Summary of: “Productivity gains with Emerging Technologies” held on Tuesday, December 8, 2020 from 8 to 9 AM.
Enterprises are deploying AI-driven solutions at a rapid pace. A large number of startups are delivering innovative technologies to further simplify the development and implementation of new capabilities. During our recent CTO Roundtable, we heard from two such startups.
Ankur Goyal of Impira and Alex Ratner of Snorkel AI discussed how AI-based technologies are helping CTOs make their organizations more productive and efficient.
Impira is an AI powered automation platform that helps you leverage valuable data trapped in pdf documents, images, and other files. Fortune 500 companies use the platform to reduce operating costs by up to 90%, automating the arduous processes associated with application forms, order forms, invoices, insurance claims, and more. Impira is different from RPA firms, OCR applications, and simple data extraction tools, which all deploy pre-trained solutions that can be people intensive, expensive, and time consuming to implement.
Ankur Goyal is the Founder & CEO of Impira. Previously, Ankur built and scaled the engineering team at MemSQL. He received his B.S. in Computer Science at Carnegie Mellon's School of Computer Science and holds several patents in database and AI technology.
Snorkel AI is an early-stage company developing Snorkel Flow, a new data-first platform for enterprise machine learning aimed at making AI adoption fast, practical, and adaptable. Snorkel Flow's end-to-end approach solves one of the greatest blockers to enterprise adoption of AI today — the labeling and management of the "training data" that fuels modern AI — using a new programmatic approach developed at the Stanford AI Lab, and deployed at places like Google, Intel, Apple, Dept. of Justice, FDA, DARPA, and others.
Alex Ratner is the co-founder and CEO of Snorkel AI. He also serves as an affiliate professor at the University of Washington. Alex holds a Ph.D. in Computer Science from Stanford University, where he was supported by a Stanford Bio-X fellowship. His research focused on applying data management and statistical learning techniques to emerging machine learning workflows, such as creating and managing training data, and applying this to real-world problems in medicine, knowledge base construction, and more.