Krista: Thanks Justin for joining us today. Today we're talking about the intersection of decision management and AI. So these are both big topics in the market. Where do you see the intersection of AI? What's your take?
Justin: So, if you think about traditional decision management platforms, such as IBM's ODM, they provide a deterministic style decision. So, I think they have a fixed algorithm. So, you have data that comes in, you evaluate that data, and then you get a definitive answer out the back. With machine learning an AI, it's much more around probabilities, and confidence levels that given the data you've actually analyzed, we think it might be this, it might be that. But that doesn't give you a definitive answer or definitive outcome. So you can use ODM, for example, to look at those outcomes and provide a certainty of a decision. So one example could be a sentiment analysis. So, if you think of somebody goes onto Twitter, and they enter a Tweet, and we might want to analyze their emotions, so we can pass that through a sentiment analysis machine learning model. And that model will analyze their text, analyze their emotions, and return a result which contains probabilities of whether they're happy, sad, disgusted, angry, all the typical chronic human emotions. We can then take that abstract response and pass it to IBM's operational decision manager, which can then analyze the scores, and then provide a definitive, actionable outcome.
krista: So, essentially what you're saying is that AI is able to take non-structured data and make it a more structured outcome. And then the decision management component takes that structured data and actually makes a decision, it makes an actionable decision on it.
Justin: Yes. Exactly that.
Krista: All right. Well thanks, Justin for joining us today. And we can't wait to have you again soon.
Justin: Okay. Thank you.
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