MIT affiliates named 2026 Schmidt Sciences AI2050 Fellows
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
More from the blog
Q&A: Hey AI, who should I vote for this year?
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
GPT-Lab
· gpt-lab.eu
Oct 30, 2026
Black October? The Week AI Changed the Cybersecurity Conversation
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
Introducing AnySpeechRecognizer and AnyDecisionModel: Open Models for Speech and Decisions on Apple Platforms
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Announcing Project ProtoStar: Making Publicly Funded Science AI-Ready
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Related papers
ECCOLA - a Method for Implementing Ethically Aligned AI Systems
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
AI-powered Code Review with LLMs: Early Results
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
LLM-based agents for automating the enhancement of user story quality: An early report
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
System for systematic literature review using multiple AI agents: Concept and an empirical evaluation
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.