June marked the first anniversary of Google’s AI Principles, which formally outline our pledge to explore the potential of AI in a respectful, ethical and socially beneficial way. For Google Cloud, they also serve as an ongoing commitment to our customers—the tens of thousands of businesses worldwide who rely on Google Cloud AI every day—to deliver the transformative capabilities they need to thrive while aiming to help improve privacy, security, fairness, and the trust of their users.
We strive to build AI aligned with our AI Principles and we’re excited to introduce Explainable AI, which helps humans understand how a machine learning model reaches its conclusions. Read More
Daily Archives: November 25, 2019
Robot debates humans about the dangers of artificial intelligence
An artificial intelligence has debated with humans about the the dangers of AI – narrowly convincing audience members that AI will do more good than harm.
Project Debater, a robot developed by IBM, debated on both sides of the argument, with two human team mates for each side helping it out. Speaking in a female American voice to a crowd at the University of Cambridge Union on Thursday evening, the AI gave each side’s opening statements, using arguments drawn from more than 1100 human submissions ahead of time. Read More
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build,especially in the post-Moore’s Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today. Read More
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