In a world dominated increasingly by AI, access to relevant data becomes paramount — but what if such streams of information dry up?
Regulators at state, national, and international levels continue to watch how businesses capture and use data that could be used to train AI. If restrictions emerge that cut off access to data that AI needs, would the technology stall out despite its promises of innovation?
Alternatives such as synthetic data exist, but are they sufficient to properly train AI and deliver results that actually matter to operations?
This episode features Shobha Phansalkar, vice president of client solutions and innovation for Wolters Kluwer; Olga Megorskaya, founder and CEO of Toloka; Pete DeJoy, co-founder and senior vice president of product for Astronomer; Melissa Bischoping, senior director of security and product design research at Tanium; and Omar Khawaja, Field CISO, Databricks.
They discussed types of data that is necessary and relevant for training AI, how organizations might determine if data is useful or simply junk, what happens if policy stonewalls data access, and whether or not AI simply dies without data.
