A Brief History Of is our series digging into the backstory behind something in the news right now.
Before ChatGPT, there was IBM Watson.
The supercomputer became the pop-culture face of an earlier generation of AI when it beat out two of the most successful Jeopardy! contestants in 2011. Big Blue then hailed the know-it-all question-and-answer robot as a game-changer for all sorts of business applications, from healthcare to digital advertising.
But while the Watson brand remains a part of many of IBM’s B2B software and consulting offerings, the company’s record has been decidedly mixed when it comes to delivering on the promises made around its AI’s transformative potential.
This week, IBM announced a refresh for Watson that will place it in the midst of another AI hype wave: A new AI platform called WatsonX that aims to give business clients a toolkit to build their own generative AI models, including services for data storage, model training, and documentation.
The platform, which was announced at the IBM Think conference this week in Orlando, marks the culmination of three years of investment in the backbone systems behind technology like large language models (LLM), according to Rob Thomas, IBM’s chief commercial officer and SVP of software.
“We saw this as a unique time for foundational models to play a role in how businesses start to consume and leverage AI,” Thomas said in a press call on Monday. “Different from consumer use cases, but we think even more impactful, potentially, over time.”
As IBM seeks to embark on this new chapter, we decided to take a look back at how it got here.
The 1997 chess showdown: The playbook for Watson’s big game-show turn came from another man-versus-machine spectacle. In 1997, IBM’s Deep Blue supercomputer shocked the world by beating world chess champion Garry Kasparov in a six-game rematch after Kasparov beat the computer the year before. The victory is often considered a seminal moment in the development of AI.
This was the kind of cultural sensation that IBM had sought to recreate with Watson, which the company’s scientists originally called “DeepJ!” in reference to its chess-playing predecessor, the New York Times reported.
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Watson takes the stage in 2011: By the mid-2000s, IBM was on the hunt for a new way to pit its processing power against a human opponent as part of a corporate tradition it calls “Grand Challenges.”
The germ of the idea came during a fateful dinner IBM researchers had at a steakhouse in which patrons were glued to the TV to watch Jeopardy! contestant Ken Jennings’s winning streak, according to a 2013 TechRepublic feature. Seven years later, Jennings and fellow contestant Brad Rutter faced off with Watson in a three-episode televised event at the company’s research lab. Watson’s victory represented a breakthrough moment for natural language question-answering technology.
Watson makes it big: IBM sunk billions of dollars into turning Watson into a business unit at the start of 2014 and grew it to an estimated 10,000 employees by 2016, the New York Times reported. Commercials that had the chirpy machine conversing with stars like Serena Williams and Bob Dylan sought to make Watson synonymous with AI in the minds of the public.
Watson’s healthcare flop: Even as Watson was celebrating its big win, IBM was already plotting out the first industry its natural language AI would transform: healthcare. The company announced days after the Jeopardy! victory that Watson would power a virtual physician assistant in partnership with Columbia University and the University of Maryland.
From there, the company attempted to turn Watson Health into a far-reaching AI service that could aid in everything from cancer treatment to drug development. But that bet never panned out, and the division was eventually sold to a private-equity firm early last year.
Watson today: While many of Watson’s loftier ambitions never quite made it off the ground, IBM still works with a host of corporate clients on projects like building customer service chatbots, ad targeting, sports statistics, and other AI-based applications. The company is now betting that a new wave of much-hyped language technology could ramp up those efforts.