Generative AI: A Jigsaw Piece, Not The Complete Puzzle

The AI summer continues to be in full swing, with generative AI technologies from OpenAI, Anthropic and Google capturing the imagination of the masses and monopolizing attention. The hype has sparked discussions about their potential to transform industries, automate jobs and revolutionize the way we interact with technology.

Beyond personal efficiency, anyone who is starting with the question of how they should deploy more generative AI is starting from the wrong premise; LLMs are but one piece in a much bigger and more interesting puzzle.Sha’Carri Richardson, Cole Hocker, And Anna Hall Dominate U.S. Track & Field Olympic TrialsWhile LLMs have made significant strides in natural language understanding and generation, they’re still fundamentally word prediction machines trained on historical data.

Fast-forward to today, and we find ourselves reflecting on what was a huge swing from symbolic AI to two decades of AI being completely data-centric—an era where machine learning became dominant. The success of deep learning can be attributed to the availability of vast amounts of data and computing power, but this led to models trained on this data being black boxes, lacking transparency and explainability.

The answer to the latter lies in catalyst technologies that combine the strengths of LLMs, predominantly knowledge graphs and symbolic reasoning. One architecture, known as retrieval augmented reasoning , enables a neurosymbolic approach to rapidly create systems that deliver decisions in specific domains that are logical, grounded and trustworthy.

However, without additional symbolic methods, LLMs—even with techniques like retrieval augmented generation —will still hallucinate and produce outputs that don’t take into account the context of individual situations, reason logically or produce evidence.

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