MIT Responsible AI for Social Empowerment and Education (RAISE) recently celebrated the second annual Day of AI with two flagship local events. The Edward M. Kennedy Institute for the U.S. Senate in Boston hosted a human rights and data policy-focused event that was streamed worldwide. Dearborn STEM Academy in Roxbury, Massachusetts, hosted a student workshop…
We are witnessing a historic, global paradigm shift driven by dramatic improvements in AI. As AI has evolved from predictive to generative, more businesses are taking notice, with enterprise adoption of AI more than doubling since 2017. According to McKinsey, 63% of respondents expect their organizations’ investment in AI to increase over the next three…
Posted by Ramki Gummadi, Software Engineer, Google and Kevin Chen, Software Engineer, YouTube Caching is a ubiquitous idea in computer science that significantly improves the performance of storage and retrieval systems by storing a subset of popular items closer to the client based on request patterns. An important algorithmic piece of cache management is the…
In some San Francisco neighborhoods, at certain hours of the night, it seems as if one in 10 cars on the road has no driver behind the wheel. These are not experimental test vehicles, and this is not a drill. Many of San Francisco’s ghostly driverless cars are commercial robotaxis, directly competing with taxis, Uber…
The MIT-Pillar AI Collective has announced its first six grant recipients. Students, alumni, and postdocs working on a broad range of topics in artificial intelligence, machine learning, and data science will receive funding and support for research projects that could translate into commercially viable products or companies. These grants are intended to help students explore…
Posted by Zalán Borsos, Research Software Engineer, and Marco Tagliasacchi, Senior Staff Research Scientist, Google Research The recent progress in generative AI unlocked the possibility of creating new content in several different domains, including text, vision and audio. These models often rely on the fact that raw data is first converted to a compressed format…
A significant proportion of people paid to train AI models may be themselves outsourcing that work to AI, a new study has found. It takes an incredible amount of data to train AI systems to perform specific tasks accurately and reliably. Many companies pay gig workers on platforms like Mechanical Turk to complete tasks that…
Posted by Jimmy Tobin and Katrin Tomanek, Software Engineers, Google Research, AI for Social Good Google’s AI for Social Good team consists of researchers, engineers, volunteers, and others with a shared focus on positive social impact. Our mission is to demonstrate AI’s societal benefit by enabling real-world value, with projects spanning work in public health,…
Posted by Trond Andersen and Yuri Lensky, Research Scientists, Google Quantum AI Team Imagine you’re shown two identical objects and then asked to close your eyes. When you open your eyes, you see the same two objects in the same position. How can you determine if they have been swapped back and forth? Intuition and…
Multi-tenant systems are invaluable for modern, fast-paced businesses. These systems allow multiple users and teams to access and use them at the same time. Machine learning operations (MLOps) teams, in particular, benefit greatly from using multi-tenant systems. MLOps teams that don’t leverage multi-tenant systems can fall victim to inefficiency, inconsistency, duplicative work, and bumpy onboarding—adding…