Severe loss of jobs and furloughs amid COVID-19!
Perhaps, this phrase has become a day-to-day headline in every news and articles we come across. It is funny how things were different a few weeks back, and how quickly our routine has changed. The world is still under shock, we’re currently facing what the world calls, one of the worst economic downturn ever since the pandemic attack.
However, with economists and researchers predicting over 40 percent of jobs in the UK will be automated by 2030, we still have a technology domain that is set to boom – AI and automation. During the past three years, AI job opportunities accelerated by nearly 500 percent, based on a report by Indeed. If we look at the present scenario, jobs are in abundance but the talent is scarce. No wonder companies are fighting to grab the best AI talent for their firm.
Despite the COVID-19 crisis, technologies such as AI, machine learning, and big data have started contributing to fight against the pandemic. COVID-19 is the first global public health crisis of the 21st century. Presently, AI tools and technologies such as CLEW and AliveCor are being used to detect signals that are preventing cardiac arrests and respiratory disruption. Today, multiple AI-powered projects that are based on machine learning, big data, and data science are brought to use across multiple fields to predict, evaluate, and explain different situations caused by the pandemic.
Therefore, if you aim to switch careers and become an AI engineer, then this could not have been a better opportunity for you to start over, learn new skills, polish the old ones if required, and move ahead of the curve.
Prerequisite skills you must adapt if you’re looking for an AI career switch
Grabbing a job in the AI field is extremely challenging unless you don’t move along and take the right path. As a result, tech professionals are already taking up AI certification programs to gain knowledge and explore more about the AI field.
- Probability and statistics
If you’re a tech professional from the software development field, you need to have a solid knowledge of statistics, probability, and knowledge about AI models like Naïve Bayes, Gaussian Mixture Models, and Hidden Markov. These theories can further help them learn more about complex algorithms.
Statistics is a stepping stone for aspiring AI professionals.
- Programming languages such as Python, R, and Java
An AI engineer needs to be proficient in programming languages like R, Python, C++, and Java. It gets easier to create complex algorithms with the help of the Python programming language. However, to speed up the entire coding process, the individual needs to be skilled in C++.
Whereas R is used in plots and statistics and Java for implementing reducers and mappers. One of the most ideal ways to master these languages is by ensuring you have explored multiple open source libraries.
Knowledge in Spark, WEKA, and RapidMiner is ideal for Java professionals looking to enter the AI field.
- Good command over Unix tools
Since most of the processing in AI takes place on Linux-based machines, budding AI professionals need to have a solid grasp over different Unix tools like grep, cat, find, cut, tr, sort, and awk, learn their functions and ways how to use them.
- Knowledge of distributed computing
Most of the AI jobs require AI professionals to deal with a large number of datasets, the ones they cannot process with a single machine. Thus, this data set should be equally distributed across the cluster making it crucial for AI engineers to know distributed computing.
- Advanced signal processing techniques
Since machine learning has feature extraction to be their core integral aspect, budding engineers need to familiarize themselves with solving complex problems with the help of signal processing algorithms like wavelets, curvelets, and contourlets.