Source: Deep Learning on Medium
You gotta love your grandparents. They give great advice along with answers to almost everything, haven’t lost their sense of humor, always up for an adventure, and of course love their grandchildren unconditionally. Grandparents are awesome, except when you’re trying to show them how FaceTime on an iPhone works. Grandparents are slowly coming to terms with technology, but explaining it to them can be pretty difficult let alone training them.
“My grandma calls Mozilla Firefox, Godzilla flame wolf … and I’m not correting her.”
So how do you explain machine learning in a way that a Grandmother who does not have a background/experience in STEM can understand it. In order to do that I won’t get too technical, but I will get in depth into the topic and explain it in simple terms. Just the big pieces I feel will cover the important parts of machine learning. To make sure I don’t get too technical I’ll be referring to you, the reader, as grandma from time to time to remind myself that I need to explain this as simple as possible and not get ahead of myself. 👵🏼 😉
So granny, what is machine learning? Machine learning is field of study that gives computers the ability to learn without being explicitly programmed. It is a very exciting and growing field as it makes computers more similar to humans. It is different from traditional computer science approaches. In regular computing, instructions are already given to the computer to calculate and solve. Computers are really dumb and have to be told exactly what to do and how to do it. With machine learning though, we can give a computer lots of data to analyze and it can train on that data to produce output values that fall within a specific range. For example, you know how you post pictures on Facebook grandma and it recommends people you should tag because they too might be in the photo? Oh, you don’t? Okay, how about when you’re watching Netflix and it recommends shows or movies you might like? That’s a little taste of machine learning. When cars drive themselves, machine learning will play an important role as they’ll collect lots of data and learn how to drive better and safer. Hopefully this is making a lot of sense because we haven’t got into the best parts of machine learning. One thing is for sure though, it will play a huge role in our lives going forward.
What Machine Learning Is Not
Machine learning is not robots out coming to destroy humans like you see in the movies. Terminator is the first thing people think of when the words artificial intelligence or AI come to mind. That’s another thing I want to talk about. Machine learning is not artificial intelligence, but a subset of AI. This field has been around for quite some time, with the roots going back to the late 1950s. During that time period IBM’s Arthur L. Samuel created the first machine learning application, which played chess.
Another buzz word you probably heard, which often gets confused with machine learning as much as AI — that is, deep learning. Deep learning has been around just as long as machine learning, but it wasn’t until the 1980s that the field gained traction. Eventually the big companies like Facebook, Google, and Microsoft would invest heavily in the technology. The result has been a revolution for AI. For example, things like Google Translate or Apple’s Siri are examples of the power of the technology. I won’t get into what AI or deep learning is as machine learning is already a big subject to cover by itself. Just know there is no threat with machine learning, maybe with AI though if it gets out of hand.
How To Get Machines To Learn
So you might be thinking how exactly do we get these machines to learn? How does a computer collect all this information and make sense of it? Well I can tell you that there’s a lot of math involved and computer algorithms to help produce the desired results. Alright grandma, I’m gonna break it down a simple as I can for you, but at the same time explain in detail what is under the hood of a learning machine.
The Math That Comes With Machine Learning
Math was never my favorite subject, but we’ve all come across linear algebra. Linear algebra is a field of mathematics that is universally agreed to be a prerequisite to a deeper understanding of machine learning. Although linear algebra is a large field with many esoteric theories and findings, the nuts and bolts tools and notations taken from the field are practical for machine learning practitioners. With a solid foundation of what linear algebra is, it is possible to focus on just the good or relevant parts. Math is important in this field because you need to know which algorithms to include when considering accuracy, training time, and a bunch of other stuff 😵💫. The math just helps us find a way to help the machine learn and make sure it does it in the best way possible. Other than linear algebra a scientist/engineer will also need to know the mathematical concepts of Calculus, algorithms, probability theory and statistics. Python is the most used programming language in this field as well. A beginner doesn’t actually need a whole lot of math to get started in this field.
