The article examines the performance of AI models on a 2,000-question test, finding that the top two models were indistinguishable at the conventional 5% significance level. When the test was reduced to 200 questions, a different model would have claimed first place roughly three times out of ten trials. The piece questions the reliability of declaring a single model as the best based on limited testing.
The article explains that when multiple AI models are asked the same question and their answers are aggregated by voting, the resulting opinions are often less independent than the number of models suggests. It presents a formula that estimates the effective number of independent models and validates this with six open models evaluated on 1,500 exam questions.
The article titled "Developers Felt 20% Faster With AI. They Were 19% Slower." discusses a paradoxical finding about developers' productivity when using AI tools. It highlights that while developers perceived a 20% increase in speed, they actually experienced a 19% decrease in performance.
Research-backed cues to detect LLM-generated text along with the mathematical intuition as to 'why' The post Is This Slop? Detecting AI-Generated Content Without a Model appeared first on Towards Data Science .
The article examines the distribution of logits from open AI models, both base and instruction-tuned, to determine how many guesses are required to crack an AI’s supposedly random number. By converting the logits into bits, the author quantifies the predictability of the models’ outputs. The analysis highlights the gap between perceived randomness and actual statistical patterns in AI-generated numbers.
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.
How a single evaluation choice inflated my results by 25 points, and what rebuilding honestly taught me about ML systems people might depend on The post My Fall-Detection Model Scored 94%, and It Was Lying to Me appeared first on Towards Data Science .
The article discusses a research paper suggesting that reward labels have minimal impact on how AI agents learn. The author tested this claim on three different models, finding that one model behaved exactly as the incorrect reward labels instructed. The piece highlights how a single model can be misled by flawed feedback.
How AI has massively changed my day-to-day workflow The post A Day in the Life of a Data Scientist in 2026 appeared first on Towards Data Science .
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.
The article reports on an experiment where an AI agent was allowed to search for evidence whenever it chose. The agent discovered some papers that a standard search missed, but also lost some that the plain search had found, and overall the gains were insufficient to be trusted. The post examines whether agentic Retrieval-Augmented Generation (RAG) truly retrieves better evidence across 298 questions.
The article examines how a small reasoning model responds when presented with incorrect answers to 200 exam questions. By inserting wrong answers and observing the model’s reasoning, the author explores whether the model’s justification aligns with the planted hint or diverges. The study highlights the model’s tendency to follow misleading cues and the implications for evaluating its reasoning process.
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.