Machine teaching is a subfield of artificial intelligence (AI) that pertains to the process of obtaining knowledge directly from people instead of merely extracting knowledge from data. The idea is to provide contextualized data to AI systems to bring outputs relevant to their users.
Machine teaching can be considered the reverse of machine learning (ML). In machine teaching, the system acts as a teacher who begins with a goal in mind rather than a desired result. The teacher develops an optimal training process that allows the learner to achieve that goal. In short, the teacher makes it easier for the learner to process and overcome problems. Ideally, machine teaching involves deconstructing the problem into smaller parts that are easier to solve for ML algorithms.
Machine teaching provides immense benefits in supervised learning scenarios where ML algorithms have little or no labeled training data to produce specific outcomes.
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What Is the Difference between Machine Teaching and ML?
To get a better grasp of machine teaching, it is crucial to understand ML first fully.
ML is also a subset of AI that allows applications to improve their accuracy over time by predicting outcomes based on historical data, even in the absence of explicit programming.
Machine teaching, on the other hand, is a discipline that focuses on developing an optimal training set to allow a learning algorithm to become more efficient. As a result, ML algorithms use the training sets to learn and improve their skills without programming.
If you think about it, ML is similar to teaching children how to play soccer by merely giving them a ball and a goal. You can tell them hundreds of different ways to kick the ball, hoping they would figure out that the ball and the goal are connected. Many consider the concept of ML as merely giving machines tons of data and expecting them to analyze it by finding associations.
It is true that most AI applications, such as image and voice recognition, apply this concept well. But AI experts believe that there is always room to improve the process.
Machine teaching aims to solve complex problems into smaller bits so ML systems can address them more effectively. In a sense, machine teaching teaches children to play soccer step by step. To score a goal, it tells them to put the ball in front of the goal, make sure to avoid the goalie, and kick the ball toward the goal to score.
What Are the Advantages of Machine Teaching?
The primary benefit of machine teaching is to make technology such as automation tools usable to subject experts with little or no computer science background. As such, computer scientists would have more time to focus on carrying out more productive and valuable tasks instead of spending time in developing training sets.
What Are the Current Applications of Machine Teaching?
While machine teaching is still in its infancy, it presents numerous potential applications. Among the most notable is the Bonsai system, which uses an AI model to automatically calibrate computer numerical control (CNC) machines typically used in manufacturing.
The Bonsai system accomplishes tasks 30 times faster than a human operator. Its autonomous calibration feature reduces downtime and increases productivity since it can be frequently recalibrated if necessary.
To date, the Bonsai system is testing the technology to improve healthcare processes, reduce carbon dioxide levels, improve heating, enhance ventilation and air-conditioning systems, ease transportation logistics, and make supply chains more efficient.
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Machine teaching takes advantage of human expertise to make AI systems more powerful by telling them what to focus on. It removes the time-consuming exploration process. You can watch this video by Microsoft, a vocal proponent of the technology, to get a basic idea of this emerging technology:
