Towards Data Science By Sabrine Bendimerad

Our AI Has an Invisible Footprint — Let's see How to Measure It

Read the original on Towards Data Science →

The article discusses the challenge of measuring the impact of AI systems, noting that obtaining data is now easier than deciding how to interpret it. It highlights the difficulty of turning raw numbers into actionable insights. The piece was originally published on Towards Data Science.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Towards Data Science.

Towards Data Science
Sep 29

AI Made Data Scientists Faster. Now It’s Expanding the Job.

AI has accelerated data scientists’ productivity, but its influence extends beyond speed. The technology is reshaping who owns data, how judgment is exercised, and the overall career trajectory of data scientists. These changes signal a broader transformation in the field’s structure and responsibilities.

By Yu Dong
Towards Data Science
Oct 1

Your AI Bill Is a Toll Booth. Stop Paying Twice.

The article titled "Your AI Bill Is a Toll Booth. Stop Paying Twice." discusses how users are unexpectedly paying more for AI services than anticipated, likening the experience to a toll booth where one pays twice. It highlights the unseen costs that can arise when using AI tools and urges readers to be vigilant about their expenses. The piece was first published on Towards Data Science.

By Gursimar Singh
Towards Data Science
Oct 8

Everyone Is Selling AI at You — Here’s How to Keep Your Judgement

The article titled "Everyone Is Selling AI at You — Here’s How to Keep Your Judgement" discusses three strategies for navigating the current AI hype. It offers practical moves to help readers maintain critical thinking and discern genuine value from marketing claims. The piece was originally published on Towards Data Science.

By Janna Lipenkova
Towards Data Science
Sep 24

Beyond RAGs: Building Actually Truthful AI Harnesses

The article "Beyond RAGs: Building Actually Truthful AI Harnesses" discusses the limitations of Retrieval-Augmented Generation (RAG) systems, emphasizing that retrieval alone does not guarantee evidence for AI claims. It explores methods for constructing AI systems that can substantiate their statements, moving beyond simple retrieval to more robust proof mechanisms. The piece highlights the importance of developing AI that can verify its own outputs rather than merely retrieve information.

By Ari Joury, PhD