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There’s been a lot of speculation and hand-wringing about what impact ChatGPT and other generative AI tools will have on employment in the tech industry, especially for developers. But what is its potential for organizations and businesses? What new opportunities lie ahead?
In this episode of The New Stack Makers podcast, Nima Negahban, CEO of Kinetica, spoke to Heather Joslyn, features editor of The New Stack, about what could come next for companies, especially when generative AI is paired with data analytics.
The conversation was sponsored by Kinetica, an analytic database.
There’s an obvious use case, Negahban told us, one that will result in a transformative “killer app”: “An Alexa for all your data in your ecosystem in real time. Where you can ask, ‘What store is performing best today?’ Or, “What products underperform when it’s raining?’ Things like that, that’s within the purview, in a very short order of what we can do today.”
The result, he said, could be “a whole new level of visibility into how your enterprise is running.”
Two big challenges loom in the generative AI space, Negahban said. One, security, especially when using internal data to help train an AI model: “Is it OK to send the necessary information that you need to a large language model?”
And two, accuracy — making sure that the AI outputs aren’t riddled with hallucinations. “If my CEO is asking a question, and [generates that analytic on the fly and gives them an answer, how do we make sure that it’s right? How do we make sure that the information that we’re going to give that person is correct, and it’s not going to put them down a false path?”
For developers, generative AI — including tools like GitHub Copilot — will bring a new expectation of efficiency and innovation, Negahban said.
For both devs and product managers, he said, it can spur creativity; for instance, he said it can enable them “to make new features that previously you wouldn’t have been able to think of?”
Much remains to be discovered about using generative AI in the enterprise. For starters, the current models are basically “text completion engines,” Negahban noted. “How do you orchestrate that in a way that can actually accomplish something? That’s a multistep process”.
In addition, organizations are just starting to grapple with how to leverage the new technology with their data. “That is part of the reason why the vector search, vector database and vector search capability world is exploding right now,” he said. “Because people want to generate embeddings, and then do embedding search.”
Kinetica’s processing engine handles ad hoc queries in a performant way, without users needing to do a lot of pre-planning, indexing or data engineering. “We’re coupling that engine with the ability to generate SQL on the fly against natural language,” he said, with Open AI technology trained on Kinetica’s own Large Language Model.
The idea, Negahban said, is “if you can take that killer app and marry it with an engine that can do the querying, in a way that’s performing in a way that doesn’t require whole teams of people to prepare the data, that can be exceptionally powerful for an enterprise.”
Check out the entire episode to get more insight.
