In our webinar last week, we had three experts — Risto Miikkulainen, Babak Hodjat, and Amir Banifatami — in the field of what is called Evolutionary AI who presented a vivid picture of innovative technology and its potential to rebuild Making a Decision in industries.
Miikkulainen, VP of AI Research Knowledgeable professor of computer science at the University of Texas at Austin and a pioneer in the field of evolutionary AI, explains that evolutionary AI takes cues from biology, much like Nervous system.However, unlike most machine learning Unlike techniques that rely on supervised learning with clearly defined goals, evolutionary AI thrives in scenarios with weak feedback. It can navigate complex problem areas without any predefined answers.
While traditional AI can be excellent at recognizing patterns – like identifying a dog in an image – Miikkulainen says that stopping at just predictions is akin to “leaving money on the table.” Instead, evolutionary AI goes one step further: it doesn’t just predict, it suggests actions. By simulating a population of potential solutions and allowing them to “evolve” toward better outcomes, evolutionary AI can uncover new strategies that human experts may even overlook.
As Hodjat, CTO of AI at Cognizant, says, evolutionary AI is more aligned with the way we make decisions. In the real world, decisions often involve balancing multiple, sometimes conflicting objectives. Evolutionary AI excels here, says Hodjat, providing a range of strategies that balance different outcomes, allowing decision-makers to choose the most appropriate approach.
“Look at a pandemic, you want to reduce the number of cases, but at the same time you don’t want to have a big economic impact on people’s livelihoods. Evolutionary AI is very good at coming up with strategies that give us a balance of multiple outcomes and then letting us choose which balance of outcomes is more interesting to us.”
Hodjat describes a two-step process: first, using AI to create “predictors” that forecast outcomes based on various decisions. Then, evolutionary algorithms search the vast space of possible strategies to find the optimal solution. He argues that this approach mirrors human decision-making processes, allowing for scenario testing and collaborative refinement between AI and human experts.
Banifatemi, technology and innovation strategist at AI Commons, a non-profit organisation that seeks to make the benefits of AI accessible to all, highlights the creative potential of evolutionary AI. He says that unlike traditional AI that derives solutions from existing knowledge, evolutionary algorithms can create entirely new solutions. This creativity, combined with the ability to optimise for multiple objectives simultaneously, makes evolutionary AI a powerful tool for tackling complex, multi-faceted problems.
Banifatemi also points to an important advantage: transparency. While many AI models operate as “black boxes,” solutions generated by evolutionary AI are often more explainable. This transparency is crucial in fields where understanding the reasoning behind a decision is as important as the decision itself.
Use cases in the real world
Miikkulainen describes an agricultural experiment in which the evolutionary AI found that basil thrived under 24-hour light – a paradoxical finding that challenged the assumptions of biologists. The experiment involved planting about 300 basil plants and providing them with different amounts of light, water, nutrients, temperatures, etc. “Most of those recipes were created by biologists – the usual suspects. But we also had some exploratory examples. And so, now we had 300 points of view on how plants grow under different conditions. And then we created a Predictive Models. We can then come up with weird recipes and ask the model what would happen in different scenarios. It so happened that we initially set a limit of six hours of darkness. But then we said, let’s open it up to try other solutions. And to the biologists’ great surprise, the model found that basilisks thrived with 24-hour light. This is a solution that would be hard for humans to find because they have all kinds of assumptions and evolutionary AI doesn’t have that,” says Miikkulainen.
Banifatmi says evolutionary AI is also looking very promising in drug research, especially for rare diseases where traditional approaches fall short. “For rare diseases, from a pharmaceutical perspective, many drug candidates need to be identified. And while the cost of drug research is huge, the market is not that big. With evolutionary AI you can find a group of potential molecules for a given constraint: like for toxicity, or for a particular genetic background. This speeds up drug research,” he says.
The story of evolutionary AI is still unfolding. As researchers and companies like Cognizant continue to invest in and develop this technology, more innovative applications will emerge – and some of these may come from Cognizant’s India offices. “Cognizant has a very strong presence in India and we have colleagues in India who are well versed in using evolutionary AI,” says Hodjat.
