Risk-Aware Selective Answering and Generative Models
Risk-Aware Selective Answering and Generative Models.
Context
Generative AI and language interface use cases present a significant opportunity, estimated to reach 42.6B in 2023 and 98.1B by 2026 at a 32.0% CAGR, with 60% of CIOs planning for widespread AI deployments across their organizations by 2025. AI core platforms, i.e, building blocks of AI and ML deployments, including developer tools needed to build and deploy models to production, is a segment with spending that has reached 32.4B in 2022 and will grow at a 32.7% CAGR to 75.7B in 2024. However, the demand for expertise in this field has caused a talent shortage, with 78% of organizations reporting increased difficulty hiring AI data scientists in 2022, and has made the purchase of AI software one of the leading strategies to address this deficiency. Moreover, as impressive as the capabilities of generative models have become, they are still trained as black-box systems and continue to be held back by seemingly unpredictable variations in output quality. This has resulted in a reluctance of users to fully adopt these models into their day-to-day lives and subsequently prevents them from being integrated into larger workflows with more strict performance requirements. In simple terms, deployed models need to understand when their outputs are unreliable in order to allow for proper quality assurance.
Our Solution
Capsa adds uncertainty estimates to supported models. Our selective question-answering research explores how those estimates can help choose which answers to return and which to defer. The linked studies describe the models, tasks and tradeoff between accuracy and the share of questions answered.
Additional Resources
Themis AI, “Systems and Methods For Uncertainty-Aware Generative Models” Internal Report 2023.
Themis AI, “Risk-Aware Image Generation” Internal Report 2023.