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AI-powered tools tend to generate extreme reactions: on one side we have the “It’s magic!” and “best thing ever!” crowd. On the other, we find the “we’re doomed!” camp. These aren’t static or monolithic groups, of course. You might even find yourself on both ends of the spectrum — in the span of a single day.
We think the best way to resist hyperbole is to look at how LLMs (and the products they’ve made possible) work, and how they don’t; what they can achieve, and where they continue to struggle.
For nuanced approaches to the inner workings of AI tools, we invite you to explore this week’s highlights. You’ll see more than a few myths busted, and even more insights gained.
Generative AI Myths, Busted: An Engineer’s Quick Guide
Faced with frequent questions (and increasing dread) about the role and impact of AI, Amy Ma wanted to make it clear to her engineering colleagues what the fuss is all about. The result is a clear, accessible, and levelheaded primer on a technology that even seasoned industry vets sometimes struggle to understand.
Deploying AI Safely and Responsibly
What does it take to build trustworthy AI applications? Stephanie Kirmer and several of her recent IEEE co-panelists take an incisive and pragmatic look at some of the most enduring myths surrounding AI ethics and its day-to-day challenges, from observability to governance.
RAG Explained: Understanding Embeddings, Similarity, and Retrieval
Retrieval-augmented generation has been with us for quite a while now, but some of its components remain under-examined. Maria Mouschoutzi’s latest explainer addresses some common knowledge gaps.
This Week’s Most-Read Stories
Career paths, data analytics, and teaching with AI: explore the stories that have generated the biggest buzz in our community in the past week.
How to Become a Machine Learning Engineer (Step-by-Step), by Egor Howell
My Experiments with NotebookLM for Teaching, by Parul Pandey
From Python to JavaScript: A Playbook for Data Analytics in n8n with Code Node Examples, by Samir Saci
Other Recommended Reads
From immersive deep dives on universal computation to a thorough guide to causal inference in retail analytics, don’t miss our latest crop of standout articles.
- Analysis of Sales Shift in Retail with Causal Impact: A Case Study at Carrefour, by Thanh Liêm Nguyen
- Implementing the Coffee Machine Project in Python Using Object Oriented Programming, by Mahnoor Javed
- Exploring Merit Order and Marginal Abatement Cost Curve in Python, by Himalaya Bir Shrestha
- Rapid Prototyping of Chatbots with Streamlit and Chainlit, by Chinmay Kakatkar
Contribute to TDS
We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?
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