What Can Educators Learn From IT?

 “With intelligence comes power”

d757f371be791107a88c3bee3146fb10Education, its infrastructure and role are topics of immense interest and concern to me. Perhaps this is the reason it took a while to put my numerous scattered thoughts and ideas together and complete this post.

To keep up with the rapidly advancing world, education system and its whole philosophy needs to be fundamentally adjusted. Incorporation of  digital technologies and personalization of the curricula are just “evolutionary” adaptations. These are changes that sooner or later will occur automatically. However, to truly revolutionize the field, educators should keep their minds open and try to adopt principles that proved to be successful in other fields. They can learn from the disciplines that are the main engines of the rapid growth, namely, IT (Information Technologies) and its subcategories, such as AI (Artificial Intelligence) and ML (Machine Learning). A few ideas/methods prevailing in these industries that, I think, are particularly relevant and applicable to the academic system are neural networks, genetic algorithms and recursive self-improvement. Let’s debunk these concepts one by one and see how exactly they can be applied to education.

  • Neural Networks

The first method, I want to introduce is used/researched in the AI and ML industries. An artificial neural network is an example of a computer architecture that mimics the brain. It starts out as a network of transistor “neurons,” connected to each other with inputs and outputs. Initially, it knows nothing—like an infant brain. To learn something, it tries to do the task, say handwriting recognition. At first, its neural firings and subsequent guesses at deciphering each letter are completely random. However, when it’s told it got something right, the transistor connections in the firing pathways that happened to create that answer get strengthened. On the other hand, when it’s told it was wrong, the connections of those pathways are weakened. After a lot of this trial and feedback, the network has, by itself, formed smart neural pathways and the machine has become optimized for the task.

Looking closely, one can see how similar this system is to the human brain. The fact that it succeeds should serve an alert for educators that something is inherently right about this system. That very “something” should be incorporated into education systems as well. Artificial neural networks “learn” by trying things in practice. Nodes choose their  paths, make mistakes and internalize information by truly experiencing it. On the contrary, current academic systems do the opposite. They praise precision and the absence of mistakes. Everything is aimed at reducing students’ chances of making any mistakes. They are encouraged to learn solely the right things and doing things right from the very beginning. Surely, this might be a more efficient way. Yet it doesn’t match the way our brains work and learn biologically. It doesn’t encourage creation of unique paths which do not resemble any other systems.

Emphasizing the ability to apply knowledge and create unique “paths” more than accuracy and standardization will boost critical and original thinking. Eliminating the fear of mistakes, such a system will also have a positive impact on students’ self-esteem and mental health.

  • Genetic Algorithms

The second method I find interesting is called “genetic algorithms”. Essentially, this method emulates evolution. It works like this: there is a performance-and-evaluation process that happens again and again. A group of computers try to do tasks, and the most successful ones are bred with each other by having half of each of their programming merged together into a new computer. The less successful ones are eliminated.

Darwin’s theory of evolution in itself provides valuable and thought-provoking insights for education. This particular system shows precisely what works in evolution. It all boils down to adaptation and synthesis. In this metaphor computers are not the equivalent of the students but that of their ideas, thoughts and expertise. Instead of making students come up with groundbreaking ideas all alone, the academic system should encourage true cooperation and synthesis. By this, I do not mean the common team-working exercises, etc. that are already part of many modern curricula. What I mean is that students should learn how to listen to each others’ ideas and how to make connections. Current team-work exercises are based on similarities rather than differences. However, to gain the most out of cooperation, the system should emphasize the integration of the differences and not the juxtaposition of the similarities. The former will multiply the power and boost a flow of new ideas, whereas the former simply increases productivity.

  • Recursive Self-improvement

The final method is called recursive self-improvement. It wasn’t long ago that Google presented its innovative automated machine learning technology that uses this method. Basically, what they did was to build a computer whose two major skills are doing research on AI and coding changes into itself—allowing it to not only learn but to improve its own architecture. They teach computers to be computer scientists so they could bootstrap their own development. Recursive self-improvement works like this— An AI system at a certain level—let’s say a kid—is programmed with the goal of improving its own intelligence. Once it does, it’s smarterat this point it’s at Einstein’s level—so now when it works to improve its intelligence, with an Einstein-level intellect, it has an easier time and it can make bigger leaps. These leaps make it much smarter than any human, allowing it to make even bigger leap. This is the way technology plans to cope with the Big Data,

This model illustrates a crucial point: today, the ability of self-development is the ultimate driver of growth and advancement. No matter how superior the educational materials, academic curricula and structure are, if students do not know how to learn, research and think independently, they are doomed to fail to keep up with the world that has an inconceivable pace of development. However, if students learn how to reason and how to find relevant innovation, they can always pick up new skills and remain up-to-date in spite of the arrival of the “robotic era”.

To sum up, looking outside and borrowing ideas from other disciplines, educators might find valuable ideas that can help renovate the academic system. Education is not only the axis of economic development but also a vital factor influencing individual human lives and the condition of the society as a whole. Hence, constant adaptation of the system is of utmost importance. Perhaps it’s easier said than done, however, thinking about the essential things collectively and sharing ideas we can come up with better solutions.

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