Jennifer Neville, who initially had no interest in computer science, found herself in the field and has since focused on uncovering the unexpected failures that challenge AI’s ability to manage complexity. In her article, she reflects on the career path that led her to this niche, highlighting the moments of both progress and setbacks that shaped her work. The piece emphasizes how these surprising failures reveal the limits of current AI systems.
The article "AI Harms and Their Technical Causes" discusses how specific technical decisions in AI development can result in safety failures. It examines the underlying causes of these harms and offers guidance on alternative approaches to mitigate risks. The piece highlights the importance of thoughtful design choices in preventing unintended negative outcomes from AI systems.
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.
Research Manager Mike Walker discusses his career across hacker culture, cybersecurity, and AI, offering insights into trust, teamwork, leadership, and the importance of curiosity, judgment, and agility for tackling tough problems. He emphasizes that understanding the rules is essential for effective problem‑solving in these fields.
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 .
Microsoft Research Focus – Edition #97 features host Sowbhagya Suresh discussing recent developments across Microsoft. Topics include challenges faced by coding agents, insights into how people use AI at work, and advancements in extended‑range weather forecasting. The episode highlights cutting‑edge research and practical applications within the company.
The article discusses how computer programming fundamentally involves problem‑solving with computers and managing complexity. It argues that these skills will remain valuable and sustain a viable career even as AI tools become more prevalent.
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 .
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?
arXiv:2408. 02379v2 Announce Type: replace-cross Abstract: Developing and certifying safe - or so-called trustworthy - AI has become an increasingly salient issue, especially in light of upcoming regulation such as the EU AI Act.
The article titled "An Introduction to Jev" discusses an AI system that focuses on making decisions rather than generating text. It highlights Jev’s unique approach to decision-making within the broader context of AI development. The piece was originally published on Towards Data Science.
The article explores the effects of removing a search box from an AI agent and instead providing it with typed tools, hard bounds, and a gate that it cannot bypass. It examines how the agent navigates a knowledge graph within strict limits and discusses findings from four models and one incorrect prediction regarding the value of this approach.