The AI for Business and Technology blog is always looking for ways to help our readers understand how their businesses can benefit from the latest in artificial intelligence and technology. Today, we are talking with Microsoft Senior Applied AI Engineer Kingsuk Maitra. Kingsuk has a PhD in electrical engineering and now leads customer success engagements for autonomous systems at Microsoft.

Blog: Let’s start by figuring out what it is we’re talking about. What exactly is an autonomous system?

Kingsuk: Well, the basic idea of automation is to do repetitive tasks without human involvement, using an established pattern that is somewhat predictable. An autonomous system, on the other hand, is way more than automation because the system is also making informed and intuitive decisions with a substantive amount of knowledge and know-how.

If you expose an autonomous system to uncharted territory, it can make a recommendation to inform decision making, whereas strict automation wouldn’t be able to do anything without explicit human intervention. This essentially frees up human resources and ingenuity for making much more informed decisions. And it also gives you a lot more leverage and liberty and latitude when it comes to ensuring quality and preventing human errors.

Blog: How does an autonomous system learn to make these recommendations?

Kingsuk: Well, artificial intelligence at a very basic level allows a machine to learn from existing experience and existing data. Traditional machine learning is predicated on the availability of large quantities of data. But in the real-life scenarios where autonomous systems are critical to day-to-day operations, such as industrial control systems the environment is often uncertain, and data is sparse. It’s noisy, unstructured and messy, and there’s no easy way to collect a lot of data and methodically label it. So what you can do is model the environment where an autonomous system is supposed to make an impact, and then let the autonomous system explore that simulated environment while being supervised by an operator.

That’s the Microsoft approach, which incorporates machine teaching and reinforcement learning. Years of expertise and experience from a seasoned human operator in a particular vertical can be incorporated into the knowledge base through machine teaching, and that is layered on top of the inputs and signals from the low-fidelity simulator. The autonomous system learns by testing out various actions and being rewarded as it takes the correct action, which is reinforcement learning.

Workers in hard hats observe equipment in a factory environment
Intelligent control systems can help machinery and processes adapt to dynamic environments in real time.

Blog: What kinds of industries could use autonomous systems?

Kingsuk: This type of solution is scalable across multiple verticals, be it manufacturing, industrial automation, energy and many others. These verticals all have their own specialized simulators, and each of them has hundreds of years of research and billions of dollars in development that has gone into the discipline to make them very mature disciplines. So Microsoft’s point of view is that we are not offering a black box solution that is going to go in and disrupt everything they have known for all that time. What we are saying is we use AI to augment the human learning that already exists in those spaces, offering this one solution that can scale. We are not replacing anything, just adding to it.

And not only is this a way to find new solutions to existing problems, but it also offers the opportunity to solve problems that were previously thought unsolvable.

Blog: What’s one example of autonomous systems being applied?

One great example of this is new product introduction, or NPI, which is a complex problem. Most of the time, a new product has a long wish list of properties it needs to have, and the way it often works is a kind of educated guesswork. There might be 50 to 200 parameters, and a person uses heuristics and trial and error, working each parameter sequentially, and it takes several months in a best-case scenario.

With machine teaching and autonomous systems, you can optimize all those parameters, work simultaneously and in parallel and the whole process takes just weeks.

Not only does this save time but it reduces waste, which is better for the environment, and it allows the product to get to market quicker, when it is actually in demand. The market can change so quickly that something that was needed months earlier may no longer be needed.

We at Microsoft are also using this technology internally for power and efficiency optimization of our buildings, which will not only save money but will help us move toward our sustainability and carbon neutrality goals.

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