Machine learning is one of the most exciting aspects of modern computing. It is one of the top reasons to major in computer science. Companies will pay massive bounties for programmers who are skilled in the area. You will make an executive’s salary from the firs day you enter the field. Your stock options will set you up for life. Moreover, you will be scouted while you are still in college. And like top college athletes, companies will entice you to go pro even before you finish your degree. It hardly gets better than that as a career direction.
But slow your roll for a hot minute. The reason the career is so exciting is that machine learning paired with artificial intelligence is the new infrastructure for all computing endeavors going forward. It is the framework that will undergird everything from social media algorithms to government policy. No aspect of life, including medicine and healthcare, is excluded. Everything you need to know about machine learning cannot fit into a single article. There are a lot of technical aspects of it that will take years of study to unravel. But there is also a human component that many in the field fail to consider. Here are three:
More Than Math
To be clear, there is a lot of math in computer science. There are excellent tools that can help you study for calculus. Just remember that there is a big difference between coding a simple Android app and mastering computer science. You don’t need to know anything about machine learning to code an app. In fact, you don’t really need to know that much about programming. But for machine learning, there is a rather steep human learning curve.
Before getting into the field, you need to know the human cost of machine learning. It is not for no reason that you will be paid all that money. It will take a great deal of study and dedication. Even after you have started your first job, the study doesn’t end. It never ends. The computer science industry is demanding and calls for lifetime learners. There will be new standards, new methods, and new programming languages. What you learn in school will be a foundation for what is to come. It will not be the end of the knowledge you need, but the beginning. Be sure you are ready for that level of dedication.
Beware of Bias
Humans are terribly biased when it comes to just about everything. We cannot separate our biases from other aspects of our lives. Machines have the potential to do better. But they will only do better if we remove our biases from the teaching process. It has been shown time and again that there is racial discrimination in facial recognition technology. At the heart of the problem is the racial inequity in the field of computer science.
Early facial recognition was based on caucasian faces. Computers got very good at recognizing caucasion faces. But they were very bad at recognizing non-caucation faces. Female faces were poorly recognized for much the same reasons. This did not initially stop law enforcement agencies around the world from deploying the bias-laden tech. More recent efforts are doing better. But we still have a long way to go. You have to remember that you are not just manipulating ones and zeros. You are affecting human lives that deserve systems free from human bias.
Machines Cannot Replace Humans
There are many jobs that machines can do more efficiently than humans. But the goal should never be to replace humans. It should be to empower humans to reach their maximum potential. Many corporations are only interested in machine learning as a means of replacing all those expensive and troublesome humans. If that is the goal toward which you are working, just remember that the computers could theoretically learn enough to replace you. Such a dystopic approach serves no one. Be sure to advance the technology in a way that empowers rather than replaces.
There is no denying the exciting potential that will be unleashed with the development of machine learning. Just remember that machine learning is a very human affair. It requires human dedication, the subjugation of human bias, and the desire to empower humans rather than render them obsolete.
