Prashant Pansare

What makes you a ‘Terminator’ of Artificial Intelligence

Blog Post created by Prashant Pansare on Mar 17, 2018

The march of the machines is on, and while we worry about robots and machines replacing our jobs, the truth is that these are likely to create new job roles. From a nascent idea discovered almost six decades ago, Artificial Intelligence today has become an actual product…one that holds the promise of changing things as we know it. As our lives mimic science fiction movies that we have grown up consuming, AI engineering is an exciting new field with the fastest growing demand for qualified professionals.

A Pew research on The Future of Jobs highlights how AI will be a part of our daily lives by 2025. While this might displace many jobs, the fact is that it will create many others…however, only the ones with the right skills will be able to leverage this fast-growing and lucrative career opportunity. So what skills does it take to become a killer Artificial Intelligence Engineer?

According to IEEE, just about every industry needs employees with AI skills as they endeavor to give computers the capability to think, learn, and adapt. Geoff Gordon, acting head of the Machine Learning Department at Carnegie Mellon University, in Pittsburgh, states,

If you look hard enough, any industry you can think of has a need for AI and machine learning”.

AI engineers are required to research and discover improvements to Machine Learning algorithms, build systems and infrastructures to apply machine learning capabilities to given data input sets, data mining and analysis, or to build machine learning applications.

To begin with, a software engineering background is a must-have to land an AI job. @##That apart here’s a list of skills that I would look for in a top-notch AI Engineer.

Programming knowledge and computer science skills

Some of the programming languages that are hot in the AI landscape are Python, C++, R, and Java. Different languages are used for different coding purposes. For example, R is a great language for statistics as well as plots, C++ helps in accelerating coding, etc. Languages like Lisp and Prolog are also suited to solve certain AI problems. Additionally, the capability to perform GPU programming and parallel processing are also important skills to gain. The use of these languages depends on the applications and performance demands. This apart, aspiring AI engineers should also have a firm grasp of algorithms, data structures, computability and complexity and computer architecture.

Statistical learning

Given that AI is language agnostic, having a strong foundation in statistics is essential for AI engineers to build and validate models from observed data. In a number of cases, Machine Learning algorithms are extensions of statistical modeling procedures. Hence, it becomes essential to have the knowledge of theories about algorithms such as Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models, etc. that are at the heart of most Machine Learning algorithms. Being proficient in these as well as gaining proficiency in statistical areas of measure theory such as mean, median, mode, distributions, and analysis methods goes a long way to use statistics as a metric of model evaluation such as p-values, receiver-operator curves, confusion matrices etc.

Data Modelling and data evaluation skills

AI and machine learning involve the analysis of complex and often unstructured data. The right analysis of these vast data sets relies heavily on the science of data modeling, understanding the underlying structure of the data set, and also finding patterns such as correlations, clusters, eigenvectors, etc., predicting properties of unseen instances, and filling in the data gaps. Clearly, having a deep understanding of data modeling and evaluation concepts become critical to creating sound and robust algorithms that lend themselves to evolution. AI engineers also have to continuously evaluate the quality of a given data model, choose the right accuracy and error measure, and a strong evaluation strategy to ensure that iterative algorithms do not utilize errors to change the data model.

Software Engineering and System Design

AI and Machine Learning engineers need to be proficient in Software Engineering and System Design. Why? Because the quintessential output of such an engineer is a piece of software that is a small and yet very important part of a very large ecosystem. Therefore, it is important that AI engineers have the capability to understand how these different components work together, have the ability to communicate with them and consequently, build the right interfaces for the component that others will be depending on. Having a sound knowledge of software engineering and system design as well as an in-depth knowledge of software engineering best practices ensure that these algorithms are able to scale appropriately as the data volumes increase and avoid any bottlenecks.

Distributed Computing

AI jobs deal with working with very large datasets. This data cannot be processed using a single machine and needs to be distributed across an entire cluster. Mastering the distributed computing landscape and gaining proficiency on Unix tools such as cat, grep, find, awk, sed, sort, cut, tr, etc. helps since the processing is usually done on Linux based machines. It pays to learn these functions, to understand how to apply them, and then apply them well.

Conceptual thinking

The technical skills aside, AI engineering demands a complete change in conceptual thinking. Since AI programs are designed to learn and self-improve over a period of time while tapping into large dataset repositories, gaining an understanding of how a product is used and how it can be used better is a necessity. Without a shift in conceptual thinking AI engineers will be unable to utilize AI to its optimal potential. For this, they also need a deep understanding of how AI algorithms make decisions. Having a ‘black box’ thinking approach will not help in creating algorithms that can grow.

Be a sponge

AI is a growing field. There’s some change or the other in this area almost every day. Hence, those working in AI and aspiring AI engineers should make learning a continuous and constant part of their everyday existence and subscribe to several scientific publications to stay on top of AI news. They need to stay up to date on research and look for absorbing as many AI experiences and opportunities as they can.

Clearly, knowing how to code is a given when trying to become an AI engineer. However, an innately curious mind, love for AI technology, and the skill to explore the seemingly limitless applicability of AI are things that help you earn brownie points. The fact is, that the world as we know it is changing dramatically. This change is going to fuel the need for more and better AI engineers. After all, as the world’s problems become more complex, you need complex systems to solve them. And AI engineers are the ones who will show the way.

 

 

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