# 127: Learning about Learning
Story behind the Passage
Is it normal that people who learn a lot also think a lot about learning? And why do I even worry about that? I mean, I already know that my ‘normal’ is usually not the normal of others. But I am not asking myself this question because I care so much about how others think about this. I just think that there is a pattern — that learners also get fascinated with the topic of learning itself. Of course, this is not a general claim but I do see it. If you look at what is happening in Artificial Intelligence (AI) and machine learning research, all of this is about learning. Getting into this more deeply actually makes me wonder what will happen to “old” theories of learning. Will they remain relevant? Can they even help? Do even the most foundational theories have an expiration date in digital times?
The first time that I ever got in touch with learning theories, I think, was in the second semester of my media studies program. It was in the context of media effects studies and particularly violence in the media. I remember how my professor explained the origins of this kind of research in the post-war propaganda studies and later in the research on how negative behavioral effects can occur based of violent media content consumption. As always, these theories went through different stages of contestation but the point is, the issue is still relevant today. Of course, we do not only talk about watching crime mysteries on television anymore, we talk about all kinds of digital media effects. Still, even though I am not in media studies anymore, I am sure that many of the “old” theories remain relevant.
Or am I completely mistaken?
My Learnings
“Albert Bandura geht in seiner Theorie des Beobachtungslernens davon aus, daß sich Menschen, indem sie das Verhalten anderer Personen verfolgen (in der Realität oder in den Medien) und daraus Regeln abstrahieren, Handlungsmuster aneignen („Lernen am Modell“).“ / “In his theory of observational learning, Albert Banduea assumes that people adopt patterns of behavior by watching the behavior of other people (in reality or in the media (“model learning”).” Since I am just startint to get deeper into IT, there is no way that I can describe in detail already how exactly Bandura’s approach can be applied to machine learning. What I know so far is that, indeed, computers learn by “watching” media, e.g., pictures, to then develop patterns and infer rules. After all, algorithms are all about rules. But in this case, we, humans, train the computer intentionally. We tell the computer what to look at and what to learn.
What if this is going to change soon?
I am sure it already has. Think of self-driving cars. The computer/car moves through the landscape and “sees” things that humans did not intentionally place in the way. Hence, the computer sees/senses, selects, and focuses on these object to then learn from them. As far as I know, this process is quite long and tedious. But I am sure this will change soon. I have no negative or positive judgement of this when it comes to ethics. I simply think that there are many exciting breakthroughs ahead in the near future. And since I am an optimist after all and I like technology, I look forward to this.
What I am just thinking about is what all this will do to theory development. When looking at my old textbook from 15 years ago, it looks ancient. Look at all the things that have happened in the meantime. Back then, my professors did not talk about AI. Still, the theories they presented often dated back 30 or more years. And they were still relevant, even though further additions and revisions of the very early assumptions had been made. But basically, these theories provided crucial clues to understanding the bigger picture of media consumption. Now I wonder whether this will change dramatically — that even the big theories might become outdated more rapidly because new findings are revealed at a faster pace due to Big Data analysis.
When it comes to Bandura, there is, of course, the possibility that not only computers will learn from us humans. We will also be learning from them. I mean, not just by studying courses online, etc., but learn from robots that are even more intelligent than the current software applications. When I say robots, I mean intelligent machines that are also able to read us and our responses, even on an ‘emotional’ level. I am pretty sure all of this is happening already, even though I do not exactly know how the technology behind it works. But I am determined to learn more.
After all, learning and thinking about learning are two very different things. Simply learning is something that mostly happens without you being aware of it. Humans learn every second. But whenever you start thinking about all this on a more abstract level, things can become quite complicated. Not in a sense that you cannot manage, but the borders are hard to draw. I think, this is the most important problem for chronical learners anyway. No matter if you call them “deep generalists” or “polymaths” — there are hardly any limits to what they learn. So, I guess, the only limit can be drawn by personal interest. Whenever you lose the motivation to continue, you stop.
For me, this is closely related to pattern recognition. I think, as soon as I at least think that I have understood a major pattern and new information just confirms the pattern, I lose interest. Of course, this is quite dangerous because you then fall into the trap of generalizing too early. Nevertheless, it makes me quite happy to know that there is a natural border to learning that I am not going to cross because I see no point in it. This might be a mistake or something that prevents you from going into depth. But the point is: I cannot look into the brain of others and assess how deeply they are into something, even if they write about it. So, I guess, the only constant that connects most of my interests is this one overarching theme: learning about learning.
Reflection Questions
1) Did you ever think about how you learn?
2) Is learning something that you actively integrate in your daily life? How?
3) When you think of the future of technology and Artificial Intelligence — how do you feel about it?