The Science Behind Maglev Trains

Fast and safe modes of transportation are very important in connecting people to their workplaces. Often, residents who live in less urban areas rely on forms of public transportation, such as trains and buses, to get to their jobs in the city. While the US has not prioritized the development of these unique trains, they are gaining popularity quickly in China, Japan, South Korea. China recently built the world’s fastest train for public use, which can travel at up to 373 miles per hour (mph). The secret lies in something that we often play with when we’re bored: magnets.

Maglev is short for “magnetic levitation,” the underlying mechanism that makes these trains seem to be gliding at high speeds. Magnetic metals, such as iron, nickel, and cobalt, have two poles: a north pole and a south pole (similar to the Earth itself). While opposite poles attract, two of the same poles repel one another. 

Source: Encyclopedia Britannica

All maglev trains take these attractive and repulsive properties of magnets to make the train levitate as it moves. The magnetic poles on the bottom of the maglev train are the same as the magnetic poles of the rails below it, causing the train to levitate due to repulsion. In the meantime, magnets on the side of the train both attract and repel magnetic plates on the railway to propel the train forward. 

Source: U.S. Department of Energy

To review: repulsion forces between the bottom of the train and the rail cause the train to levitate. Attraction and repulsion forces between the sides of the train and the platforms on the sides of the train make the train move forward.

The repulsion forces that make the train levitate are pretty straightforward (see diagram above). However, the forward movement of the train depends on two main concepts: electromagnetism and superconductivity. Electromagnetism is the powering of a magnet from an electrical current. For maglev trains to move forward, alternating magnetic poles on the platforms next to the train have to be turned on so that like poles can repel one another and opposite poles can attract one another (see image below). The faster that these poles are alternated by electricity, the faster the train will go. Meanwhile, something needs to make these magnets as strong as they are to levitate the train and push it forward–this ability comes from the superconductivity of the magnets. Superconductivity refers to the enhanced ability of metals to conduct a magnetic force when they are cooled to very low temperatures. Basically, for a maglev train to glide forward, its magnets must be cooled down to low temperatures to strengthen their force, and electricity must be used to switch the poles of the magnets next to the train as the train glides by.

Source: University of Technology Petronas

As you can imagine, there are some downsides to maglev trains, such as the energy cost of cooling down the metals underneath the train and the electricity cost of powering the magnets underneath and next to the train. While maglev trains are expensive, they do have quite some benefits over traditional trains. Since they do not make contact with the rails underneath, damage to the train’s underside is less likely. In addition, they are faster and quieter than traditional trains. As more maglev trains are built and glide along their tracks, it’s important for us to know about how they work!

AI, Machine learning, Hands of robot and human touching on big data network connection background, Science and artificial intelligence technology, innovation and futuristic.

Artificial Intelligence vs Machine Learning

By Gael Hyppolite

Artificial intelligence and machine learning have both become something of a buzzword within modern tech. Many modern products boast the use of neural networks or advertise AI as a part of their functionality. You’ve likely experienced targeted ads or filtered content on some media platform; that’s AI. Perhaps you have a voice assistant on your phone or computer that can answer questions and perform simple tasks; that’s AI. Maybe you’ve played against the CPU in a video game; that’s AI. AI shows up in a much wider range of situations than most people might think, from chatbots to email spam filters. 

However, despite the prevalence of artificial intelligence, many people don’t actually have a great understanding of what “AI” truly means, and what its connection is to machine learning. For example, of the technologies I listed, only a couple of them make use of ML despite all of them being AI. This of course raises the obvious question: what, if any, is the difference between AI and ML?

What’s the Difference?

The simplest, or most direct answer to this question, is that AI refers to what the technology does, while ML refers to how a technology works. To understand what this means, let’s take an example: chatbots. In its simplest form, a chatbot provides pre programmed responses to specific statements. You give a greeting and it responds with “Hello”, or you ask a question and it looks up an answer. This is an example of a “Type I” or “Reactive AI”. Being the most basic type of AI, it simply takes in and responds to information. These types of AI have no memory or internal understanding of the tasks they perform, they simply provide a particular response when presented with a particular situation. In other words, these types of AI are incapable of “learning”.

The chatbot qualifies as AI because it attempts to mimic humans through its limited ability to converse. However, ML refers to a machine that is capable of learning from the situations it encounters and adapting how it approaches and solves on its own. Therefore, the chatbot doesn’t make use of ML, as it fails to “learn” anything from its interactions. An example of AI which does make use of machine learning would be the youtube recommendation system. Everytime you click on or like a video, youtube updates an internal metric that it uses to decide which videos that you’ll most likely want to watch next, and then recommends those videos to you on its front page. It is therefore a “Type II” or “”Limited Memory AI”, as it remembers and learns from the situations it has previously encountered. Most modern AI fall into the “Type II” category, which effectively requires that they make use of machine learning.

Therefore, the fundamental difference between machine learning and artificial intelligence, is that ML refers to how a subset of AI is implemented. As for AI itself, the definition of artificial intelligence is much looser than many people realize, simply referring to machines that mimic human intelligence in some way.

The Future of AI

There are in fact 4 types of AI that we use to classify the development of artificial intelligence. I’ve already explained the first 2 of these, but I will list all 4

Type I AI: Also known as Reactive AI, they are only capable of giving predefined responses when presented with a specific situation. These AI have no concept or understanding of the tasks they perform.

Type II AI: Also known as Limited Memory AI, they can keep track of and learn from past events in order to adjust their actions in the future. Most AI today fall into this category.

Type III AI: Also known as Theory of Mind AI, these are currently the realm of imagination and science fiction. While none have been created, they would theoretically be capable of understanding humans and other entities as individual agents. They can understand the deeper meaning behind human emotion, learning and responding to stimuli more quickly by understanding intent.

Type IV AI: Also known as Self Aware AI effectively represents the singularity. This is the point at which AI not only understand the thoughts and emotions of others as agents, but recognize their own individuality and possess their own thoughts and feelings.

The jump from Type I to Type II AI required the development of one of the greatest innovations of the 20th century: machine learning. Furthermore, it required us to refine ML over the course of nearly a century in order to make it a feasible technology. The ability for machines to learn was so revolutionary in the field of AI, that many people began mistaking it for AI itself (though you’re no longer one of those people). Since then, we’ve made attempts to develop Type III, but we’ve made no significant progress. Such a massive jump in AI technology will likely require the development of a strategy similar in power to ML, not to mention what would be required to develop Type IV AI.

Such developments are probably decades if not centuries off, as we’re still only now beginning to understand the power and applications of Type II AI.