We stand at the threshold of a fundamental discontinuity brought about by the confluence of at least two parallel developments viz.
- An increased ability to sense and produce enormous amounts of data (the digital universe continues to double every 2 years)
- The ability to create extremely large clusters (~200,000 cores) driven by parallel runtimes. These developments, the first of which has been occurring across domains, have enabled machine learning and other data driven approaches to become the paradigm of choice for complex problem solving.
Driverless cars, face recognition and other behavioral biometrics with accuracy good enough to be deployable as a primary border control mechanism, paintings that are generated entirely by a machine to be consistent with a style of painting, and the use of the entire literature of published knowledge to enable truly personalized medicine are a few examples of what has been made possible due to advances in machine learning. Indeed, the results go far beyond convincing, both in terms of accuracy and in terms of the ease of construction of the machine.
There is now a considerable opportunity to improving life-at-large based on these capabilities. The “Mphasis Laboratory for Machine Learning and Computational Thinking” was established thanks to a generous contribution from Mphasis, with an objective to address these opportunities.
The overall goals of the laboratory are to,
Apply machine learning and design thinking to produce world-class papers and compelling proof-of-concepts of systems with the potential for large societal impact
Produce experiential pedagogy-based modules that are virtually offered and designed to be broadly accessible by all students of various disciplines. Each module is based on a sequence of hands-on activities that allow a student to reconstruct proof-of-concepts produced in the laboratory.
Conduct workshops that create opportunities for collaboration between academicians, practitioners, and policy makers