IBM Stops Sales of Watson For Drug Discovery and Research
As the AI hype-cycle has grown stronger, there has been a tsunami wave of claims about what sorts of improvements and breakthroughs the technology could deliver. One of them — and potentially the most important — has been the idea that we can use AI to find new medicines and treatments for existing conditions where current options have not succeeded.
Ironically, that promise has now come up short. IBM has announced that it will stop selling its Watson AI system as a tool for drug discovery. It’s a high-profile defeat for the company, which has aggressively marketed AI as being useful for these purposes and which ran into issues in 2018 when reports indicated its systems had made improper, dangerous recommendations for cancer patients (fortunately, these recommendations were never acted upon).
While IBM blames sluggish sales as the reason for its withdrawal, there are clearly deeper problems. A recent analysis by IEEE Spectrum gives more context around these issues. After years of toil and a number of moonshot projects, IBM has surprisingly little to show for its efforts. And the company has created a certain amount of hostility towards itself, IEEE writes, because it took an aggressive, marketing-first approach to AI and Watson, throwing out grand promises that didn’t accurately suggest what the system was actually capable of.
Watson astonished the world with its performance on Jeopardy, where it used its ability to analyse the relationships between words rather than treating them like search terms. Theoretically, Watson could use its engine to sort and navigate medical data in a similar fashion. Unfortunately, reality has not cooperated. Out of the research conducted on using AI to improve patient outcomes, none of it has involved IBM Watson.
The IEEE notes that IBM faced humungous challenges in trying to bring its AI program online and use it effectively for human medicine. Nothing like Watson (or rather what Watson was intended to be) has ever existed before., and no one knew how to build it.