From Software Engineer to Artificial Intelligence: Transform Your Programming Career
It’s 2020, and fears about how the emergence of AI will affect job security are disrupting the minds of many.
Machines now can learn new things and perform cognitive tasks that were once possible only with the human brain. AI is already changing the world and spreading throughout civilization.
But we’re just getting started. AI technology is growing more sophisticated every day. From autonomous vehicles to predicting hurricanes, the adoption of artificial intelligence is accelerating across major industries.
Careers in the field of AI will be significant in the future. They already are.
Techies with AI skills will take on tasks that no other professionals will be able to perform. This will no doubt create a threat for individuals with routine skills in industries like manufacturing and automotive.
Artificial intelligence will create plenty of job opportunities, mostly on the data science industry’s technical engineering front.
According to the World Economic Forum, there will be about 130 million new AI jobs by 2022.
Your tech skills will dominate the jobs of tomorrow, yet there's an unfilled space. Look closely, there is still an imbalance between the demand and supply.
As the labor market moves rapidly, identifying newer AI jobs and gaining skills will help aspiring techies keep pace.
Why are Tech Workers Turning to AI?
“AI is stealing our jobs.”
A phrase that sends a shiver down one’s spine.
AI and robots aren’t here to steal your jobs. Although the world is shaken by it, rapid advancements in artificial intelligence are here to help the current workforce. Today’s jobs will require new tools and technologies as they become more complex. The rise of AI and automation is here to improve our daily lives.
Having said that, tech workers and software engineers are becoming concerned about problems AI might cause in the future.
Artificial intelligence is poised to take over nearly 1.8 million jobs by the end of 2020.
By 2037, these jobs are likely to become redundant and replaced by automation:
- Sports referee replaced by video technology
- Newspaper delivery boy replaced by electronic reading devices
- Taxi dispatchers replaced by mobile applications
- Cashiers replaced by self-checkout machines
- Telemarketers replaced by automated robots
- Travel agents replaced by AI-powered chatbots
- Journalist replaced by AI software
- Assembly line workers replaced by automated robots
Does your profession fall under any of these categories?
I hope not.
Most jobs will be affected by AI, but only such that a partnership is formed between a machine and a human. This will mean huge job offerings for professionals like artificial intelligence engineers.
What Does Moving From Software Engineering to AI Look Like?
For many, the transition is already happening.
Software engineers are already required to stay up to date with the latest tools, frameworks, and technologies. No doubt, they have the zeal to keep learning newer job skills, making it much easier for them to make a career shift.
Here’s what you need to do first:
- Acquire extensive knowledge of the current technology trends.
- Gain a solid understanding of the theory.
- Master the art of framing problems in a non-deterministic manner.
The following skills will be a huge benefit to developers and programmers looking to get into an AI career.
Machine learning makes the computer smarter. Ever wondered what lies behind the mechanism that triggers the next movie for you to watch on Netflix? Or perhaps behind Spotify recommending songs for your playlist? Well, that’s machine learning!
In all these processes, these platforms are collecting as much data as possible through the kind of movies you’re watching or the music genre you prefer listening to. These steps are just the basics of what is out there.
Do you have an idea of what takes place when you’re training a neural network? What are the major components that allow things to work and others not to?
An ideal way to get a solid understanding is by first learning about machine learning before delving deeper into the theory. You should know:
- How the loss of function works.
- How to build functional models and be able to communicate the findings efficiently.
- The benefits of backpropagation.
A software engineer considers user needs to develop and design new applications. It is the process of analyzing through designing, constructing, and testing the applications, with the help of programming languages.
Learning new skills in machine learning will augment software engineering skills such as:
- Creating code that can be reused to accelerate the speed of the experiment you’re working on.
- Testing various functions of the pipeline such as data preprocessing, input or output sanitization and augmentation, along with the model interference timeline.
- Providing back-up (checkpointing) for models at different levels of training.
- Setting up a distributed infrastructure to keep a track of a hyperparameter search, to run training, and to find out inference in a more systematic manner.
Statistics is the fundamental skill that is needed by all tech professionals moving into the AI domain. Without statistics, it can be challenging getting into the AI field.
You should have:
- A solid understanding of overfitting and underfitting. Overfitting takes place when a statistical model can capture the noise of the data. Likewise, underfitting is when the machine learning model or statistical model is unable to detect or capture data. Knowing overfitting and underfitting will help you analyze upcoming predictions.
- The ability and the level of confidence to provide the right attribution to the results obtained from your model.
- Knowledge of various ways of determining and measuring model success. E.g. the area under ROC curve, recall, and precision, etc. also looking at the evaluation metric biases based on the outputs of your model.
90% of your time is spent on data munging, just ask any data scientist. This skill is equally important for a software engineer looking to become an artificial intelligence engineer. For the succession rate of your model to increase, you need to know whether your model correlates with the quality or quantity of your data.
The data tasks fall under categories such as:
- Data pre-processing (data augmentation, missing data amputation, and cross-validation split) and data post-processing (cleaning artifacts and taking care of special outliers).
- Finding data sources that are reliable and can accurately gauge the quality of the data.
Debugging and Tuning Models
In traditional software development, a bug generally leads to the program crashing. This may be annoying for the user, but is important for the developer.
When a program fails, the developer can easily check for errors and debug, solve the problem, and even find out the reason why.
However, when the machine learning model encounters the same kind of situation, it makes a ruckus, often because there is no clear reason why the program crashed.
Though predictions can be made manually, most often the machine learning model tends to fail due to poor output predictions. When such a program fails, the artificial intelligence engineer needs to inspect for errors, understand the biases, and debug the program.
Thus, it is important to find the right parameters and the right architecture to test various configurations.
You can start by creating a simple model. Statistical models such as linear regression and nearest-neighbors will often give you an 80% faster time in implementing the model.
Later, if you decide to train on more complex models, you will need to start by training the model to overfit for a small sub-section of the dataset.
Learn AI to Transform Your Software Engineering Career
The AI trajectory in 2020 has lots of potential benefits for software engineers seeking a career shift.
“Although software engineering is not yet a true engineering discipline, it has the potential to become one,” says Mary Shaw, American software engineer and a chief scientist at Carnegie-Mellon University’s Software Engineering Institute … from 1992 to 1999. Look how quickly that has changed!
The excitement of AI careers isn’t going to fade. The moment is coming when machines will be thought of as “intelligent” (if we haven't passed it already!)