Miikkulainen, VP of AI Research Knowledgeable professor of computer science at the University of Texas at Austin and a pioneer in the field of evolutionary AI, explains that evolutionary AI takes cues from biology, much like Nervous system.However, unlike most machine learning Unlike techniques that rely on supervised learning with clearly defined goals, evolutionary AI thrives in scenarios with weak feedback. It can navigate complex problem areas without any predefined answers.
While traditional AI can be excellent at recognizing patterns – like identifying a dog in an image – Miikkulainen says that stopping at just predictions is akin to “leaving money on the table.” Instead, evolutionary AI goes one step further: it doesn’t just predict, it suggests actions. By simulating a population of potential solutions and allowing them to “evolve” toward better outcomes, evolutionary AI can uncover new strategies that human experts may even overlook.
As Hodjat, CTO of AI at Cognizant, says, evolutionary AI is more aligned with the way we make decisions. In the real world, decisions often involve balancing multiple, sometimes conflicting objectives. Evolutionary AI excels here, says Hodjat, providing a range of strategies that balance different outcomes, allowing decision-makers to choose the most appropriate approach.
“Look at a pandemic, you want to reduce the number of cases, but at the same time you don’t want to have a big economic impact on people’s livelihoods. Evolutionary AI is very good at coming up with strategies that give us a balance of multiple outcomes and then letting us choose which balance of outcomes is more interesting to us.”
Hodjat describes a two-step process: first, using AI to create “predictors” that forecast outcomes based on various decisions. Then, evolutionary algorithms search the vast space of possible strategies to find the optimal solution. He argues that this approach mirrors human decision-making processes, allowing for scenario testing and collaborative refinement between AI and human experts.
Banifatemi, technology and innovation strategist at AI Commons, a non-profit organisation that seeks to make the benefits of AI accessible to all, highlights the creative potential of evolutionary AI. He says that unlike traditional AI that derives solutions from existing knowledge, evolutionary algorithms can create entirely new solutions. This creativity, combined with the ability to optimise for multiple objectives simultaneously, makes evolutionary AI a powerful tool for tackling complex, multi-faceted problems.
Banifatemi also points to an important advantage: transparency. While many AI models operate as “black boxes,” solutions generated by evolutionary AI are often more explainable. This transparency is crucial in fields where understanding the reasoning behind a decision is as important as the decision itself.
Use cases in the real world
Miikkulainen describes an agricultural experiment in which the evolutionary AI found that basil thrived under 24-hour light – a paradoxical finding that challenged the assumptions of biologists. The experiment involved planting about 300 basil plants and providing them with different amounts of light, water, nutrients, temperatures, etc. “Most of those recipes were created by biologists – the usual suspects. But we also had some exploratory examples. And so, now we had 300 points of view on how plants grow under different conditions. And then we created a Predictive Models. We can then come up with weird recipes and ask the model what would happen in different scenarios. It so happened that we initially set a limit of six hours of darkness. But then we said, let’s open it up to try other solutions. And to the biologists’ great surprise, the model found that basilisks thrived with 24-hour light. This is a solution that would be hard for humans to find because they have all kinds of assumptions and evolutionary AI doesn’t have that,” says Miikkulainen.
Banifatmi says evolutionary AI is also looking very promising in drug research, especially for rare diseases where traditional approaches fall short. “For rare diseases, from a pharmaceutical perspective, many drug candidates need to be identified. And while the cost of drug research is huge, the market is not that big. With evolutionary AI you can find a group of potential molecules for a given constraint: like for toxicity, or for a particular genetic background. This speeds up drug research,” he says.
The story of evolutionary AI is still unfolding. As researchers and companies like Cognizant continue to invest in and develop this technology, more innovative applications will emerge – and some of these may come from Cognizant’s India offices. “Cognizant has a very strong presence in India and we have colleagues in India who are well versed in using evolutionary AI,” says Hodjat.
