IBM CEO says generalized AI ‘still a long time away,’ wants focus on near-term value

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Eleven years after IBM Watts rocked the tech world by beating two of Jeopardy’s biggest champions, the days of tackling the “moonshot” AI goal outside the research lab are over, IBM Chairman and CEO Arvind Krishna told IBM Thinkers yesterday. In Boston.

“I don’t want us to just work on‘ moonshots ’,” he said. “That should be the research you do in the lab.” Instead, he said IBM’s focus will be on AI projects that provide near-term value for clients, adding that they believe generalized artificial intelligence is “still a long way off.”

“Some people think it could be early 2030, but most people put it in the range of 2050 to 2075,” he said. “Personally, after growing up as a scientist, I react to anything 25 years or more away and with the conclusion that we have no idea how this will happen.”

Focus on enterprise AI

That doesn’t stop the company from moving forward with specific AI projects, which, while not explicitly “moonshots,” are examples of IBM’s focus on enterprise-applied AI.

One example is a partnership with McDonald’s that reduces costs and increases efficiency by automating customer orders in view of the huge number of potential variants. “It’s not a common question-and-answer, you have to figure out how customers can change the order – we were trying it out as customers pretending this morning. You order a quarter pounder, then you peel the onion; You can add. You order a drink, it asks you medium or large or small, “he said.

Krishna gave another example, which he called Watson AIops, of how IBM is implementing AI in information technology to increase productivity by becoming predictable rather than reactive. With the current demographic of labor and skills shortages, these examples become more consistent, he added.

‘Moonshot’ normalizes AI targets on the back burner

But while IBM is seeing success in cases of narrow, quick-win AI usage, the company’s larger, headline-making AI targets will no longer be on the front burner.

For example, Krishna also mentioned the demise of Watson Health – a business that signaled the future of healthcare, but whose data and analytics assets were sold in January – while still believing in the company’s previous Healthcare AI goals. Ultimately, “these problems are so difficult and given the effects of life and death, it can take half a decade or more for them to become fruitful.”

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