What AI Agents Should Never Do on Their Own
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .
As AI gets smarter, the real differentiator may be how well humans regulate their own thinking. The post Meta-Cognitive Regulation Might Be the Most Important AI Skill Nobody Is Talking About appeared first on Towards Data Science .
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .
The article titled "10 Things I’m Learning Beyond AI to Become More Technologically Fluent" discusses the author's exploration of various technologies that are shaping the future, beyond just artificial intelligence. It is presented as part one of a series, focusing on understanding these emerging technologies and their impact.
arXiv:2607. 11881v1 Announce Type: cross Abstract: Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more.
What our over-dependence on external consulting teaches us about delegating our minds to machines The post The Big Con of Agentic AI appeared first on Towards Data Science .
The study examines how AI’s growing presence in computing affects the perceived importance of cognitive skills among students and experts. Findings suggest that most cognitive skills will become less critical in an AI‑rich future, while critical thinking remains essential. Interviews provide reasons for these shifts and offer guidance for preparing future computing students.
The article titled "5 AI Skills That Will Keep Data Scientists Relevant in 2027" outlines five specific AI competencies, explaining what each skill addresses and providing runnable code snippets that readers can directly paste into a notebook. It serves as a practical guide for data scientists aiming to stay current with emerging AI technologies.
Two competing perspectives on fluid intelligence (gf) measures propose that performance is primarily constrained either by working memory capacity or by the ability to induce novel relations. The first perspective is currently dominant in measurement, as evident from the use of a limited set of recurring rules, whereas the second perspective is reflected in many definitions but rarely present in measurement.
arXiv:2603. 29693v3 Announce Type: replace Abstract: A robust decision-making process must take into account uncertainty, especially when the choice involves inherent risks.
The article outlines four practical applications of AI for PhD students: locating relevant citations, consolidating code snippets, fact‑checking research claims, and preparing for the thesis defence. It highlights how AI tools can streamline the research process and improve the quality of academic work.
The article discusses how agentic AI is reshaping the analytics stack by taking over more execution tasks. It raises the question of which responsibilities should remain with human analysts versus AI agents and explores the importance of this distinction. The piece highlights the evolving role of AI in analytics and the need to define clear boundaries between human and machine work.
They aren’t designed, you can’t help perceiving one anyway, and that makes them an engineering problem almost no one is solving. The post Where Does an AI’s Personality Actually Come From?
If you are a programmer and you don't feel "special" anymore, you are not alone The post The Era of No-Code AI: What You Need to Know appeared first on Towards Data Science .