This gave me pause. This client was describing solution in which a machine could help someone determine the best way to learn. This would mean teaching the machine how to understand and respond to unpredictable and unknown factors. In short, she was describing a machine learning solution.
Her idea may seem far-fetched, but it’s not. We are in the midst of an exciting technological revolution in which such an idea is entirely possible.
First, what is machine learning?
A web search defines “machine learning” as: “An application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed…[focusing] on the development of computer programs that can access data and use it to learn from themselves.”1 However, gaining a deeper understanding of this new technology is more challenging.
How do machines “learn for themselves,” and why is this such a remarkable advancement? The answer is the inductive learning process. Here is a classic example as understood by the famous syllogism:
- Socrates is human
- All humans are deadly
- So Socrates is deadly
In an inductive learning process, the second line is removed, requiring you to make the connection between the first and third by yourself:
- Socrates is human
- So Socrates is deadly
Humans can understand this syllogism easily, but for machines, creating an algorithm that learns to make this induction is a challenge. But we are very close to achieving this.
Once this technology is perfected, a world of possibilities will open up for the use of machines.
Why does machine learning matter for developing people?
Technology and innovation are changing the way we learn.
First came the revelation that learning outside of lecture and text – such as through business simulations and experiential learning – provides unmatched value. Experiential learning allows people to get hands-on experience and practice making decisions in life-like situations without the risk associated with making them in real life. Mistakes are critical to learning, so what better way to learn something than by practicing – and, yes, often failing? Leveraging experiential learning to build innovation leadership, especially in a business context, has allowed for the optimization of learning practices throughout the industry, and utilizing technology has been essential for bringing these experiences to life.
Today, machine learning provides the platform for bringing the best, most experiential solutions to learners, and will revolutionize the way we gain knowledge and skills.
We’ve generated some ideas about what this could look like, hypothesizing that machine learning could utilize algorithms to:
- Recognize people’s learning styles
- Connect roles with learning styles to point towards the best coaching paths
- Identify the changing needs of the ever-moving markets and connect people with the skills they need to compete in it
- Suggest better content, methods and learning processes for different individuals, depending on the profile of the specific employee
How could machine learning be applied to the question my client asked?
Returning to my client’s initial question on how an LMS system can help employees make better decisions, the team came up with a solution.
Together, we imagined a software program to help people find the best possible configuration of blended learning paths. Clients would have multi-device access to this software, and the suggestions would come from previous requests, similar to the way Amazon suggests items for people to buy. Although it’s not ready yet, we have produced some incredible results using the test-retest or “lean start up” system to figure it out. It’s only the beginning, and we are just starting to imagine what we could create in the future.
This experience, driven by client demand, helped us reflect on the possibilities for using machine learning in other ways and think about which new algorithms could be useful to help people learn better and faster – as well as what impact they could have on organizations.
How could these ideas become a reality?
Reflecting on these ideas, I asked myself, “What would have to be true in order for these ideas to be brought to life?”
First, the demand for a machine learning product would need to be high. This is an impending reality, as the clients of machine learning (a population soon to be made up of mainly Millennials and Generation Z) were born during a time when personal computers were a norm in the household and have high expectations of and are comfortable with technology.
Second, technology would need to be even more accessible than it is currently.
Third, “human learning” would need to be facilitated by “machine learning.” The primary purpose of this innovation is to help people, and it is essential not to confuse the means with the end.
We are on the right track with machine learning, and we will be there soon. In some ways I feel as if I have already learned from machine learning. This experience with a client has reinforced my own personal algorithm: listening to the customer is always the source of innovation.