From Accuracy To Explainability: Steering AI And Large Language Models Into The Next Phase

In the field of AI, while accuracy in human-like responses was once the primary goal for LLMs, the challenge has now shifted to explainability. This shift is due to regulatory considerations and the need for transparency in AI decision-making processes. Unlike rule-based systems, LLMs and generative AI can produce different outputs for the same input, making it hard to explain their reasoning.

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Looking to the future, venture capitalists and startup founders are keenly observing the AI landscape, recognizing that the most valuable AI applications will be those that can provide explainability. In this rapidly changing environment, staying ahead of regulatory standards is imperative for success. The new benchmark for LLMs will be their ability to be explained, not just their accuracy.

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Read Judah's column in Forbes

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