The article describes an experiment where the author created a trap for AI image judging systems. Three vision AI models failed to recognize the intended image, while GPT‑4o generated answers and Claude consistently responded with the letter 'B'. The piece highlights the differing capabilities and limitations of various AI models in image interpretation tasks.
The article describes a controlled experiment in which four AI assistants—Gemini, DeepSeek, ChatGPT, and Claude—were tested on a forecasting task that included four hidden traps: leakage, reporting delays, promotion effects, and structural breaks. The study examines how each assistant handled these challenges and compares their performance. The post was published on Towards Data Science.
One near miss, four months of running agents, and the question almost nobody is asking: what are you supposed to do while the AI writes the code? The post AI Made Me 5x Faster. It Also Made Me 5x Wors...
A tiny GPT model was trained to memorize every fact presented to it, yet it failed to answer any question that required combining two of those facts. The author tested a one-line fix suggested in a recent paper, which only partially resolved the issue. The post highlights the limitations of small language models in integrating multiple pieces of information.
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.
Simon Willison introduces GPT‑6 Astra, a new model that offers improved attention to detail, better prompt comprehension, and the ability to generate more sophisticated outputs. The model excels at creating 3D renderings, producing detailed scenes such as gardens, shipyards, animals, cityscapes, and even Dyson spheres. Willison highlights its whimsical creativity, noting examples like a pelican wearing a red neckerchief riding a bicycle.
Without these, Claude will be confidently wrong. The post 4 Lines You Should Include in Your Claude Skill appeared first on Towards Data Science .
The article titled "The AI That Learned to Understand Long After It Stopped Trying" discusses a small, strange discovery in machine learning known as grokking. It highlights how this phenomenon involves an AI developing understanding after ceasing to actively try. The piece was originally published on Towards Data Science.
Simon Willison reflects on the evolving role of software developers in the age of AI, noting that while AI can produce high‑quality code, it also enables poor execution that leads to project failures. He argues that the industry is beginning to recognize the continued need for human collaboration and expertise to truly innovate. The piece highlights the tension between automation and the essential human element in software creation.
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.
Because the alternative is much too dangerous The post We Should Train AI to Betray Its Users appeared first on Towards Data Science .
arXiv:2607. 18514v1 Announce Type: cross Abstract: Visual diagrams, figures, and tables are central to scientific papers, and convey information beyond what is captured in text.
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.