When people speak of IBM Watson, they do so in hushed tones in the corner of rooms, whispering for fear it will jinx it and the promise will not be realized. ‘Did you hear? Watson will cure cancer.’ Watson is, in many ways, the Chuck Norris of cognitive computing.
Watson’s reputation stems, in no small part, from IBM itself, who are not modest when it comes to blowing their own horn. Dr. John Kelly, who heads up the Watson team at IBM, has claimed that ‘This is brand new technology. It’s going to change the world.’ Not to be outdone, Ann Rubin, IBM's VP of branded content and global creative, said that Watson enables users to ‘outthink cancer, outthink risk, outthink doubt, outthink competitors.’ Big promises. But is this hype warranted, or, as some believe, is the emperor running amok completely naked?
IBM established itself as a leader in the artificial intelligence field back in 1996 when its Deep Blue chess playing computer defeated Russian chess Grandmaster Garry Kasparov. Then Watson’s Jeopardy victory in 2011 cemented its reputation. Shortly after the Jeopardy victory in 2014, IBM broke down Watson’s capabilities into 40 different components each serving a particular business problem, which is the business model it still uses today.
IBM pitches Watson as a solution for a variety of industries, from healthcare to financial services. It crunches through vast amounts of data to understand problems and environments in the same way humans do, a process known as cognitive computing. It then uses these insights to provide predictions and make decisions. According to IBM, the business is growing. It is at the center of the company's ‘strategic imperatives’ - which include cloud, mobile and analytics - that contributed $32.8 billion in revenue in 2016. However, further details around the return on investment are scant, as it is lumped in with the other technologies in the company’s financial statements. In February last year, chief executive Ginni Rometty explained that it does not provide much information because Watson is new and growing, telling the annual IBM analyst meeting, ’We are building an era, a platform, an industry, and making a market with it. We have competitors who don’t disclose for a decade, [so] I’m going to protect it and nurture it — we will disclose eventually’.
Rometty’s position is understandable, but evidence of success may at least help to counter the growing body of criticism. Among these critics is Meg Whitman, CEO of IBM competitor Hewlett-Packard Enterprise. Last year she noted that ‘We're in a lot of customers where actually from a Watson perspective it's not as far along in terms of real-world applications as you might imagine from the advertising.’ This could easily be dismissed as a competitor trying to get one over on a rival, but her’s is not a lone voice. Many others are starting to argue that Watson is just a thinly veiled advertising campaign, exploiting the notoriety achieved from its Jeopardy! win to promote its consultancy services and the technologies it has put under the Watson umbrella that are not especially groundbreaking.
Also among the dissenters is Roger Schank Ph.D., founder of the Institute for the Learning Sciences at Northwestern University. He accuses Watson of false advertising, writing in an excoriating blog post that Watson is essentially just a ‘word counter’ little different from Google search. He says that it is incapable of any non-trivial reasoning, contrary to how IBM presents it in its advertising, noting specifically an ad campaign IBM ran that showed Watson’s analysis of Bob Dylan songs for key themes, which it identified as ‘love fades’ and ‘time passes’ - themes Schank argued no Dylan fan would ever put down as his most important. Although, Dylan says those themes sound ‘about right’ in the advert, so it could just be that Schank is misremembering.
IBM refutes accusations that it is not the high-end proposition it claims. Dr. Kelly says that ‘Everything we brand Watson analytics is very high-end AI,’ and you will find little argument that IBM’s Watson artificial intelligence system is an incredible piece of technology. It’s more than capable of searching across vast repositories of unstructured digital data and returning answers remarkably quickly and has done amazing things, particularly in healthcare and refining diagnoses. The problem is not whether it's a market leader, though, it’s the promises it is making. Watson has suffered from the goals it has set for itself, from curing cancer to being the solution to cybersecurity. These are important things to resolve, and IBM needs to be honest with how much Watson can really do. Overhyping AI is dangerous, as we saw when inflated expectations and subsequent disillusionment in the 1980s led to the so-called AI Winter, bringing investment to a grinding halt and pushing research ‘underground.’ Obviously, IBM wants to sell its product, and because it is such a nascent technology it needs big targets like cancer and cybersecurity to put itself on the map and gain customer support. However, in the long term, it could be shooting itself in the foot. The thaw has been hard earned, and although another setback of the same magnitude is unlikely for AI, expectations should still be managed.
