Most people wouldn’t think to teach five-year-olds how to hit a baseball by handing them a bat and ball, telling them to toss the objects into the air in a zillion different combinations and hoping they figure out how the two things connect. 

And yet, this is in some ways how we approach machine learning today — by showing machines a lot of data and expecting them to learn associations or find patterns on their own. 

For many of the most common applications of AI technologies today, such as simple text or image recognition, this works extremely well. 

But as the desire to use AI for more scenarios has grown, Microsoft scientists and product developers have pioneered a complementary approach called machine teaching. This relies on people’s expertise to break a problem into easier tasks and give machine learning models important clues about how to find a solution faster. It’s like teaching a child to hit a home run by first putting the ball on the tee, then tossing an underhand pitch and eventually moving on to fastballs. 

“This feels very natural and intuitive when we talk about this in human terms but when we switch to machine learning, everybody’s mindset, whether they realize it or not, is ‘let’s just throw fastballs at the system,’” said Mark Hammond, Microsoft general manager for Business AI. “Machine teaching is a set of tools that helps you stop doing that.” 

Machine teaching seeks to gain knowledge from people rather than extracting knowledge from data alone. A person who understands the task at hand — whether how to decide which department in a company should receive an incoming email or how to automatically position wind turbines to generate more energy — would first decompose that problem into smaller parts. Then they would provide a limited number of examples, or the equivalent of lesson plans, to help the machine learning algorithms solve it. 

Machine teaching can dramatically shortcut the time it takes an intelligent agent to find the solution. It’s also part of larger goal to enable a broader swath of people to use AI in more sophisticated ways. Machine teaching allows developers or subject matter experts with little AI expertise, such as lawyers, accountants, engineers, nurses or forklift operators, to impart important abstract concepts to an intelligent system, which then performs the machine learning mechanics in the background. 

Microsoft researchers  began exploring machine teaching principles nearly a decade ago, and those concepts are now working their way into products that help companies build everything from intelligent customer service bots to  autonomous systems. 

Making hard problems truly solvable 

More than a decade ago, Hammond was working as a systems programmer in a Yale neuroscience lab and noticed how scientists used a step-by-step approach to train animals to perform tasks for their studies. He had a similar epiphany about borrowing those lessons to teach machines. 

That ultimately led him to found  Bonsai, which was  acquired by Microsoft  last year. It combines machine teaching with deep reinforcement learning and simulation to help companies develop “brains” that run autonomous systems in applications ranging from robotics and manufacturing to energy and building management. 

Adding a machine teaching layer — or infusing an organization’s unique subject matter expertise directly into a deep reinforcement learning model — can dramatically reduce the time it takes to find solutions to these deeply complex real-world problems, Hammond said. 

Armed with that information from its machine teaching component, the Bonsai system would select the best reinforcement learning model and create an AI “brain” to reduce expensive downtime by autonomously calibrating the equipment. It would test different actions in a simulated environment and be rewarded or penalized depending on how quickly and precisely it performs the calibration. 

“The reason machine teaching proves critical is because if you just use reinforcement learning naively and don’t give it any information on how to solve the problem, it’s going to explore randomly and will maybe hopefully — but frequently not ever — hit on a solution that works,” Hammond said. “It makes problems truly solvable whereas without machine teaching they aren’t.”