Deep learning has transformed AI and how it is applied in the enterprise. However, most deep learning algorithms still lack the ability to understand data and situations that are different from those encountered during training. This becomes a critical issue when companies don’t have vast amounts of training data—or when the real world no longer perfectly reflects the training data. The solution? Transfer learning, the ability for an AI system to take what it has learned in one situation and apply it to new and different situations and domains.
Transfer learning makes powerful systems more reusable and reduces the amount of training data, compute, and professional services needed. But is it ready for business deployment, or is it still emerging technology? How can it used in business today? Join Catherine Havasi to find out, as she explains how transfer learning can be applied to enterprise AI solutions across multiple industries.
Catherine Havasi is cofounder and CEO of Luminoso, an AI-based natural language understanding company located in Cambridge, MA. Luminoso’s solutions are based upon nearly a decade of Catherine’s research at the MIT Media Lab on applying natural language processing and machine learning to improve text analytics. As a woman in machine learning (especially embeddings/deep learning), as well as a women tech CEO, she offers a unique (and rare) perspective on the field. Catherine directs the Open Mind Common Sense Project, one of the largest common sense knowledge bases in the world, which she cofounded alongside Marvin Minsky and Push Singh in 1999.
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