In the world of artificial intelligence, IBM Watson claims to have a higher enterprise business IQ than potential rivals like Amazon Alexa. But how can IBM make such a bold A.I. claim?
The answer has to do with artificial intelligence for business vs. consumer use cases, according to David Kenny, senior VP for IBM Watson and Cloud Platform. Among the three big differentiators Kenny points to:
1. Smaller Data Sets: Business-centric A.I. needs to learn more from less data. While Alexa learns from millions or billions of consumer interactions, Watson learns from smaller, business-centric data points, he asserts. For instance, learning about a building blueprint in the engineering sector is far different than learning from consumer searches about the weather, Kenny says.
2. Workflow Integration: When you adopt A.I. in enterprise at scale, that means it has to be in the workflow. That’s why IBM is working with Box, WorkDay and Salesforce, among other enterprise-class application providers, Kenny adds.
3. In-house Control & Learning: In most cases, enterprises have decades of IP. With that reality in mind, IBM doesn’t push ‘one’ Watson running across thousands of businesses. Instead, each business can have its own instance of Watson to keep the intellectual property in-house, maintain competitive advantage and avoid commoditization.
Interesting points. How would the Amazon Alexa team respond? We’ll be sure to pose IBM’s points to them during our next Amazon Web Services meeting.
PS: You should be able to ask Apple Siri a question, and have Watson provide the answers, Kenny says. IBM’s API work with Apple should allow developers to allegedly do that, Kenny seemed to indicate. Think of Siri as a browser, and Watson as the application, he adds.