Think of a brain
The world has a lot of information and our brain takes it all in to form our view of reality. A computer has to be able to do the same and that’s where a neural network comes into play. Neural networks is the most popular way to get a computer to mimic a human brain. Our brains are really good at solving problems, but each neuron is responsible for solving a tiny part of that big problem. Think of it as an assembly line where each neuron in your brain has a certain job to do to solve a problem. So their inspired by the brain, but in
what way are neurons linked together? Your brain is made up of approximately 100 billion nerve cells and those are called neurons. The have this cool ability to collect and send signals. Think of it like a network with a bunch of wires.
A neuron takes in inputs and and produces outputs. The input nodes (input layer) provide information from the outside world to the network. Similar to how your eyes sees and collects information and sends it to the brain. The output nodes (output layer) are the ones responsible for communicating that information back to the outside world. Lets say that the network below is trained to recognize digits. So if give a number in the input layer that will go through the hidden layer and then come out the output layer as the number being recognized. The neurons in the hidden layer are going to communicate the information they got from each other to best piece together what number they think is being passed. Each layer influences the next.
Things can get even cooler when your training to network for other things like audio recognition. Parsing speech or breaking down audio and picking out distinct sounds, which combines to make certain syllables, to certain words, phrases, and etc. This is how you wanna think of it when building your own network.
- convolutional network— for image recognition
- long short-term memory network- good for speech recognition
There are other methods for a machine to learn like supervised learning, unsupervised learning and reinforcement learning. I won’t be covering those topics, but those are the three methods that are often employed. To simply put it grandma, a neural network lets the computer take information, break it down into pieces it can understand, and then outputs the closest outcome it thinks it is.
The Challenges And Limitations
As awesome as machine learning is there are limitations to it. I’ll bring up the biggest ones I think need to be overcome for this technology to continue to move forward. So machine learning algorithms require massive stores of training data and labeling that data is a tedious process. You need to make sure that the data being fed into the machine is labeled. If not, it is not going to get smart over time. An algorithm can only develop the ability to make decisions and behave in a way that is consistent with the environment which is required to navigate. Machines also can not explain themselves and that can be difficult when you want to know why that particular decision was made and how. Lastly, and I think this one is the most important, is to avoid bias. Transparency is important and unbiased decision making builds trust. For example, facial recognition has a large part in social media, law enforcement, and other applications. But biases in the data sets provided by facial recognition can lead to inexact outcomes. If bias finds its way into an algorithm and data sets and the training data is not neutral the outcomes will inherently amplify the discrimination and bias that lies in those data sets.
The Future Is Machine
The future of machine learning is unstoppable and is the fundamental building block for artificial intelligence (AI). Today it already plays a role in our lives. If you use Spotify to listen to music you will see it creates daily mixes for you based on what you’re listening to. Amazon learns and teaches itself how to get products that might be of interest to you based on your buying habits. Virtual assistance like Amazon’s Alexa, Apple’s Siri, or Microsoft’s Cortona, use machine learning to help understand your speech and the language humans use when they interact with them. Businesses are obsessed with this technology because it can automate task normally done by humans. Chatbots and service bots that can provide customer service for a lot of companies are able to respond in an intelligent and helpful way. This can increase the amount of auditory and text questions. My favorite place to see machine learning being used is in autonomous cars and trucks with vision. Vehicles need to be able to understand the realities of the road and how to respond to them whether it’s a stop sign, a snow storm, a ball in the street or another vehicle is through machine learning. The more data they collect the better they can act human-like with the information they are processing — knowing a stop sign covered with snow is still a stop sign.
So there you have it grandma. I know I lost you somewhere, but hopefully you have a better understanding on what machine learning is. I see machine learning as a tool that can continue to make our lives easier. People continue to come up with more useful ways that machine learning can be used and are disrupting industries by doing so. I can only imagine where we will be when this technology leads to real AI.
Simple explanations of Artificial Intelligence, Machine Learning, and Deep Learning and how they’re all different…medium.com
Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning generally is to understand…www.digitalocean.com