Engineers pre-train AI computers to make them even more powerful

September 22, 2020

In 2016, a supercomputer beat the world champion in Go, a complicated board game. How? By using reinforcement learning, a type of artificial intelligence whereby computers train themselves after being programmed with simple instructions. The computers learn from their mistakes and, step by step, become highly powerful.

The main drawback to reinforcement learning is that it can't be used in some real-life applications. That's because in the process of training themselves, computers initially try just about anything and everything before eventually stumbling on the right path. This initial trial-and-error phase can be problematic for certain applications, such as climate-control systems where abrupt swings in temperature wouldn't be tolerated.

Learning the driver's manual before starting the engine

The CSEM engineers have developed an approach that overcomes this problem. They showed that computers can first be trained on extremely simplified theoretical models before being set to learn on real-life systems. That means that when the computers start the machine-learning process on the real-life systems, they can draw on what they learned previously on the models. The computers can therefore get on the right path quickly without going through a period of extreme fluctuations. The engineers' research has just been published in IEEE Transactions on Neural Networks and Learning Systems.

"It's like learning the driver's manual before you start a car," says Pierre-Jean Alet, head of smart energy systems research at CSEM and a co-author of the study. "With this pre-training step, computers build up a knowledge base they can draw on so they aren't flying blind as they search for the right answer."

Slashing energy use by over 20%

The engineers tested their approach on a heating, ventilation and air conditioning (HVAC) system for a complex 100-room building using a three-step process. First, they trained a computer on a "virtual model" built from simple equations that roughly described the building's behavior. Then they fed actual building data (temperature, how long blinds were open, weather conditions, etc.) into the computer, to make the training more accurate. Finally, they let the computer run its reinforcement-learning algorithms to find the best way to manage the HVAC system. Broad applications

This discovery could open up new horizons for machine learning by expanding its use to applications where large fluctuations in operating parameters would have important financial or security costs.
Source: Baptiste Schubnel, Rafael E. Carrillo, Pierre-Jean Alet and Andreas Hutter, "A hybrid learning method for system identification and optimal control," IEEE Transactions on Neural Networks and Learning Systems.

Swiss Center for Electronics and Microtechnology - CSEM

Related Learning Articles from Brightsurf:

Learning the language of sugars
We're told not to eat too much sugar, but in reality, all of our cells are covered in sugar molecules called glycans.

When learning on your own is not enough
We make decisions based on not only our own learning experience, but also learning from others.

Learning more about particle collisions with machine learning
A team of Argonne scientists has devised a machine learning algorithm that calculates, with low computational time, how the ATLAS detector in the Large Hadron Collider would respond to the ten times more data expected with a planned upgrade in 2027.

Getting kids moving, and learning
Children are set to move more, improve their skills, and come up with their own creative tennis games with the launch of HomeCourtTennis, a new initiative to assist teachers and coaches with keeping kids active while at home.

How expectations influence learning
During learning, the brain is a prediction engine that continually makes theories about our environment and accurately registers whether an assumption is true or not.

Technology in higher education: learning with it instead of from it
Technology has shifted the way that professors teach students in higher education.

Learning is optimized when we fail 15% of the time
If you're always scoring 100%, you're probably not learning anything new.

School spending cuts triggered by great recession linked to sizable learning losses for learning losses for students in hardest hit areas
Substantial school spending cuts triggered by the Great Recession were associated with sizable losses in academic achievement for students living in counties most affected by the economic downturn, according to a new study published today in AERA Open, a peer-reviewed journal of the American Educational Research Association.

Lessons in learning
A new Harvard study shows that, though students felt like they learned more from traditional lectures, they actually learned more when taking part in active learning classrooms.

Learning to look
A team led by JGI scientists has overhauled the perception of inovirus diversity.

Read More: Learning News and Learning Current Events is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to