy-to-understand steps, making it accessible for both beginners and experienced developers.

2. A Brief Practical Guide to LLM Quantization by Raghunaathan

This practical guide provides a concise overview of LLM quantization, explaining its importance and benefits in real-world applications. You’ll learn about various quantization techniques, their implementation, and how they can significantly reduce model size while maintaining accuracy.

3. Feature Selection Unlocked: Exploring Filter, Wrapper, and Embedding Techniques by MD Tahseen Equbal

Feature Selection is the process of identifying and selecting the most important features (variables, predictors) from your dataset that contribute the most to predicting the target variable. This article explores filter, wrapper, and embedding techniques with practical examples and expert insights.

4. Dynamic Time Series Model Updating by Shenggang Li

Incremental learning or online learning allows the system to continuously improve its predictions without refitting the model. This article teaches how to enhance your predictions and improve accuracy by implementing dynamic updates, ensuring your models stay relevant in a rapidly changing environment. It focuses on examples like the Yule model and Neural Networks, showing how they can be updated as new data comes in.

5. Survival of the Fittest Programs: How Machines Evolve to Solve Problems with Genetic Programming by Linh V Nguyen

This article highlights how algorithms mimic the process of evolution, generating solutions that are not only innovative but also highly efficient. This article explores the world of genetic programming, where the principles of natural selection are applied to create programs that adapt and improve over time.

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Published via Towards AI