Professor Emeritus Dimitri Bertsekas, influential computer scientist and prolific author, dies at 83
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
More from the blog
Q&A: The “collision” between AI, your brain, and society
In a new book, Professor Eric So explores how the technology affects our thinking skills.
Q&A: Building accountability behind our words — and AI’s — in civic conversations
Professor Deb Roy contends there should be someone who is “answerable” for every piece of text in public life, no matter its origins.
Using AI to mitigate the growing environmental threat of data centers
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
What AI gets wrong and what failure teaches us
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Related papers
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.
Time for AI (Ethics) Maturity Model Is Now
It is argued that AI software is still software and needs to be approached from the software development perspective, and whether the focus should be on AI ethics or the quality of an AI system, called a maturity model for the development of AI systems is discussed.
Experimenting with Multi-Agent Software Development: Towards a Unified Platform
A unified platform that utilizes multiple artificial intelligence agents to automate the process of transforming user requirements into well-organized deliverables, including user stories, prioritization, and UML sequence diagrams, along with the modular approach to APIs, unit tests, and end-to-end tests.
Continuous experimentation on artificial intelligence software: a research agenda
A holistic view of an iterative, continuous approach to develop industrial AI software basing on business goals, requirements and Minimum Viable Products is described and a research agenda with seven questions for future studies is proposed.