How autonomous systems use AI that learns from the world around it


Millions of engineers across industries such as automotive, aerospace, industrial machinery and medical devices have already built models of the systems they work on using MATLAB or Simulink. This new partnership allows users to bring simulation models built using MATLAB and Simulink to Microsoft’s Azure cloud computing platform, enabling unprecedented scalability and making it easier for developers and engineers building autonomous systems.

“Our core interest really comes down to engineering productivity — the ability to succeed at a task in the least amount of time possible,” said Loren Dean, MathWorks senior director of engineering for MATLAB products.  “This partnership allows engineers to stay in a familiar workflow to learn and apply AI without having to do the things that are non-traditional for them, like setting up the infrastructure to run a bunch of simulations at once. They’re shielded from all that.”

By running hundreds or thousands of simulations in parallel in Azure and learning from massive amounts of data at once, deep reinforcement learning algorithms can find optimal solutions to chaotic, real-world control problems that other types of AI still struggle to solve.

It turns out these problems are everywhere, said Gurdeep Pall, Microsoft’s corporate vice president for Business AI. Microsoft received three times more interest than it expected after opening its autonomous systems limited preview program in May.

The companies who have applied to work with Microsoft’s autonomous systems team and partners are looking to develop control systems to intelligently stitch fabric, optimize chemical engineering processes, manufacture durable consumer goods and even process food. The potential goes far beyond robotics or autonomous vehicles, Microsoft says.

“These are the kinds of diverse use cases for autonomous systems that we’re starting to see emerge,” Pall said.  “As customers learn about the capabilities of our toolchain, we’re seeing them apply it in really interesting ways because these control problems exist almost everywhere you look.”

Most customer use cases Microsoft has seen so far involve helping existing employees do their jobs more efficiently, safely or with higher quality, said Mark Hammond, Microsoft general manager for Business AI and the former CEO of the startup Bonsai, which Microsoft acquired last year. As sensors in modern workplaces collect ever more data, it can become difficult for any one operator — such as someone who is guiding a drill bit or calibrating expensive equipment — to track it all. AI tools can process that data and bring the most relevant patterns to that operator’s attention, enabling them to make more informed decisions.

“The journey from automated to autonomous systems is a spectrum of solutions, and very few of the engagements we’re seeing are in that fully autonomous with no humans in the loop zone,” Hammond said. “The vast majority are assistive technologies that work with people.”

Training AI systems in virtual worlds

Traditionally, AI models have often relied on labor-intensive labeled data for training, which works well for many problems but not for those that lack real-world data. Now, Microsoft and partners like MathWorks are expanding the use of AI into more areas such as those that require learning from the three-dimensional physical world around them — through the power of reinforcement learning and simulation.

Engineers have long used simulations to mathematically model the systems they work with in the real world. This allows them to estimate how a particular change in a chemical, manufacturing or industrial process may affect performance, without having to worry about slowing production or putting people or equipment at risk.

Now, those same simulations can be used to train reinforcement learning algorithms to find optimal solutions, Dean said.

“The AI is really augmenting how these traditional systems have worked — it just gives you greater confidence in your design and gives you additional capabilities that either had to be done manually before or were difficult to solve,” Dean said.

Imagine a building engineer whose job is to calibrate all the heating and cooling systems in a large commercial building to keep each room at a comfortable temperature as people stream in and out for meetings and outside weather fluctuates — while using as little energy as possible. That could involve tuning dozens of different parameters and might take many cycles of modeling and measuring changes for that engineer to find the best balance of controls.

With the new Microsoft and MathWorks partnership, that engineering expert could use machine teaching tools to help an AI system focus on the most important dimensions of the problem, set safety limits and figure out how to reward success as the algorithms learn. This allows for greater transparency and trust in how the AI system is making decisions and also helps it work more efficiently than randomly exploring all possibilities.

The engineer could train the AI using models that he or she already developed in MATLAB or Simulink. The simulations can be automatically scaled up in the Azure cloud — which means the engineer doesn’t have to worry about learning how to host and manage computing clusters.

The end result is the building engineer uses AI to zero in on promising solutions much faster — but still uses his or her judgment to decide what works best.

“This partnership really marries the best of MathWorks’ capabilities for modeling and simulation with the best of Microsoft’s capabilities for cloud computing and AI,” Microsoft’s Hammond said.





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