AI Engineering vs AI Application Engineering
What is the difference, and why does it matter?
ChatGPT has taken the world by storm. It has woken up many, including myself, to the reality that AI has reached an inflection point, and will rapidly become an integral part of our daily lives.
Although AI has been around for a few decades by now, it has only begun to emerge from the shadows during the past few years. With AI technologies being applied more and more in real world applications, the field of AI Application Engineering is still the Wild West in terms of conventions and terminologies. In this article, I explain the difference between "AI Engineering" and "AI Application Engineering". They are both related to AI, but have a different area of focus.
Now that we are at this inflection point, we can expect the domain of AI Application Engineering to rapidly gain in prominence. To avoid confusion, we need to get our terminology straight.
Science vs Engineering
Before going further, let's first take a step back and look at the fundamentals.
Science is the systematic study of the natural world through observation and experimentation. It is a method of acquiring knowledge through the use of a very systematic process and culture, which we call the "scientific method". This method involves making observations, formulating hypotheses, testing predictions through experiments, and developing theories that can explain the results. Science also involves the development of technologies and techniques to improve the ability to observe, measure, and analyze.
Engineering is the application of scientific, mathematical, and practical knowledge in order to design, build, and maintain complex systems and structures. Engineers use their understanding of the physical and natural world, as well as the principles of mathematics and science, to create new products, systems, and processes that meet specific requirements and solve real-world problems. Engineers apply their knowledge of physics, mathematics, and materials science to design, build, test, and improve structures, machines, and systems. They also use their knowledge of computer science, software development, and electronics to create and improve software, hardware, and other technology-based systems.
Data Science vs AI
Data Science and AI are related fields, but they are not the same thing.
Data Science is a multidisciplinary field that involves the extraction of insights and knowledge from data using techniques from statistics, mathematics, and computer science. It includes the process of collecting, cleaning, and preprocessing data, as well as the use of machine learning and statistical models to analyze and understand the data. Data scientists use various tools such as Python, R, SQL and visualization tools like Tableau, PowerBI etc. to manipulate and analyze data.
AI, on the other hand, is the simulation of human intelligence in machines that are programmed to think and learn like humans. It involves the development of algorithms and models that enable machines to perform tasks that would typically require human intelligence, such as speech recognition, natural language processing, and decision-making. AI draws on multiple fields like computer science, mathematics, linguistics, psychology, and philosophy. It encompasses multiple subfields such as machine learning, natural language processing, computer vision, and robotics.
Data Scientist
A data scientist utilises algorithms, math, statistics, design, engineering, communication, and management skills to derive meaningful and actionable insights from large amounts of data and create a positive business impact. Data scientists extensively use statistical methods, distributed architecture, visualisation tools, and diverse data-oriented technologies like Hadoop, Spark, Python, SQL, R to glean insights from data. The information extracted by data scientists is used to guide various business processes, analyse user metrics, predict potential business risks, assess market trends, and make better decisions to reach organisational goals.
AI Engineer vs AI Application Engineer
We now come to the main topic of this article: what is the difference between an "AI Engineer" and an "AI Application Engineer"? The difference is primarily in their focus and responsibilities.
An AI Engineer is focused on the more general aspects of artificial intelligence, such as researching, designing, and developing AI-based systems and algorithms. They may be involved in the development of AI-based platforms, toolkits, and frameworks that can be used by other developers to build AI applications. They are more focused on the theoretical and technical aspects of AI and may conduct research on new AI techniques and algorithms. They need to have a strong understanding of AI concepts and techniques, and be familiar with the latest research in the field.
An AI Application Engineer is focused on designing, developing, and maintaining AI-powered systems and applications for specific domains and industries. They are responsible for implementing, testing, and deploying AI models and algorithms to solve real-world problems. They may also be responsible for integrating AI systems with existing software and hardware, as well as troubleshooting and maintaining them. They need to have a strong understanding of the industry or domain they are working in, as well as the ability to communicate effectively with both technical and non-technical stakeholders.
Both roles involve the application of AI techniques and technologies to solve real-world problems, but AI Engineers tend to focus more on the development of the underlying AI technology and systems, while AI Application Engineers tend to focus more on the implementation and deployment of AI-based solutions to specific industries and domains.
Why does this matter?
With the recent release of ChatGPT, Industry 4.0 has reached an inflection point.
Just like it took both scientists and engineers to introduce electricity to our daily lives, it will take both scientists and engineers to introduce AI. Scientists researched and experimented with various theories and principles to acquire a deep understanding of electricity. However, it took engineers to take this scientific knowledge and apply it to the development, implementation, and installation of practical applications, such as power generators and electric motors, that we now use in our daily lives.
Similarly, Data Scientists and AI Engineers have been researching and experimenting with various algorithms and technologies related to AI for decades. They have developed a deep understanding of the underlying theories and principles of AI, including machine learning, natural language processing, and computer vision, which resulted in the amazing technology behind ChatGPT. However, it will take AI Application Engineers to take this scientific knowledge and apply ChatGPT to the development of practical applications that will affect our daily lives.
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