What is Artificial Intelligence Engineering?

Artificial Intelligence Engineering

We are living at a time when the gadgets that we just used to imagine in the past have come to life. Driverless cars, voice assistants, drones, and many other smart devices of present times were only a part of science fiction movies. You may be wondering what technology is powering these fascinating applications. Well, it’s none other than Artificial Intelligence (AI) and its most important subset, machine learning. Today, technology companies are taking up the charge and pushing the boundaries of automation and social media. You can already see a number of opportunities to tap into the field of AI that will create societal value.

Though AI has been dominating the headlines of technology news, there are hardly a few people who actually understand artificial intelligence engineering. At the same time, some professionals are trying to take the first step by enrolling in AI for beginners online courses to grasp the basics of this interesting field. This is highly appreciated as we now know that the future belongs to AI, and its popularity isn’t fading away any time soon. So, the earlier you learn the Ai terminology, the stronger you can stay afloat in the dynamic job market. Let us understand what artificial intelligence engineering is all about and how you can start a career in this domain.

Artificial Intelligence Engineering Explained

AI engineering, or artificial intelligence engineering, is a field of research and practice that aims to build AI systems as per human needs for mission outcomes. It basically involves the principles of software engineering, systems engineering, computer science, and human-centered design. AI engineering focuses on creating smart tools and machines that can improve a society’s standard of living. You may have heard of various engineering disciplines like electrical engineering, mechanical engineering, software engineering, civil engineering, and so on. On the other hand, AI engineering is a relatively new discipline that is essential to build, manage, and analyze AI functionalities effectively.

When you start approaching Ai engineering, you will come across its three pillars, namely:

  • Human-centric AI
  • Scalable AI
  • Robust AI

Human-centric AI

As per Cognizant, human-centric AI is defined by the computer systems that are continuously improving due to human input while offering an effective experience between humans and machines. It focuses on learning from human input and collaboration using algorithms that exist in a larger human-based system. Companies that offer human-centered AI solutions make use of human science and qualitatively thick data to comprehend the aspirations, deeper needs, and drivers that underlie customer behavior in the market. This enables clients to make more informed business decisions and develop clear strategies and solutions to their problems.

Scalable AI

Scalable AI refers to the ability of AI algorithms, models, data, and infrastructure to operate at a certain level of size, speed, and complexity as required for a project. Scalability is a crucial factor for many engineering disciplines and so to realize the capabilities of operational AI. in other words, the more scalable an AI solution is, the better it is able to grow and handle an increasing workload, data, or additional processes. Businesses that use AI need to ensure that their AI systems are scalable so that when the datasets grow, the systems’ performance is not affected, and the customers get the usual desired experience.

Robust AI

Robust and secure AI systems is a feature that relates to the operation of AI systems at the desired level of performance even when there is uncertainty or the presence of any danger or threat. An AI system is said to be robust when it is capable of either side-lining unwarranted responses to unexpected triggers in its environment or developing an alternative response to deal with the new situation. Security is another aspect of this discipline; it means the AI systems have built-in mechanisms or structures to avoid or prevent dangers from a given threat model. Usually, a reliable model of an AI system is first developed and mathematically proven to be declared robust. 

Is AI Engineering Right for You?

We recommend that you just don’t go with the hype surrounding AI and select the AI engineering discipline for your career. First, you need to understand your career interests and assess whether they align with AI engineering or not. If you are then determined to learn Ai, then you have a number of options to step into the field. However, if you are quite new to AI, then we recommend taking an AI basics course to get familiar with the technology and terms related to it.

Simplilearn, a leading online Bootcamp and certification training provider, offers a free Introduction to Artificial Intelligence course for free as part of its SkillUp initiative. The training program will give you a complete overview of the Ai concepts and workflows, including the basics of machine learning and deep learning. Some of the top skills covered in the program are supervised learning, unsupervised learning, the purpose of AI, machine learning workflow, and more. Upon enrolling, you can access the course for 90 days. Moreover, you will get a course completion certificate at the end.

So, what are you waiting for? Pave your way towards a high-paying career by selecting the AI engineering discipline.

Shashank is an experienced tech blogger. He has completed his Master in Business Administration but his heart lies in technology & Gadgets. He is basically Creative in Nature and his one of favorite thing is Capturing Nature closely whenever he gets the free time.

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