Machine learning is the branch of artificial intelligence which allows computers to learn without programming. The tremendous amount of progress made in this field in the last few years has led to the deafening buzz surrounding it. Here’s an attempt at debunking some common misconceptions about Machine Learning Engineers, i.e. people working in this domain.
 Machine learning engineers create machines with human-like cognition
This is one of the most widespread misconceptions. The belief that machine learning engineers are capable of creating machines which can display human-like understanding and thinking ability is wrong. No one can judge the future. But, at least in the present, contemporary machine learning models aren’t equipped with human-like cognition.
 Machine learning engineers are only required to feed in data day after day
It is true that machine learning systems need data for training. But, claiming that it is the only thing that machine learning engineers do is misleading. Machine learning models use a combination of many sophisticated algorithms to make use of the data provided, assuming the data is well-organized. Hence, while machine learning engineers feed in data, they also think holistically and factor in components like logging or A/B testing infrastructure.
 Machine learning engineers are masters at creating models that are free of biases
Machine learning models are dependent on the data provided. So, if biased data is provided, the data-driven predictions of the machines can be unfair. What you feed is what you get. Machine learning engineers need to be careful to not let their biases creep into their machine learning models. The good news is that research can prevent the application of biased algorithms in real life systems.
 Machine learning engineers wire machines to think for themselves
Machine learning engineers cannot create models that can improve by themselves. At least not with our present level of technology. Machine learning models have to perform very specific tasks. Even while performing those tasks, they follow a set of algorithms. For instance, if a self-driving car hits somebody it can only be due to a fault in the car’s algorithm or other mechanical issues, not due to its wish to inflict pain.
 Machine learning engineers are creating models that will remove humans from the work chain
Sure, machines are replacing people in industries. But, it is only possible for those tasks that need precise machine-like steps. In industrial work which deals with real-world problems, there is always the possibility of complications which might be out of the scope of the system. So, unless the system’s algorithm is updated, it will fail to work in presence of such complications.
 Machine learning engineers are similar to data scientists
Data science is a holistic term. But, there’s something that’s fundamentally different about the way machine learning works. Data scientists explore data and try to find approaches that fit their business requirements. Machine learners, on the other hand, don’t deal with data directly. Because they know the results they want, they let the algorithms do their work for them.
Don’t let these far-flung misconceptions let you form a biased opinion of this booming profession. Make sure you do your research well before deciding in favor of or against a particular career.
Since you are here, read the article on Want to Land A Machine Learning job, do this while you are still in college!