Machine Learning and Human Self-awareness

With all the talk around machine learning, it causes us to reflect how humans learn. What are the parallels between humans and machines? What can machine learning teach us about our experiences and the actions we take based on our experiences?

ML is a way to provide meaningful experiences to machines. 

Photo Credit: Alan Levine

We convey information to silicone based entities in a language they understand, “When this happens, this other thing tends to happen.” Or, getting slightly more complicated, “When these four things happen, with some of those things being more significant than others, this other thing has a very big chance of happening.”

What makes machine learning different from run-of-the-mill statistics is that we tend to care less about the process or even the veracity of the data. The outcome is all that matters. If a machine is able to experience enough scenarios and outcomes, there is a fairly good chance it can provide us with a prediction. 

If machines learn by experiencing data, there is theoretically no limit to what they can learn. Data is the limit. A machine needs enough of the right kind of data for its predictions or insights to be meaningful. 

Humans learn through experience as well, but the sheer number of datapoints processed through their five senses is astronomical. Think of going for a walk. Every forward leg movement is a vicious, light-speed cycle of inputs and their resulting outputs. Not only are we learning as well walk, but we’re taking into account years of walking/learning experiences. We have multiple models going on at once.

There isn’t a single action humans take that isn’t informed by nearly infinite numbers of data points. Human decisions are the result of a form of ‘supervised learning’. We act, experience, and choose to act again based on an aggregation of results or outcomes. And to add to the permutations of parallelism, we’re impacted by external models (other humans).

What are the experiences and training we’re providing each other? How does abuse impact a person expectations of outcomes? How does this impact the actions they take in the future? How does poverty impact the ‘supervised learning’ that humans experience? When a person lands in jail, how did they get there? What models are society using to put them there? When someone does something to contribute positively to society, how do we create responses that affirm these actions and stimulate more of them?

The more we explore machine learning, the more we’ll learn about ourselves. My hope is that this will provide us with a level of enlightenment and self-awareness that we’ve not seen before.