What Is Artificial Intelligence Engineering?
Artificial intelligence (AI) has the potential to bring a revolution to many fields, including engineering. AI for engineering means helping engineers to analyze and optimize complex systems, with the help of Artificial Intelligence and Computer Science.
A recent promising branch of Artificial Intelligence is Machine Learning. Within Machine Learning models, Deep Learning algorithms have proven their values in the last 10 years (2012-2022). They are now out of the academic box, attracting the practical interest of major corporations in the automotive and aerospace global corporations, as well as life sciences, civil and naval engineering companies. To name a few, global leaders such as our collaborators from Bosch and Airbus are active in the implementation of Deep Learning.
What is the reason behind this recent and sustained attention for AI engineering, and how can AI engineers contribute to this blooming interest?
We will show that Artificial Intelligence with specific Machine Learning models can reduce costs and improve the quality of products and services. This is a powerful motivation for industrial corporations since it is by no means possible, in our challenging world, to sustain the existence of a company without making it grow; and sustainable growth can only come from being competitive on the market. On its turn, long-lasting competitiveness cannot be based on such short-sighted practices as reducing prices, but is rather generated by continuous innovation on the product and services.
From an individual perspective, we will also explore what are the benefits for an engineer in joining the AI community. Also, from the point of view of the rest of us engineers who are not AI specialists: will AI help us or will AI replace us?
Examples of Artificial Intelligence Engineering
What are artificial intelligence engineers doing and why are AI engineering jobs sought after?
Here are three examples of tasks that an artificial intelligence (AI) engineer might work on:
- Developing machine learning algorithms. An AI engineer may design and implement machine learning algorithms to enable a system to learn from data and make predictions or decisions without explicit programming.
- Building natural language processing systems. An AI engineer may create systems which can understand and process human language, such as chatbots or language translation software.
- Implementing computer vision systems. An AI engineer may build systems which can analyze and interpret visual data from various sources, ranging from cameras to stored 3D industrial shapes (CAD). We will talk more on this type of application.
In summary, an AI engineer while mostly working on software engineering leverages data science and big data technologies to enable learning and understanding by artificial intelligence agents.
General Applications of Artificial Intelligence and Machine Learning in Engineering
The world is drastically changing; let’s assume you want to be part of this change. Now, technologies like speech recognition, business process management, and image processing are only a few of the Artificial Intelligence technologies changing the world.
Applications of Artificial Intelligence and Machine Learning in Engineering — 4 Cases
Now, to be more specific; let's think how can artificial intelligence engineers help the world in various industries. Here are are four examples on how you would be expected to deliver, directly or via software you developed.
Case 1: Enhance Decision-Making.
An AI engineer can analyze large amounts of data and provide insights that can help making more informed decisions.
Case 2: Help to Optimize Designs.
As an AI engineer or design engineer you can use AI to optimize the design of systems. An example is finding the optimal shape of a mechanical part. Engineers in mechanical engineering seek lightweight components with the slightest possible mechanical stress.
Case 3: Help to Improve Product Quality.
An AI engineer can leverage artificial intelligence to identify patterns and trends in data that can help product design and manufacturing departments to identify and fix problems in a product before its release.
Case 4: Help to Automate and Shorten Routine Tasks in Engineering.
This will be the main focus in the second part of the article. Routine tasks such as data analysis, modelling, and simulation are quite expensive and tedious.
Simulations of car aerodynamics could require hours of manual work and usage of hundreds of processors.
Hence, automation can allow expert engineers to focus on more complex and creative tasks and can allow engineer with less expertise to approach modelling and simulation with confidence.
We will see how artificial intelligence can support engineering tasks by breaking down silos between departments and helping to effectively manage data to glean insights from it.
Artificial Intelligence Engineering Jobs
We will now explore two very different professional profiles: design engineers and machine learning engineers. In the end, we will see how machine learning engineers can produce software of great value for design engineers.
Job Profile: Design Engineer
A design engineer works in the conception or amelioration of an industrial product. Their role is to make it more sustainable and competitive in the market in terms of pricing and performance, while increasing the company profitability.
