German chemist Friedrich Kekulé was having a reverie or daydream of a snake biting its own tail, and he started wondering if the six carbon atoms in the benzene molecule had a similar structure. this hallucinatory experience led to the discovery of the hexagonal ring structure with alternating single and double bonds, a groundbreaking concept in organic chemistry. Kekule` was not the only one – Dmitri Mendeleev reportedly had a vision of the periodic table and Edison claimed to mine his dreams for material. Writer Stephen King claimed to have dreamt up his novel “Misery” during a somnolent transatlantic flight, and the masterpieces of Van Gogh and Salvodar Dali were often inspired by the hallucinations inside their heads.

The word ‘hallucinate’ has entered the technology lexicon, after the launch of ChatGPT and the realisation that these Generative AI chatbots were inventing or ‘dreaming up’ a lot of false and weird stuff. ChatGPT’s alter ego Sydney famously expressed its undying love for a New York Times reporter. A US lawyer relied upon it to file a case against an airline, but the judge found that all the cases cited were dreamt up by ChatGPT. When I was writing a paper on Indian philosophy and privacy for a Cambridge University course, ChatGPT authoritatively gave me five research papers to cite – all of them were wrong! This hallucinatory ability of GenAI has lots of people worried, especially when dealing with enterprise use cases or applications in healthcare or education. In fact, the efficacy of an LLM is often measured by how much it does or does not hallucinate, with research companies introducing Hallucination Indexes. A recent Cornell research ( https://bit.ly/48gko5Y ) revealed that GPT 3.5 hallucinated 69% of the time, and Meta’s LlaMA 2 hit an astounding 88% number. While the later versions of the models have improved substantially, companies are worried that the nonsense that the models spew out could hurt their brand and stock price, anger customers, and pose a legal threat.

However, I believe that we need to think differently about this: what if hallucinations in LLMs are a feature, and not a bug? Technically, this is true; the probabilistic construct of these models promotes this behaviour, and it might be impossible for Generative AI to be totally accurate all the time. But, what if we could start leveraging this very human-like behaviour of creativity and, yes, hallucination, the same way Kekule and Dali did to create their masterpiece? Sundar Pichai of Google think so too. He suggested that that hallucinating could be a feature and a bug, and that a GenAI experience should be “imaginative, like a child who doesn’t know what the constraints are when they’re imagining something.” Marc Andreessen of A16Z remarked: “When we like the answer, we call it creativity; when we do not, we call it hallucination.”

Artists and creators have caught on to these. John Thornhill writes in the Financial Times (https://bit.ly/42E8FNm) about Martin Puchner, a Harvard professor, who “loves hallucinations.” Puchner talks about how humans mix cultures and inputs from previous generations to generate new stuff, and how civilizations advance that way. “Culture,” he says. “is a huge recycling project..” And this is precisely what GenAI is doing. It is borrowing, stealing, copying and mashing up different inputs from humans to create new stuff. Thus, says Thornhill, “Hallucinations may not be so much an algorithmic aberration as a reflection of human culture.” If we stop looking at GenAI as a prediction and forecasting tool, but a great one for enhancing our creative prowess by giving us innovative ideas and content, ‘hallucinations’ become a good thing. Modern artists and creators have started harnessing this power of hallucination. Visual Electric, a California based company backed by Sequioa, encourages hallucinations to create new visuals and ideas ( https://bit.ly/49A1wAd). Austin Carr writes in BusinessWeek about a film director, Paul Trillo, who used GenAI to create an acclaimed short film with psychedelic effect. Gaming developer, Inworld AI, leverages the creativity of GenAI to help video game developers build interactive computer characters.

We need to see GenAI for what it is, not confuse it with Machine or Deep Learning, which are also AI, and expect it to do high accuracy prediction and forecasting. Think of GenAI as a writer of fiction, not non-fiction, and it is the ‘creative’ aspect of GenAI which makes possible the art of idea generation, producing art, and helping us work better with Copilot. If we think like how Stephen King or Van Gogh did, it can become an immensely powerful creative tool for us. With use cases that require exact answers we need to be careful until these models improve, and until then, as John Thornhill concludes his FT article: “Caveat Prompter.”