Is Machine Learning Going to Take Over Computer Science Jobs?

Will Machine Learning Take Over Computer Science Jobs?

Before we find out whether machine learning (ML) will take over computer jobs, we need to understand what is machine learning?

Machine learning is an application of artificial intelligence (AI) based method of data analysis, where an algorithm finds patterns and insights from a given pool of past data that it then applies to new data, primarily to make better decisions and accurate predictions. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. In other words, machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to learn with data, without being explicitly programmed. Machine learning tries to solve problems which are normally reserved for human abilities.

With the advent of big and unstructured data, faster computational speed, very cheap storage and need for customized and real-time solutions, the machine learning field is nearly grown now.

Does this mean that machine learning will take over computer science jobs? Travis Addair, a software engineer with an eclectic background and who has worked on applied machine learning at Google, came up with a response on Quora:

Here’s a bold prediction for you: machine learning is NOT going to take over the computer science jobs, but computer science will automate machine learning jobs.

Well, maybe after I explain what I mean it won’t seem so (figuratively) bold.

You see, most of what we call applied machine learning today is actually a relatively unglamorous meta-optimization problem. We’re trying to explore the space of feature representations, sampling strategies, hyperparameters, model types, and model configurations to get the best performance on our test dataset.

In practice, this process can best be described as guesstimation: you try one combination of all these different variables, you see how the model does, then you think “well, the model did poorly on X performance metric, so let’s try changing variable Y”. And this process basically continues in a loop until you’re satisfied with the performance of your model.

In some ways, the process is so well-defined that it practically begs to be automated. And already we’ve seen a lot of progress on this front through tools like AutoML that allow people with little-to-no machine learning expertise to build complex machine learning models. So, already in the span of a few years, we’ve made significant progress in “democratizing” or “automating” the machine learning process, and yet in decades and decades of effort, we’ve done little to move the needle on automating software development. Hmm…

Now, this isn’t to say that there aren’t significant challenges in solving a real-world problem with machine learning, but in large part, those challenges are orthogonal to the actual machine learning modeling process I described above. The hardest thing about machine learning in an industry is (1) figuring out what the right data is to solve the problem, and (2) figuring out how to integrate a working model with a production system. Both of those require domain expertise, and the solutions are specific to the individual problem being solved. In other words, there’s no easy path towards automation. But they both require talented data scientists (for the former) and talented software engineers (for the latter) to solve.

So despite whatever Mark Cuban or anyone else is saying these days, software engineering and computer science are here to stay. However, don’t be surprised if knowing how to code an LSTM in TensorFlow isn’t as hot a skill in a few years.

So, there you have it! Machine learning will not reduce the need for computer science jobs significantly (if at all).

Source: Forbes

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