To do so, engineers work with a variety of materials and technologies; including computer-aided design (CAD) software and computer-aided engineering simulation (CAE) software, in order to create prototypes and final products.
Design engineers may work on a wide range of projects; including transportation vehicles, consumer products, medical devices, or machinery buildings.
One of the main issues is that CAE softwares are very sophisticated and therefore only a fraction of engineers (ca. 10% of them) can effectively access it.
The figure below shows some of the operations involving Design Engineers when dealing with industrial simulations, and their expectations. How could an AI engineer help a Design engineer?
Job Profile: Machine Learning Engineer
Machine Learning engineers are professionals who apply machine learning techniques to design, develop, and deploy machine learning models and systems.
What Do Machine Learning Engineers Do?
Machine Learning engineers work on a variety of projects, ranging from developing machine learning algorithms for self-driving cars to building machine learning-powered recommendation engines for e-commerce websites. We could say that Machine Learning engineers love to write algorithms that assist humans by providing software that equals or exceeds human performance in specific tasks.
What Are the Skills Required From Machine Learning Engineers?
Machine Learning Engineers typically have a strong background in computer science and mathematics, as well as experience with programming languages such as Python and machine learning frameworks such as TensorFlow.
Some of the typical daily activities and underlying technical skills of Machine Learning engineers can be:
- perform statistical analyses
- implement machine learning models with robust knowledge of machine learning algorithms
- understanding of the final users' business processes
- ability to work in a data science team
Therefore, a Machine Learning Engineer should have a solid understanding of artificial intelligence and computer science concepts and algorithms, and be able to apply them to real-world problems collaborating with other teams such as Design Engineers.
The figure below shows some of the operations involving Machine Learning and CAE Expert Engineers when dealing with industrial simulations, and their skills. We are now getting closer to the solution of the issue raised in the previous section - How could an AI engineer help a Design engineer?
Bringing Design Engineers & Machine Learning Engineers together
The point of contact between Design Engineers and an Artificial Intelligence engineer is a software platform such as NCS.
If you look into the technical specifications of NCShape Expert, it covers artificial intelligence tools and data science capabilities for any AI engineer and data scientists.
NCS Expert for AI Engineering: Total Control
Low-level Python-based interface enables any AI engineer to interact with the core technology and removes any limitations.
NCS Expert for AI Engineering: Best Practices
A fully guided workflow helping any AI engineer to start with the best practices right away.
NCS Expert for AI Engineering: Unique Algorithms
Generative neural networks optimized and ready to work, enabling an AI engineer to become her/his "Company Hero".
The figure below illustrates how AI Engineering and Design Engineers are not siloed anymore but rather sitting side by side; with data scientists and senior simulation experts providing added value to design engineers.
AI Engineer & Design Engineer: Success Stories
An AI engineer or simulation expert could of course work disconnected from her/his colleagues or customers in the design departments.
However, the most exciting and successful stories are the ones including the production of user-tailored bespoke solutions, reachable by all non AI experts at the click of a button. Such is the power of the NCS platform.
The figure below illustrates what was until a few years ago simply a dream: getting simulations for electromagnetic, stress, thermal or fluid fields within the time to click a button (in a fraction of a second).
This is the intuitive expectation by Design Engineers, and this is how an AI engineer can contribute to make it happen.
NCS Expert users have the ability to generate packaged operational applications that are exploited in NCS Production.
About Neural Concept
Located in the shores of Lake Geneva within EPFL (Lausanne Polytechnic) Innovation Park, Neural Concept is a Swiss Company founded in 2018. Pierre Baqué, founder and CEO, collaborated closely with EPFL Computer Vision Laboratory where he worked as a researcher and conceived the revolutionary idea of coupling simulation and AI via computer vision techniques.
Neural Concept is now thriving with several industrial customer projects successfully brought to production, thanks to a team fully committed to its vision; which is to revolutionize the future of industrial engineering and do it with intelligence.
.jpg)
