This quote (see copy-pasted below) articulates why using machine learning is more robust... because "if you torture the data, for long enough, you can get it to confess to almost anything." The speaker justifies increased use of cognitive computing by stating that machines are better at the "discovery" portion because they can find the stories "that [are] trying to get out" of the data. So using machines for analysis are better than humans because they lack "any preconceptions or prejudices. That's discovery." 6 years later, the conversation in AI and machine learning has significantly shifted to acknowledge the inbuilt bias and prejudices that can be hard-coded into the technologies and softwares themselves (by the human beings that build these machines!).
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Quote from Transcript:
"And finally, what about discovery? So I'd like to take you back to your days, we've all had courses in statistics. And I, I don't know if you had the same experience, but I had pretty cranky instructors in statistics. And they told me that we should always form a hypothesis and then test the hypothesis. Don't jump the gun, don't start looking at the data before you've formed a hypothesis. That was nice. But then [inaudible name] came along. Professor [inaudible name] came along and said, well wait a minute. You know, if you torture the data, for long enough, you can get it to confess to almost anything. What about the story in that data that's trying to get out so what about exploring the data and looking at it before you have any preconceptions or prejudices. That's discovery. And in fact, cognitive computing is helping us to find out stories that are trying to escape from that data are harmonious. And in fact, we're finding in our research in cognitive computing, that amazing things are beginning to reveal themself to us by taking a very new and cognitive approach to information."
