MIT Transit Lab to develop an AI platform for public transit agencies
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
More from the blog
TenderIntell at ICSOB 2026: Three Days in Gothenburg
At ICSOB 2026 in Gothenburg, we presented TenderIntell, our work on multi-agent support for tendering. This post looks back at the conference, the idea behind a panel of three AI reviewers who score each proposal independently, and why evaluating agents reliably is the question we keep coming back to. The post TenderIntell at ICSOB 2026: Three Days in Gothenburg appeared first on GPT-Lab.
Three Lincoln Laboratory technologies receive 2026 R&D 100 Awards
A secure operating system, a quantum microsystem platform, and a 5G interference-canceling algorithm are named among the year's most significant innovations.
New research could lead to faster, trustworthy AI models for climate prediction
MIT scientists developed a training method to generate models that simulate the effects of human activities on climate much more quickly than other approaches.
Gemini 4 Carbon is now available
Today, we’re making Gemini 4 Carbon available to developers, enterprises, scientists, and consumers.
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.
The Key Concepts of Ethics of Artificial Intelligence
It is suggested that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.
AI Ethics in Industry: A Research Framework
This paper discusses a research framework for implementing AI ethics in industrial settings and presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.
A Multiple Case Study of Artificial Intelligent System Development in Industry
This investigation revealed different types of AI systems and different AI development approaches, but it is common that business opportunities involving with AI systems are not validated and there is lack of business-driven metrics that guide the development ofAI systems.