AIs that can understand other AIs

The ability to translate complex, technical concepts into accessible, non-technical language for a range of audiences is an important human skill. Imagine two experts from vastly different fields—say, a technical designer and a marketing professional—coming together to co-create a product presentation for a non-expert audience. Their challenge is not just to share knowledge. To be an effective team they must develop a shared language that connects their disparate domains.

This collaborative process of translation and adaptation mirrors a new approach put forward by Google DeepMind. By augmenting large language models with each other, Composition to Augment Language Models (CALM) effectively enables these AI 'experts' to develop a common language, enhancing their ability to tackle complex tasks through collaborative intelligence.

In a business setting, this could revolutionize how companies interact with data. For instance, a retail giant could use CALM to blend a model specialized in analyzing customer behavior patterns with another that excels in generating engaging product descriptions. This would create an AI system capable of not only identifying emerging market trends but also creating compelling marketing content tailored to those trends, all in real-time.

This fusion of technical analysis and creative communication could transform data-driven strategies because the models could seamlessly match traditional predictive AI systems with generative systems to create narratives in real-time that are able to be configured for different audiences, who use different terminologies and have different mental models underpinning their decision making.