Learn AI Together — Towards AI Community Newsletter #14

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning everyone! In this week’s Learn AI Together newsletter edition, I’m excited to introduce a new video series that will dive into AI’s impact on our society. In this first episode, I entered the world of journalism and news coverage. I tried to cover all the current and upcoming potential use cases of AI in the journalism industry, from the automation of news reporting to the complex ethical landscape shaped by deepfakes and misinformation. It’s a fun and relaxing video to learn more about the current usage of AI in different industries, and I’d love to hear your thoughts on this series and know what industry you’d like me to cover next!

This week, we have many collaboration opportunities and interesting reads from the community. Whether you’re here to explore the impact of AI, engage with community-driven projects, or expand your technical knowledge, this week’s newsletter is packed with content that will inspire and inform you. As the poll suggests, we also have lots of work in progress for upcoming courses. We’d love to hear from you at Towards AI to better understand your needs (level, interests, experience…) when learning AI-related topics!

Let’s dive in!

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

What’s AI Weekly

From automated reporting to personalized content curation, AI is reshaping how news is created, distributed, and consumed, affecting everyone. And this is just the beginning. The potential for AI in journalism is vast, and its full impact will be just as immense, if not more, than the Internet. This week in the What’s AI, Louis-François Bouchard dives into the world of journalism to cover everything AI-related that is or will be applicable, from generated articles, the future of journalists, dealing with biases in AI, and keeping objectivity to the ethical dilemmas posed by deepfakes and misinformation. Tune in on YouTube or read the complete article here!

Learn AI Together Community section!

Featured Community post from Discord

_victord has developed Bind, a platform that allows you to build a community from your user data. The bot identifies users as they join your community, assigns roles automatically using your product’s data, and encourages new members to follow a call to action. Check it out here and support a fellow community member! Share your feedback and questions in the thread!

AI poll of the week!

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. Samuraipizza is pursuing a self-education project to train an LLM with time series data and let it predict the next iteration. They are currently seeking fellow computational chemists/physicists. If this is your domain, connect in the thread!
2. Jam_zaw is seeking a programmer to collaborate on a research project focused on session-based recommendation systems aimed at publication in high-ranking scientific journals. It is a paid opportunity with a commitment of 3 hours per week. If you are interested, reach out in the thread!
3. Electrixyt is looking for individuals to join their startup, developing AI models for stock price predictions, house prices, and more. They have a place in their server to learn AI development with others, talk and have fun, and share the models that you built. If you are excited about working in the industry, connect in the thread!
4. Lxfted is building a stock trading bot and trying to get some ideas from other people. If you’re interested in working, reach out in the thread!

Meme of the week!

Meme shared by ghost_in_the_machine

TAI Curated section

Article of the week

LLM Quantization Techniques- GPTQ by Rajesh K

Large Language Models (LLMs) have received high praise for their expertise in understanding code and answering complex questions. However, this increase in complexity requires resource-intensive hardware solutions, which can be expensive and require specialized hardware. Quantization reduces the computational memory cost of running calculations such as 8-bit integers over 32-bit floating point. This method allows much smaller model representation, takes less power, and matrix multiplication with integer arithmetic.

Our must-read articles

1. Can Machine Learning Outperform Statistical Models for Time Series Forecasting? by Satyajit Chaudhuri

The above article is an experimental study that uses statistical and machine learning-based forecasts to predict the future for a practical use case. The study compares the forecasts based on certain accuracy metrics to judge if the machine learning models can compete with their traditional counterparts.

2. Inside OpenAI Sora: Five Key Technical Details We Learned About the Amazing Video Generation Model by Jesus Rodriguez

Last week, OpenAI unveiled its latest work on generative video models with Sora. This remarkable text-to-video model can generate up to a minute of high-quality video. The release took the generative AI world by storm with extensive debate on X and media publications. OpenAI hasn’t published too many details from the technical side, but some key details were highlighted as part of the release.

3. The Transformer Architecture From a Top View by Dimitris Effrosynidis

Transformer architecture significantly improved natural language task performance compared to earlier RNNs. Transformers revolutionized NLP by leveraging self-attention mechanisms, allowing the model to learn the relevance and context of all words in a sentence. This article explores the architecture step by step.

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