Watson’s reputation stems, in no small part, from IBM itself, who are not modest when it comes to blowing their own horn. Dr. John Kelly, who heads up the Watson team at IBM, has claimed that ‘This is brand new technology. It’s going to change the world.’ Not to be outdone, Ann Rubin, IBM's VP of branded content and global creative, said that Watson enables users to ‘outthink cancer, outthink risk, outthink doubt, outthink competitors.’ Big promises. But is this hype warranted, or, as some believe, is the emperor running amok completely naked?
IBM established itself as a leader in the artificial intelligence field back in 1996 when its Deep Blue chess playing computer defeated Russian chess Grandmaster Garry Kasparov. Then Watson’s Jeopardy victory in 2011 cemented its reputation. Shortly after the Jeopardy victory in 2014, IBM broke down Watson’s capabilities into 40 different components each serving a particular business problem, which is the business model it still uses today.
IBM pitches Watson as a solution for a variety of industries, from healthcare to financial services. It crunches through vast amounts of data to understand problems and environments in the same way humans do, a process known as cognitive computing. It then uses these insights to provide predictions and make decisions. According to IBM, the business is growing. It is at the center of the company's ‘strategic imperatives’ - which include cloud, mobile and analytics - that contributed $32.8 billion in revenue in 2016. However, further details around the return on investment are scant, as it is lumped in with the other technologies in the company’s financial statements. In February last year, chief executive Ginni Rometty explained that it does not provide much information because Watson is new and growing, telling the annual IBM analyst meeting, ’We are building an era, a platform, an industry, and making a market with it. We have competitors who don’t disclose for a decade, [so] I’m going to protect it and nurture it — we will disclose eventually’.
Rometty’s position is understandable, but evidence of success may at least help to counter the growing body of criticism. Among these critics is Meg Whitman, CEO of IBM competitor Hewlett-Packard Enterprise. Last year she noted that ‘We're in a lot of customers where actually from a Watson perspective it's not as far along in terms of real-world applications as you might imagine from the advertising.’ This could easily be dismissed as a competitor trying to get one over on a rival, but her’s is not a lone voice. Many others are starting to argue that Watson is just a thinly veiled advertising campaign, exploiting the notoriety achieved from its Jeopardy! win to promote its consultancy services and the technologies it has put under the Watson umbrella that are not especially groundbreaking.
Also among the dissenters is Roger Schank Ph.D., founder of the Institute for the Learning Sciences at Northwestern University. He accuses Watson of false advertising, writing in an excoriating blog post that Watson is essentially just a ‘word counter’ little different from Google search. He says that it is incapable of any non-trivial reasoning, contrary to how IBM presents it in its advertising, noting specifically an ad campaign IBM ran that showed Watson’s analysis of Bob Dylan songs for key themes, which it identified as ‘love fades’ and ‘time passes’ - themes Schank argued no Dylan fan would ever put down as his most important. Although, Dylan says those themes sound ‘about right’ in the advert, so it could just be that Schank is misremembering.
IBM refutes accusations that it is not the high-end proposition it claims. Dr. Kelly says that ‘Everything we brand Watson analytics is very high-end AI,’ and you will find little argument that IBM’s Watson artificial intelligence system is an incredible piece of technology. It’s more than capable of searching across vast repositories of unstructured digital data and returning answers remarkably quickly and has done amazing things, particularly in healthcare and refining diagnoses. The problem is not whether it's a market leader, though, it’s the promises it is making. Watson has suffered from the goals it has set for itself, from curing cancer to being the solution to cybersecurity. These are important things to resolve, and IBM needs to be honest with how much Watson can really do. Overhyping AI is dangerous, as we saw when inflated expectations and subsequent disillusionment in the 1980s led to the so-called AI Winter, bringing investment to a grinding halt and pushing research ‘underground.’ Obviously, IBM wants to sell its product, and because it is such a nascent technology it needs big targets like cancer and cybersecurity to put itself on the map and gain customer support. However, in the long term, it could be shooting itself in the foot. The thaw has been hard earned, and although another setback of the same magnitude is unlikely for AI, expectations should still be managed.
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