Embedding AI across an enterprise to tap its full business value requires shifting from bespoke builds to an industrialized AI factory.
For AI to make a sizable contribution to a company’s bottom line, organizations must scale the technology across the organization, infusing it in core business processes, workflows, and customer journeys to optimize decision making and operations daily.
Achieving such scale requires a highly efficient AI production line, where every AI team quickly churns out dozens of race-ready, risk-compliant, reliable models.
Labelled data is expensive, which makes benefiting from the current success in supervised learning unfeasible for smaller companies.
However, good representations can be learned without any task-specific information from raw data.
In self-supervised learning, labels are generated artificially. A common approach is to take multiple augmented views from the same source and contrast them to different sources.
Many papers have proven that simply increasing the similarity (decreasing the distance in the embedding space) of such views from the same source can lead to strong representations.
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