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?

Like any profession, AI engineers need to cover some basic requirements. Obviously, a bachelor’s degree is an essential first step to become a successful AI engineer. Having a good grasp on foundation subjects like computer science, mathematics, information technology, statistics, finance, and economics. After covering the basics, aspiring AI engineers must focus on specialized subjects like data science, machine learning, and NLP (natural language processing). These subjects are usually available as certificate programs at universities, coding schools, and other institutions. Having industry certifications for machine learning, deep learning, computer programming languages like Python, Django, JavaScript, and data science is a bonus.

Apart from these certifications and degrees, these employers look for the following technical skills:

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.

Business Skills

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.

Soft Skills

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.

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STUTTGART, Germany — NATO and its member nations have formally agreed upon how the alliance should target and coordinate investments in emerging and disruptive technology, or EDT, with plans to release artificial intelligence and data strategies by the summer of 2021.

In recent years the alliance has publicly declared its need to focus on so-called EDTs, and identified seven science and technology areas that are of direct interest. Now, the NATO enterprise and representatives of its 30 member states have endorsed a strategy that shows how the alliance can both foster these technologies — through stronger relationships with innovation hubs and specific funding mechanisms — and protect EDT investments from outside influence and export issues.

NATO will eventually develop individual strategies for each of the seven science and technology areas — artificial intelligence, data and computing, autonomy, quantum-enabled technologies, biotechnology, hypersonic technology, and space. But for the near future, the priority is AI and data, said David van Weel, NATO’s assistant secretary general for emerging security challenges.

The alliance expects to release an artificial intelligence strategy by this summer, he told Defense News in an exclusive March 4 interview. This effort comes on the heels of the U.S. Congress backing the creation of a national AI strategy in January as part of the country’s annual defense authorization bill.

NATO would do well to have its own AI and data policy strategies in place, van Weel said. He expects the strategies to include NATO’s plans for setting interoperability and technology standards across all EDT domains, and provide guidance on both principles for responsible use of AI-enabled platforms and export control mechanisms.

“It’s basically enabling the organization to then be able to start incorporating AI into military requirements for NATO itself, but also for our allies,” van Weel said. “Data and AI are the first [EDTs] that we will pick up with speed and we’ll deliver on this year.”

The EDT implementation strategy was endorsed during the alliance’s annual meeting of defense ministers in February, and followed the establishment of an EDT road map during the 2019 alliance summit in London. The defense leaders of NATO’s members along with their counterparts in Sweden, Finland and the European Union met virtually for the 2021 ministerial.

The overarching goal of the strategy was to create the conditions for continued interoperability across the alliance as it tackles “a whole new field” of technologies on the horizon. “One of the big challenges when we go into this new phase of disruptive technologies is how do you keep all allies on the same hymn sheet when it comes down to communicating with each other, using the same technology, being interoperable,” van Weel said. “So that’s a big part [of the strategy] and a big role for NATO to play.”

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