Photo by Raghav Modi on Unsplash
Why Math is Critical for Success in AI: A Deep Dive into the Connection
Artificial intelligence (AI) has been around since the 1950s, but it’s only recently that AI has gained the recognition it deserves. This field of study has grown and expanded in recent years, and much of that success can be attributed to the ability to process large amounts of data quickly and efficiently. This leads many people to ask whether mathematics plays an important role in artificial intelligence, which many feel doesn’t due to its perceived abstract nature. The reality, however, is much different than this perception suggests, and mathematics will always play an important role in AI development.
A historical perspective
It may seem like today’s machines are more advanced than ever before, but artificial intelligence can trace its roots back to ancient Greece. Aristotle described a form of logic and deduction that was some of the first steps toward developing AI. However, it wasn’t until Alan Turing developed a test that computers could be said to think. Unfortunately, computers at that time weren’t powerful enough to complete his test, so they were declared unable to pass it with flying colors.
It took almost fifty years for another computer scientist — John McCarthy — to develop an algorithm called Logic Theorist that finally completed Turing’s test by proving its ability to solve calculus equations used in physics.
Since then, AI has continued to grow and evolve. Today, many people refer to these advancements as machine learning or deep learning algorithms because they rely on neural networks (artificial neurons) instead of hard-coded rules and programming language. This means that computers don’t need humans to program them; rather, we teach them what we want them to do through trial-and-error methods. They learn from their mistakes (in much the same way humans do), which makes them faster learners than any human could hope to be!
As you can see, mathematics is important when studying artificial intelligence because it helps us determine how our current systems work as well as how we might improve them in future iterations.
What’s the difference between Algorithms and Math?
To understand why math is so important to AI, it’s first helpful to understand what algorithms and math are.
An algorithm is simply a process or set of steps. Algorithms can be as simple as rules for playing games or a complicated computer program that tracks shipments and inventory. The world runs on algorithms — they tell you when to stop at a traffic light, and what price to pay for an item online when your driverless car should make left-hand turns versus right-hand turns.
AI algorithms are no different. They’re just sets of instructions that help computers learn, sort through data, and make decisions.
A math equation is also a set of instructions — it tells you how to solve for x (or y or z). Without math equations, we wouldn’t have GPS devices or calculators.
AI relies heavily on math equations because they’re used to solve complex problems like how best to deliver packages across town (or across oceans) or how best to design self-driving cars so they don’t get into accidents. Math equations also help us prove why our AI systems work (or don’t work). We need them to describe patterns we see in nature and then apply those patterns using technology.
Why is there such a high demand for AI right now?
While AI has existed for decades, its widespread use is a more recent phenomenon. Recent advancements in computing power — specifically GPU (graphics processing unit) acceleration and even smarter neural networks — have made it possible to deploy AI at scale.
Some estimates predict that today’s demand for AI could grow rapidly. That’s why there’s so much hype around AI right now. It’s ready to explode into every industry. And we are all waiting for it to transform our lives and enterprises. But before we get too excited about AI, let’s take a look at what it actually is. Simply put, artificial intelligence refers to machines or software programs capable of performing tasks usually associated with human intelligence such as learning and problem-solving. This type of software can also be referred to as machine learning or artificial neural networks.
How do you know what math skills are required?
A common problem for people trying to learn new skills or topics is where to find more information. A good first step is to ask your teacher, mentor, or classmates where they would recommend you look. They will be able to point you toward great resources that go into more depth on a subject than you might be ready for right now. That said, if no one can help you with that question, don’t worry! A quick Google search should answer most basic questions (and even some advanced ones). It’s also worthwhile to check out relevant subreddits and Quora communities as well as perusing Wikipedia pages — you never know what piece of knowledge could come in handy later!
Where can I learn more about these subjects?
If you want to take your computer science knowledge deeper, check out these resources. Coursera has a terrific introduction to machine learning, which you can learn from for free. If that interests you but doesn’t scratch your itch for programming language specifics, look at their follow-up course on Python as well. And if you are interested in learning more about how mathematics relates to artificial intelligence, there is an incredible amount of material available online. Try starting with Stanford University’s CS231n: Convolutional Neural Networks for Visual Recognition and Udacity’s Introduction to Machine Learning. And if you want some more practice with coding and data analysis, try Udacity again — their Intro to Data Analysis Using Python class is fantastic!
Thank you for reading 😁
Have a great day!