Adaptive Learning

The fourth generation of machine intelligence, adaptive learning, creates the first truly integrated human and machine learning environment. For text analytics, this has given us the most accurate analytics to date, allowing us to get actionable information in many areas for the first time. In the examples we will share here, we show that adaptive learning is 95% accurate in predicting people’s intention to purchase a car. Adaptive learning correlates with actual sales, unlike any previous approach to Machine Intelligence.

Adaptive learning combines the previous generations of rule-based, simple machine learning, and deep learning approaches to machine intelligence. Human analysts are optimally engaged in making the machine intelligence smarter, faster, and easier to interpret, building on a network of the previous generations of machine intelligence.

The first generation of machine intelligence meant that people manually created rules. For example, in text analytics someone might create a rule that the word “Ford” followed by “Focus” meant that “Ford” referred to a car, and they would create a separate rule that “Ford” preceded by “Harrison” meant that “Ford” referred to a person.

The rule-based approach is very time consuming and not very accurate. Even after an analyst has exhausted all the words and phrases they can think of, there are always other contexts and new innovations that aren’t captured. For one of our clients, their experts analysts were only able to capture 11% of the documents they wanted to analyze using rules: this clearly is too limited.

The dominant form of machine intelligence today is simple machine learning. Simple machine learning uses statistical methods to make decisions about data processing. For example, a sentence might have the word “Ford” labeled as a car, and the machine learning algorithm will learn by itself that the following word “Focus” is evidence that “Ford” is a car in this context.

Simple machine learning can be fast, provided that you already have labeled examples for ‘supervised learning’. It also tends to be more accurate, because statistics are usually better than human intuition in deciding which features (like words and phrases) matter. The major drawback for supervised machine learning is that you need the labeled examples: if you have too few labels or the labels aren’t representative of the entire data set, then the accuracy is low or limited to a specific domain.

There has been a recent rise in the use of machine learning that learns more sophisticated relationships between features, known as deep learning. For example, if you had the sentence “We Will Let Harrison Ford Focus on Star Wars”, there is conflicting evidence between “Harrison” and “Focus” about whether “Ford” is a person or a car.

Deep learning can automatically learn how to use combinations of features when making a decision. For simple machine learning, a human has to tell the algorithm which combination of features to consider. Deep learning often cuts down on the amount of human time needed and typically gets up to 5% more accurate results than simple machine learning for text analytics–although only when applied to data from the same sources as it learned from.

Adaptive learning brings human analysts into the process at every step. This is in contrast to rule-based, simple machine learning and deep learning approaches, where the humans only create rules and label data at the start of the process. For example, if you had the sentence “We Will Help Tom Ford Escape from New York”, and your system hadn’t seen any examples of “Tom Ford” or “Ford Escape”, you will need human input to build the knowledge.

Adaptive learning systems require the least human effort because they only require human input when it matters most and continually expand their knowledge when new information is encountered. As we show here, they are also the most accurate. They combine the three other types of machine intelligence, adding new types of ‘unsupervised machine learning’ and methods for optimizing the input from multiple, possibly disagreeing, humans.