Evolutionary AI: A Better Decision-Making Strategy for Business | Cognizant
Often when we talk to businesses, what their expectation from AI is more insights. Whereas AI can help with the actual decision-making too. If you actually have the means to predict what the impact of a decision is, how do you come up the best strategy? Evolutionary computation starts evolving decision strategies on a surrogate model of the real world. This mental model that is created based on decisions that have been made historically. Model results can be adjusted with parameters, for example, if we are more inclined to saving money than make revenue. The cost saving strategy will still maximize revenue as much as possible -- telling you the best possible 2 or 3 outcomes that you are solving for. They can also develop better expertise. They can run “what-if” scenarios and this way learn what works. And that’s the direct result of maintaining multiple best decision strategies at any given point in time, defining multiple objectives. Instead of tradeoffs, the human now takes a role of designing what the goals are. One of them might have higher performance but also higher cost. Another one might be more economical but not perform quite so well. The experts can think at the level of goals, objectives, restrictions, costs and constraints. The implementation is done by AI. We evolve decision strategies based on that and then implement the decisions in the real world. And, we collect the outcomes and the decisions and we refine the models over time.
Learn more: https://cogniz.at/2Ioy869
Subscribe to this channel: https://cogniz.at/subscribeyt
Often when we talk to businesses, what their expectation from AI is more insights. Whereas AI can help with the actual decision-making too. If you actually have the means to predict what the impact of a decision is, how do you come up the best strategy? Evolutionary computation starts evolving decision strategies on a surrogate model of the real world. This mental model that is created based on decisions that have been made historically. Model results can be adjusted with parameters, for example, if we are more inclined to saving money than make revenue. The cost saving strategy will still maximize revenue as much as possible — telling you the best possible 2 or 3 outcomes that you are solving for. They can also develop better expertise. They can run “what-if” scenarios and this way learn what works. And that’s the direct result of maintaining multiple best decision strategies at any given point in time, defining multiple objectives. Instead of tradeoffs, the human now takes a role of designing what the goals are. One of them might have higher performance but also higher cost. Another one might be more economical but not perform quite so well. The experts can think at the level of goals, objectives, restrictions, costs and constraints. The implementation is done by AI. We evolve decision strategies based on that and then implement the decisions in the real world. And, we collect the outcomes and the decisions and we refine the models over time.
Learn more: https://cogniz.at/2Ioy869
Subscribe to this channel: https://cogniz.at/subscribeyt
