Sifted: UMNAI Tackling Trust Issues in AI

In this article by Sifted, Tim Smith speaks to UMNAI’s CEO Ken Cassar to discuss the issue of failing trust in AI. Recognising the challenges posed by the opaque "black box" nature of current AI systems, Ken describes how UMNAI is at the forefront of tackling the trust and reliability issues prevalent in today's AI models, particularly in critical applications.

UMNAI’s novel "neuro-symbolic" AI architecture, invented by our Chief Scientist Angelo Dalli, ingeniously merges neural networks with rule-based logic, ensuring both performance and trustworthiness in AI applications. The Hybrid Intelligence Framework, used to create UMNAI’s AI models is at the forefront of tackling the trust and reliability issues prevalent in today's AI, particularly in critical applications. With Hybrid Intelligence you can develop solutions that enhance the transparency and decision-making clarity of AI.

Addressing the trust gap in AI, UMNAI's architecture breaks down complex tasks into smaller, manageable hierarchically related models, allowing for a deeper understanding of AI's decision-making process. Through its modular architecture, our system not only increases the accuracy of AI decisions but also allows users to trace and comprehend the logic behind them. This approach marks a significant stride toward resolving the opacity issues associated with AI, ensuring that AI's decisions are transparent and justifiable.

UMNAI is committed to redefining the AI landscape by setting new standards for transparency, reliability, and security in AI systems. Our innovative neuro-symbolic AI architecture and Hybrid Intelligence framework pave the way for a future where AI's vast potential can be harnessed confidently and responsibly. With a steadfast focus on bridging the trust gap, UMNAI is pioneering a transformative journey in the AI industry, ensuring that AI's integration into our lives and critical industries is both seamless and trustworthy.

Read the full article here.