Open engineering consortium MLCommons has launched as an industry-academic partnership to accelerate machine learning innovation and broaden access to this critical technology for the public good. The non-profit organisation initially formed as MLPerf in 2018 and now boasts a founding board that includes representatives from Alibaba, Facebook AI, Google, Intel, Nvidia and Professor Vijay Janapa Reddi of Harvard University, as well as 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 will advance development of, and access to, the latest AI and machine learning datasets and models, best practices, benchmarks and metrics. It wants to enable access to machine learning products such as computer vision, natural language processing, and speech recognition by as many people, as fast as possible.
The 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.
MLCommons said it’s focused on building for the entire ML community a set of best practices and metrics to foster the ongoing development, implementation, and sharing of machine learning and AI technologies, and to measure progress on quality, speed, and reliability. A cornerstone asset within MLCommons is MLPerf, the industry standard ML benchmark suite that measures full system performance for real applications.
Machine learning and AI also require high quality datasets, as they are foundational to the performance of new functionalities. To accelerate innovation in ML, MLCommons is committed to the creation of large-scale, public datasets that are shared and made accessible to all.
An early example of such an initiative for MLCommons is People’s Speech, a public speech-to-text dataset in multiple languages that will enable better speech-based assistance. MLCommons collected more than 80,000 hours of speech with the goal of democratising speech technology.
With People’s Speech, MLCommons said it 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.
Credit: Google News