Machine Learning; The Matrix Materialized
By: Taira Mehta
Tags: Machine Learning, Supervised Learning, Matrix, AI, Unsupervised Learning

I was quite the geek growing up.

I was entranced by the world of Star Wars, Star Trek (which is far better), Transformers, and so many other fandoms out there. But, I wasn’t your average geek. Most people loved these films or shows because of the stories behind them, or the special effects the producers used. I liked them for a completely different reason: bots.

These productions were an amazing awakening into the world of a term coined as “Artificial Intelligence” and never failed to captivate my attention. The idea that humans could manufacture things more intelligent than they sounded like sorcery to me, and I wanted in on it.

Think for a second, what could our world look like with these bots?

Imagine the possibilities we would have in a world where machines can learn to do things themselves. Imagine the new doors that would open up to us because of the emergence of higher intelligence.

Imagine the world as if it were the Matrix, but materialized.

“That sounds cool and all, but you lost me on AI.”

And I don’t blame you for that. The term is so overused for purposes aside from the original meaning, and that misinterpretation is what tends to make people intimidated by the topic.

The idea of Artificial intelligence (AI) was first thought of back in 1956 and belongs to a Stanford researcher John McCarthy, who coined the term and defined the key mission of AI as a sub-field of computer science.

The basic idea behind artificial intelligence is rather self-explanatory. It demonstrates the ability of an algorithm to have intelligent traits like having original thought or learning. It was meant as a way for machines to imitate human intelligence by adapting based on situations and thinking.

For the most part, the goal of AI is to utilize a computer’s ability to process information faster and combine it with the ability to be self-adapting. This would help replace many tasks that humans have, as AI can conduct those tasks with better efficiency and accuracy.

Above: AI has many applications because it is such a huge and adaptable concept

“Cool, what’s Machine Learning got to do with this?”

It’s a misconception that the terms “Machine learning” and “AI” are synonymous, and that’s what trips people up.

AI is the umbrella term used for the many different applications of itself to tackle specific problem sets. The most common of these applications is Machine learning.

The goal behind using machine learning is rather straightforward: allowing machines to learn. Learning, in this case, would be described as improving or adjusting from experience and applying it without being programmed to tackle different scenarios. Ultimately, this would allow the software to interpret a set of data and draw meaningful conclusions from it as well.

There are three main types of machine learning: supervised, unsupervised and reinforcement learning. Let’s break it down a little.

Supervised Learning

I don’t know about you, but there’s one main way I like to study new concepts. Examples.

The concept behind making a child study with examples is that they will see enough instances and be able to retain specific knowledge from them. Eventually, when they make the connection as to why this relationship occurs, they can apply their knowledge in unknown situations.

Think of sorting shapes; the most common first-grade activity. You would be approached with shapes that looked familiar like the square, rectangle or triangle. Sorting them was a breeze, you already encountered these examples before. Then your teacher whips out some rhombuses, octagons, and parallelograms and the sorting game just got tough. It’s all about looking for relationships between the shapes after that. Sides, vertices, and size play a role in determining groups for sorting.

Swap children with computers and this is a great example of supervised learning. The basic definition of supervised learning is to induce learning with training labels. Essentially, it’s the process of providing AI with a set of data and having it draw a label from that data by looking at past examples. When you feed these examples to a machine learning algorithm, it would learn by looking for similarities and differences off of those examples given to apply it to unknown situations. Therefore, when presented with a new image, the algorithm would classify it as the mold it fits into with the greatest accuracy.

This is possible through a set of algorithms called a “Neural network”. They work by interpreting different types of sensory data using a machine perception and eventually labeling or clustering raw input.

There are three main uses of supervised learning:

Binary classification

When the algorithm classifies data into two categories

Multiclass classification

When the algorithm needs to choose between more than two types of answers for a specific variable

Regression

Used to predict continuous values, like predicting net profit as a measurement of the customer lifetime value

Above: Graphic showing the process in which data gets filtered through supervised learning

Unsupervised Learning

Take the same example with the shapes from before. Instead of having prior knowledge on the shapes such as the triangle, square, and rectangle, you do not know anything of the sorts. Now, you are presented with a bunch of types of shapes and are left to conclude on your own based on what you see. The concept of coming up with differences and similarities based on no prior knowledge is very similar to the concept of unsupervised learning.

Unsupervised learning is providing a system with unlabeled and uncategorized inputted data and allowing it to find patterns on its own. There are a few common uses of this type of machine learning. These include (but are not limited to):

Cluster analysis

Cluster analysis helps to discover new hidden patterns or groups in data

Anomaly detection

Anomaly detection spots out unusual data points in the given set. It is useful for finding fraudulent transactions.

Association mining

Association is a data mining function that finds the likelihood of the co-occurrence of items in a set

Above: The method in which data gets filtered through unsupervised learning

Reinforcement Learning

Okay, let’s go back to the Kindergarten scenario one last time. You are given random unknown shapes again and have no prior knowledge of them. Only this time, instead of doing your own thing without consequence, there’s a slight twist. Your teacher can now give you a reward based on whether you did something right and a timeout for doing something wrong. The objective for you would be to get the minimum amount of timeouts and the maximum amount of rewards. This is very similar to the idea of reinforcement learning.

Reinforcement learning, in a nutshell, is a type of programming that trains algorithms using the ideas of rewards and punishments. The algorithm adapts and learns by interacting with its environment. The algorithm gets rewarded for performing correctly and gets penalized for not. This way it learns by itself and without any human intervention. This makes it very adaptable.

There are a few main uses of reinforcement learning

Optimization

Text, speech, and dialog systems

Data processing

Create training systems that provide custom instruction and materials according to the requirement of students

Games that require strategy

Above: The method of how data gets filtered through reinforcement learning

“That’s interesting and all… but what does that have to do with the Matrix?”

Machine Learning opens up a world of possibilities.

The Matrix and other sci-fi movies always felt far off, and they showed us a futuristic outlook as to what life could look like 50 years down the road. See, with the rapid development and research that’s being done at such a fast pace, that future might not be too far off.

Machine learning enables us to allocate some independence towards machines and allows them to act for themselves without human intervention. This is huge. The next step after allowing machines to act for themselves is to allow them to think based on prior memories. After that, we may even be able to foster artificial consciousness.

Soon, bots like C-P3O or R2D2 would be seen as old school. The future isn’t that far away.

Recap

Supervised learning is when you provide AI with a set of data and have it draw a label from that data by looking at past examples.

Unsupervised learning is giving an algorithm unlabeled and uncategorized inputted data and allowing it to find patterns on its own .

. Reinforcement learning is a type of dynamic programming that trains algorithms using the ideas of rewards and punishments and focuses on maximizing rewards.

Ending Notes

To end off, Machine learning is still evolving. It is changing the way we live, work, and play even though we don’t often notice it directly. It’s something so intriguing and complex that has revolutionized the world around us and will continue to impact us in the future. Machine learning is adapting at such a fast rate, that the future seems to be getting closer to us at an accelerated speed. Our once far off dreams of robotic consciousness may even become a reality within our lifetimes.

Who knows, maybe you’ll be the next Luke Skywalker of your story.