Often when we talk to businesses, what their expectation is from AI is more
insights. Whereas AI can actually help with the
actual decision making. Why do you need the insights in the first place? You need
the insights in order to make decisions, Everybody now has data and people have
gotten into the habit of making predictions but very few cases they are
optimising the decision making, the actions based on those predictions. Then
comes the difficult part: if you actually have a means to be able to predict what
the impact of a decision is how do you come up with the best strategy? That's
where Evolutionary Computation comes in. Evolutionary AI, technically is a machine
learning method where we are trying to discover something new. Evolutionary
computation starts evolving decision strategies not against the real world
that's very costly but against this model, this mental model that is created
based on decisions that have been made historically. One of the approaches we
have developed at Cognizant called ESP (Evolutionary Surrogate-Assisted
Prescription). We built a model of the world, the surrogate model. Once we have
that model then we can evaluate candidates and we can do a search.
Because we're using evolutionary computation we have multiple best
decision strategies. You don't have to take the answer of the model and
implement it as it. You can actually adjust some of the parameters and see
how it would turn out. If we're more inclined to saving money versus making
revenue, it will try to do the cost saving strategy while still maximizing
revenue as much as possible and when you set that the system will tell you what
is the best possible on the other two or three outcomes that you're solving for.
They can also develop better expertise. We 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.
You can define multiple objectives and you get as a result not just one solution that's
ultimate best but the set of trade-offs. The human now takes the 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 become more empowered. They can think at the level of goals and
objectives and restrictions cost constraints. They don't have to think in
terms of implementation. The implementation is done by AI.
The trick here is to try to keep up with a changing world and changing priorities.
As humans when we make decisions our mental model of the world does keep
changing and it's based on our experience; it's not a static one-time
affair and the Evolutionary AI system is very similar. We build a model of the
world that's the best given the data we have right now. We evolve decision
strategies based on that but then as we implement the decisions in the real
world we collect the outcomes and the decisions and we refine the models. And
if you capture that data you may have thousands of examples, hundreds of
thousands some cases millions of examples and then we have the data that
it takes to build the surrogate model. We can use this loop to find where we need
more data then we can go back and we can look at what was learned. The discovery
process is very powerful and it can discover things that humans don't know
already and we can develop new knowledge based on that.