This month, IBM celebrates a milestone in Silicon Valley. Forty years ago, we opened the doors to our first major software development lab in the world. IBM’s Silicon Valley Lab (SVL) would produce some of the defining technologies of the last four decades: from the first integrations of the relational database to today’s development of machine learning technology for the world’s most important data.
IBM was a pioneer in the valley, having opened its first production facility in the region almost 30 years before Silicon Valley was given its name.
SVL was dedicated on the morning of October 7, 1977. San Jose Mayor Janet Gray Hayes – the first woman ever to be mayor of a city of more than 500,000 people – delivered a message at the opening ceremony. “Today is a proud day. We’re after quality, and that’s what we have in this laboratory,” she said.
In the early 80s, the lab’s areas of focus included database and transaction management and language development. These were the early days of Db2 and IBM’s Information Management System (IMS). Today, SVL is focused on the development of machine learning and artificial intelligence technologies for global enterprises.
To bring machine learning to businesses around the world, SVL launched IBM’s first Machine Learning Hub, a cohort of IBM data scientists who work side-by-side with customers to get their hands on machine learning technology, ML techniques and different ways to use ML and AI in their businesses. The ML Hub partners not only with IBM customers, but also with nonprofits and startups to extend the power of machine learning to communities that need it most.
Today, there are five Machine Learning Hubs operating around the world in Boeblingen, San Jose, Beijing, Bangalore and Toronto.
The lab has also incorporated machine learning into IBM’s leading analytics platforms. Designers and developers in Silicon Valley helped build IBM’s Data Science Experience (DSX), a platform that makes machine learning accessible in an integrated data science platform. SVLers also helped build Watson Machine Learning, spearheading a non-programmatic user experience, enabling fast ML model building and lowering the entry point for people interested in using machine learning.
As this month marks SVL’s 40th anniversary, San Jose Mayor Sam Liccardo visited the lab to meet with employees, discuss the lab’s place in Silicon Valley history and celebrate the continued partnership between IBM and the City of San Jose.
During his visit, Mayor Liccardo applauded IBM’s record of local engagement in San Jose. The lab has been part of the fabric of the city since its first dedication in 1977 – from SVL’s ongoing Girls Who Code summer immersion program to IBM’s STEM mentorship for San Jose students, which encourages underrepresented minorities to pursue math and science higher education.
Mayor Liccardo also expressed appreciation for IBM’s Smarter Cities civic partnership, which kicks off later this month. IBM has committed $500,000 in time, resources and talent toward San Jose’s affordable housing initiative. SVL volunteers will lend their data science and machine learning expertise to help unearth insights related to San Jose’s affordable housing challenges, help the city better understand the issue and unearth potential solutions.
“You have been an integral part – a catalyst – for what has become this amazing Silicon Valley ecosystem. So, thank you for being part of this, and driving it for 40 years,” said Mayor Liccardo.
Dinesh Nirmal, VP of Analytics Development and SVL site executive, closed the anniversary event with a final word about the people behind the innovation. “We go on three principles: people, products and customers… it all starts with all of you.”
SVL is home to IBM Fellows, Distinguished Engineers, Senior Technical Staff Members, developers, designers and data scientists who continue to invent, build and innovate – defining the next 40 years of the lab’s legacy in the Valley. To learn more about IBM’s machine learning projects and research, both at SVL and at our labs around the world, follow Inside Machine Learning.