“Machine learning” is a hot new catchphrase these days, but its origins go back to the 1950s when Arthur Samuel invented a computer that could learn how to play checkers against human opponents.
Now, machine learning has become much more sophisticated. Machine learning as a principle of software development is opening up whole new worlds of possibility, so you should be at least slightly familiar with the subject.
Here’s what you should know about machine learning as a software engineer.
1. The Definition of Machine Learning
Explaining machine learning to the laymen is slightly difficult, but if you’re a software engineer, you at least have some idea of what it is. Simply put, machine learning is the automation of automation.
Traditionally, automation occurs when a software developer creates a program for a computer to use. When a program is run, the computer (and its attached tools) executes a set of instructions to carry out a task. This is how “robots” manufacture cars, solve math problems and run web applications.
On the other hand, machine learning involves creating an algorithm and providing data points so that the computer can create and run its own code.
For example, rather than giving a computer instructions on how to put together a car, you give the computer data points and examples of all the ways cars are made, and the computer uses this to come up with its own rules.
2. Machine Learning is Only as Good as the Education
Though some like to pretend otherwise, we are still quite a ways off from true artificial intelligence. Even with machine learning, computers are still dumb, hyper-literalists.
This means that a machine’s ability to “learn” will only be as good as the data you give it. If you wanted to teach a computer how to write a novel, but only gave it Dr. Seuss books as data points, it’s going to write a lot like Dr. Seuss. It can’t synthesize the information in a more creative way as our brains can.
3. Machine Learning Can Highlight Our Biases
Because machine learning is so reliant on the data we provide it, it can highlight our biases in powerful (and dangerous) ways. Despite having already put these machine learning practices to consumer use, major companies are having to reckon with the fact machine learning has the capacity to bring out the worst our societies and psyches have to offer.
YouTube’s recommended video algorithm, for example, has been unintentionally guiding viewers towards racist and conspiratorial videos.
So, as software engineers, it is our job to make sure that machine learning is used for good, and we need to keep in mind that these things still need human supervision.
Want to Work With Machine Learning as a Software Engineer?
This quick article should have given you a nice primer on what you should know about machine learning as a software engineer. If you a career in machine learning is something that interests you, be sure to check out the Careers section of our website.
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