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A few weeks ago, Red Hat chief executive Matt Hicks, peppered by his family to pick up various things while on a grocery run, asked one of the large AI public models to organize the grocery list by the aisle configuration in the nearest Star Market.
The AI promptly did so â with just one wobble.
"My bet would be that (mistake) was because of how we named the product, not because of its understanding," Mr Hicks tells me. "These things that take 30 minutes out of a task are really quite incredible to me. I was a little stunned."
The second instance involved his own Raleigh, North Carolina-headquartered company that IBM acquired a few years ago for US$34 billion (S$45 billion), at the time the biggest software acquisition to ever happen.
There was a ranked list of responses to customer questions, and the AI model had to guess the right answer from the stack. When a customer query came in and Red Hat engineers pondered which of the top four responses suited the question best, the AI model picked the one ranked 74th and moved it to the top of the queue â accurately, as it turned out because it solved the customer's issue.
"While these changes may seem small, when you add them up across an entire population I really believe the efficiency boost can positively impact (a nation's) gross domestic product," says Mr Hicks. "I am like, wow, these can be incredibly valuable for people and concretely valuable for businesses."
The question is, when do companies move these things out of research centers and make them billable? And when do we start seeing these magical capabilities popping up in the technology mines of Asian companies?
The answer, loaded in tech speak, comes as a revelation.
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