Artificial intelligence is a disruptive technology that is enabling technological revolutions in its initial stages. In simpler terms, AI is any behavior or activity portrayed by machines and systems. From a computing perspective, artificial intelligence imitates human behavior by analyzing past data. AI has self-learning attributes which means whenever an AI-powered machine receives new information, it can make corrections on its own to make sure it doesn’t repeat old errors. This property allows AI systems to perform tasks that require human intelligence and human understanding like visual perception, speech recognition, decision making, etc.
According to research done by Gartner, artificial intelligence will create a business value of $3.9 trillion by 2022. It also suggests that artificial intelligence will continue to be the most disruptive technology for another decade, thanks to its use cases in leveraging computer power, capacity, speed, and data diversity. This growth results in an explosive amount of demand for the right talent in this field and its many disciplines, including artificial intelligence engineering.
What Is Artificial Intelligence?
In theory, artificial intelligence engineering is the usage of algorithms, computer programming, neural network, and other technologies to develop AI techniques and applications. These techniques have use cases in commerce, science, and other domains. Therefore, an artificial intelligence engineer should be capable of extracting data efficiently from a variety of sources, algorithms, and machine learning models to create AI-backed applications capable of performing complex tasks.
Can You Be An AI Engineer?
Apart from these certifications and degrees, these employers look for the following technical skills:
To work on algorithms, machine learning, neural networks, and other AI-backed technologies, an AI engineer must be good at programming and have a good understanding of the software development lifecycle, coding techniques, and new techniques.
Programming languages such as Python, R, Java, and C++ are important for the construction and implementation of AI models. Having a thorough knowledge of at least one of the major programming languages and tools is a bonus.
Mathematical Skills like linear algebra, probability, and statistics help AI engineers to understand the functioning of various AI models like Hidden Markov, Naive Bayes, Gaussian mixture models, etc.
In a business setting, artificial intelligence engineers need to understand how a machine learning system can adapt to the changing dynamics of the business. An AI engineer must be quick to decide when a machine learning model is ready for business use and monitor its performance accurately to judge if there needs to be any alterations. Like any other profession, having basic knowledge beyond the domain will help in scaling up the career. For example, knowing how the core business runs, who the target audience is, what is the market condition like, etc. will help an AI engineer put his knowledge to the best use.
Communication and the ability to collaborate with a team are something that hiring managers look closely for. The ability to think creatively, practically, and objectively will help an AI engineer to solve business problems quickly. And what’s the point of the AI engineer not being unable to communicate his/her ideas to the rest of the organization and stakeholders? Therefore, having communication and presentation skills is a must.