Self-Driving Cars Could Someday Take Requests

This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore. The idea of letting a machine do the driving for you may put a lot of people off autonomous vehicles. But research could make it possible to backseat-drive an…

Source: IEEE Spectrum

Automatically aggregated summary — full article and all rights belong to the original publisher.

How to encourage smarter AI use in the classroom

This article is from Making AI Work, MIT Technology Review’s limited-run newsletter examining how to apply LLMs across industries. To receive it in your inbox, sign up here. Chatbots took many schools by surprise upon their release a few years ago. Suddenly, students carried an…

Source: MIT Technology Review

Automatically aggregated summary — full article and all rights belong to the original publisher.

What It Takes to Be an Adaptable Engineer

The AI boom has disrupted the way engineers work, introducing new tools to learn, raising expectations for what teams can achieve in a workday, and making it harder to get hired in the first place. This makes it difficult to advise students on which specific…

Source: IEEE Spectrum

Automatically aggregated summary — full article and all rights belong to the original publisher.

The Download: kids outlearning AI, and space travel agents

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Kids outlearn AI—and we still don’t know why Teaching a computer to use human language requires an inhuman amount of data.…

Source: MIT Technology Review

Automatically aggregated summary — full article and all rights belong to the original publisher.

SKILL-RAG: Self-Knowledge Induced Learning and Filtering for Retrieval-Augmented Generation

arXiv:2509.20377v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has significantly improved the performance of large language models (LLMs) on knowledge-intensive tasks in recent years. However, since retrieval systems may return irrelevant content, incorporating such information into the model often leads to hallucinations. Thus, identifying and…

Source: cs.AI updates on arXiv.org

Automatically aggregated summary — full article and all rights belong to the original publisher.

Self-Revising Discovery Systems for Science: A Categorical Framework for Agentic Artificial Intelligence

arXiv:2606.01444v2 Announce Type: replace Abstract: Scientific discovery is not only answer generation but revision of the representational regime in which evidence, artifacts, operations, and verifiers are typed. We develop a category-theoretic account of agentic discovery for materials science. In a fixed regime b with schema…

Source: cs.AI updates on arXiv.org

Automatically aggregated summary — full article and all rights belong to the original publisher.