MLCommons Launches and Unites 50+ Global Technology and Academic Leaders in AI and ML

Engineering consortium to deliver industry-wide benchmarks, best practices and datasets to speed computer vision, natural language processing, and speech recognition development for all

MLCommons, an open engineering consortium, launches its industry-academic partnership to accelerate machine learning innovation and broaden access to this critical technology for the public good. The non-profit organization initially formed as MLPerf, now boasts a founding board that includes representatives from Alibaba, Facebook AI, Google, Intel, NVIDIA and Professor Vijay Janapa Reddi of Harvard University; and a broad range of more than 50 founding members. The founding membership includes over 15 startups and small companies that focus on semiconductors, systems, and software from across the globe, as well as researchers from universities like U.C. Berkeley, Stanford, and the University of Toronto.

MLCommons Launches and Unites 50+ Global Technology and Academic Leaders in AI and Machine Learning to Accelerate Innovation in ML

MLCommons will advance development of, and access to, the latest AI and Machine Learning datasets and models, best practices, benchmarks and metrics. An intent is to enable access to machine learning solutions such as computer vision, natural language processing, and speech recognition by as many people, as fast as possible.

“MLCommons has a clear mission – accelerate Machine Learning innovation to ‘raise all boats’ and increase positive impact on society,” said Peter Mattson, President of MLCommons. “We are excited to build on MLPerf and extend its scope and already impressive impact, by bringing together our global partners across industry and academia to develop technologies that benefit everyone.”

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“Machine Learning is a young field that needs industry-wide shared infrastructure and understanding,” said David Kanter, Executive Director of MLCommons. “With our members, MLCommons is the first organization that focuses on collective engineering to build that infrastructure. We are thrilled to launch the organization today to establish measurements, datasets, and development practices that will be essential for fairness and transparency across the community.”

Today’s launch of MLCommons in partnership with its founding members will promote global collaboration to build and share best practices – across industry and academia, software and hardware, from nascent startups to the largest companies. For example, MLCube enables researchers and developers to easily share machine learning models to ensure portability and reproducibility across a wide range of infrastructure, so that innovations can be easily adopted and fuel the next wave of technology.

MLCommons will focus on:

• Benchmarks and Metrics – that deliver transparency and a level playing field for comparing ML systems, software, and solutions, e.g. MLPerf, the industry-standard for machine learning training and inference performance.
• Datasets and Models – that are publicly available and can form the foundation for new capabilities and AI applications, e.g. People’s Speech, the world’s largest public speech-to-text dataset.
• Best Practices – e.g. MLCube, a set of common conventions that enables open and frictionless sharing of ML models across different infrastructure and between researchers and developers around the globe.

Benchmarks and Best Practices Align Industry and Research to Drive AI Forward

The opportunities to apply Machine Learning to benefit everyone are endless; from communication, to healthcare, to making driving safer. To foster the ongoing development, implementation, and sharing of Machine Learning and AI technologies, and to measure progress on quality, speed, and reliability, the industry requires a universally agreed upon set of best practices and metrics.

MLCommons is focused on building these tools for the entire ML community. A cornerstone asset within MLCommons is MLPerf, the industry standard ML benchmark suite that measures full system performance for real applications. With MLPerf, MLCommons is promoting industry wide transparency and making like-for-like comparisons possible.

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Public Datasets that Accelerate Innovation and Accessibility

Machine Learning and AI require high quality datasets, as they are foundational to the performance of new capabilities. To accelerate innovation in ML, MLCommons is committed to the creation of large-scale, high-quality public datasets that are shared and made accessible to all.

An early example of such an initiative for MLCommons is People’s Speech, the world’s largest public speech-to-text dataset in multiple languages that will enable better speech-based assistance. MLCommons has collected more than 80,000 hours of speech with the goal of democratizing speech technology. With People’s Speech, MLCommons will create opportunities to extend the reach of advanced speech technologies to many more languages and help to offer the benefits of speech assistance to the entire world population rather than confining it to speakers of the most common languages.

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