Join former U.S. chief data scientist DJ Patil and iMerit CEO Radha Basu as they discuss what’s needed from data science for artificial intelligence to advance and potentially achieve human-like intelligence in the future. These industry veterans will explore the complex relationship between technology and humans; what’s needed for humans and AI to work together to bring new opportunities to market that will truly have a societal impact.
Here are 3 key takeaways from the session:
- A feedback loop of results will continuously force enterprises to adapt their ML data operations to meet the demands of their models. Algorithms in the field will come back with edge cases, which data operations will scramble to resolve before the algorithm is redeployed.
- Factors that help increase annotation efficiency include, expertise on nuances and edge cases, work experience, motivation for the job, cross-trained annotators, confidence to challenge algorithms and provide insights, diversity of opinions and backgrounds, and accountability and transparency.
- The combination of technology and human-in-the-loop expertise gives enterprises a true end-to-end solution as they move to deploy their models in the field. By bridging together the right expertise, judgment, and technology, the highest quality data possible will be generated.