Superintelligent works across stakeholders and systems throughout the AI development lifecycle by providing a platform to define, audit, and monitor AI solutions at scale, continuously.
An industry-first open AI disclosures registry providing an organized, transparent AI informational repository.
A brand new platform to analyze models and data used in models to deliver human understandable AI outcomes.
AI performance monitoring and auditing platform to empower building high performance, trustable, and compliant AI solutions.
Current AI systems are opaque, non-intuitive and difficult to understand. As a user/developer/business owner it’s hard to answer even simple questions such as; Why did you do that? When do you succeed or fail? When do I trust you?
Superintelligent platform empowers AI systems development by providing specification, robustness, and assurance throughout the AI development life-cycle.
The history of robotics and artificial intelligence in many ways is also the history of humanity’s attempts to control such technologies. Numerous recent advancements in all aspects of research, development, and deployment of intelligent systems are well publicized but transparency, safety and security issues related to AI are rarely addressed. Our goal with what we are building is to enable businesses of all sizes to unlock this AI black box and deliver trustworthy AI solutions to their customers.
We created Superintelligent because we believe there is a need to provide a human friendly AI glass box platform that unravels and empowers organizations to build transparent, auditable, trustworthy and understandable AI solutions
Dr. Roman Yampolskiy
Shifting the field toward building trusted systems instead of prioritizing accuracy above all else will be a central pillar to the continued adoption of AI.
-Dario Gil, IBM Research Director
I don’t believe it’s possible for AI to scale in the enterprise beyond hundreds of (experiments) unless you have that explainability.
-Vinodh Swaminathan, Principal KPMG
If we want to help humans improve AI, we need to understand it. Where are those errors happening? To which part of the data segments? With which part of the model?
-Dr. Harry Shum, EVP AI Research, Microsoft