Will AI replace the need for human annotators?
Innovation in AI is accelerating at a steep rate, and the tools and processes used to support such AI innovation are no exception. Data annotation is seeing advances, such as using AI and automation to pre-label data and conduct quality assurance, that seemingly promise to eliminate the need for human-powered labeling.
This panel explores:
- The current and future state of humans-in-the-loop data annotation
- The importance of human judgment and subject matter expertise in annotating nuanced training data sets
- How technology will help us scale human annotation workforces
Paul ChristiansonVP Client Success
Paul Christianson helps CloudFactory clients gain a competitive edge in how they capture data to create amazing user experiences. Prior to CloudFactory, Paul worked on large-scale client software implementations at IBM. Paul is a graduate of the University of North Carolina at Chapel Hill.
Damian RochmanVP Product
Damian Rochman leads product management and development for CloudFactory, creating products and services that help tech teams train and augment their AI models. Prior to CloudFactory, he was Kaltura's Director of Product Management for MediaSpace and Social Business (SBS) extensions.
Nir BuschiCo-founder & Chief Business Officer, Dataloop
Nir is the Co-founder and Chief Business Officer of Dataloop, the leading enterprise grade data platform for production AI systems. Nir oversees the business development, strategic partnerships and marketing teams, and works with customers and partners seeking to accelerate the development of AI.
Joe MorrisonBusiness Development Executive - Imagery Analysis, Azavea
Joe works at Azavea, a B-Corporation that builds geospatial web applications aimed at creating civic, social, and environmental impact. He focuses on helping to grow Azavea's imagery analysis offering, including automated analysis of satellite, aerial, and drone imagery.