Generative AI for Enterprise: How to Tame a Stochastic Parrot
During the last couple of years of the 20th century, Google truly rose to prominence. Compared to the search engines of those days, it seemed clear that “search,” as defined before Google, was just about to become completely disrupted. However, nobody at that time was foreseeing what was about to really happen: the end of the traditional ways of selling advertising.
With Generative AI rapidly popularized by ChatGPT, we might face a similar Google moment right now. Besides the most recent hype, there are many ongoing discussions about what future impact it will effectively have, especially in the enterprise context.
However, unlike a parrot, ChatGPT uses probabilistic methods to generate its responses. This means that instead of simply repeating what it has seen before, it generates new responses by predicting what is most likely to come next based on the input it receives. This is why ChatGPT is referred to as a "stochastic" model – it generates responses based on probability rather than determinism.
For example, while ChatGPT is extremely advanced and can generate responses that are very human-like, it is still limited by the data it was trained on. It can sometimes produce nonsensical responses, lack context, or disregard existing access rights within a company.
To create true value in the enterprise context, there would be a need for Generative AI capabilities to be integrated into a semantic enterprise search engine and trained on internal data.
On March 15th at 4:00 PM SGT, Squirro is organizing a 45-minute webinar: Generative AI for Enterprises: How to Tame the Stochastic Parrot.