What is machine teaching?
Machine teaching is the practice of infusing context -- and often business consequences -- into the selection of training data used in machine learning (ML) so that the most relevant outputs are produced by the ML algorithms.
Proponents of machine teaching -- most vocally Microsoft -- hope to make the practice easily replicable, so that those without a background in computer science or software engineering can use machine teaching in new contexts. A Machine Teaching Group formed within Microsoft espouses the notion that: "By separating the teaching information from the algorithm, we can allow the algorithms and the teaching language to innovate independently and the teacher doesn't need to understand machine learning algorithms."
The idea is to enable business users to take machine teaching tools and apply them to problems specific to their industry sectors. Lawyers, nurses, city planners and other subject matter experts can impart abstract concepts to intelligent systems that perform ML.
The implication is that machine teaching requires a blend of human intelligence and AI. By including human teachers in the training process, generating results through ML becomes faster and less expensive than using data alone.
To understand machine teaching, it's useful to understand the concept of ML. As a subset of AI, ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. ML algorithms use historical data as input to predict new output values.
Machine teaching vs. ML
The machine teaching discipline is centered on creating the optimal training set that can guide a learning algorithm with the most efficiency. The goal of ML in the context of machine teaching is to use those training data sets to allow autonomous systems to learn and improve their skills without being explicitly programmed to do so.
Although the two terms sound similar and complement each other, ML has a different definition. It's a term first popularized in 1959 by Arthur Lee Samuel that entails the use of computer algorithms and statistical models to perform specific tasks without explicit instructions. Although it is still considered by many to be an emerging science, ML systems are widely used in settings like email filtering and internet search page recommendations.
The ML process
ML algorithms build mathematical models that look for patterns in data to make decisions without further human intervention. These mathematical models are based on sample data, generally known as training data. The more data, the better the decisions.
The process of learning begins with observations of data, such as examples, direct experience or instructions, in order to find these patterns and make better decisions in the future. ML models are generally divided into two types: supervised and unsupervised.
Supervised learning algorithms apply past experiences to new data using examples that are labeled (known simply as labeled data), to predict future outcomes. The learning algorithm can compare its output with the intended output, find errors and make the needed adjustments. The teaching process continues until the machine can reliably make predictions with an acceptable degree of accuracy.
Unsupervised algorithms use training data that is not labeled. These algorithms look to find structure in the data. This is a particularly useful capability in research and science when looking for hidden patterns.
A third type of learning algorithm, known as reinforcement learning, uses a reward feedback signal to teach machines and their software agents to choose the ideal behavior.
Benefits of machine teaching
The chief benefit of machine teaching is that it puts automation tools into the hands of subject experts with no computer science background. The goal is to make machine teaching tools as easy to use as word processing software or computer spreadsheets, wherein writers and accountants don't need to know computer programming to use them. If that goal is achieved, it would free up computer scientists to tackle more creative tasks, rather than the monotonous tasks of creating training sets.
Applications of machine teaching
Machine teaching is being tested in a variety of applications, notably in industry settings. In 2017, Siemens' subject matter experts, using Bonsai's platform (since acquired by Microsoft), trained an AI model to autocalibrate a computer numerical control (CNC) machine more than 30 times faster than an expert human operator. CNC machines need to be recalibrated frequently, as even minor friction leads to errors that result in costly manufacturing imperfections.
Other applications being tested include keeping carbon dioxide levels safe in buildings with large, automated heating, ventilation and air conditioning (HVAC) systems; supply chain management; healthcare operations; and transportation logistics.
Learn more in a demo of Microsoft's machine teaching tool: