How to Implement AI in Engineering?


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How to implement AI in engineering?

Engineers are increasingly striving to integrate AI into projects and applications while also climbing their own AI learning curve. To tackle AI, engineers should first grasp what it is and how it fits into their present process, which may not be as simple as it appears. A simple Google search for “What is AI?” produces millions of results with varied degrees of technical and relevant information.

So, what exactly is AI to engineers?

The majority of the attention on AI is focused on the AI model, which motivates engineers to swiftly dig into the modeling component of AI. After a few introductory projects, engineers realize that AI is more than just modeling; it is a whole set of stages that involves data preparation, modeling, simulation and testing, and deployment.

ai-driven-workflowThe four steps that engineers should take to create a comprehensive, AI-driven process (MathWorks)

Most of the time, AI is only a small part of a bigger system, and it must perform correctly in all scenarios with other end-product components such as sensors and algorithms such as control, signal processing, and sensor fusion. Engineers in these settings frequently have the abilities necessary to successfully incorporate AI into their product. They are already familiar with the problem, and with tools for data preparation and model construction, they can get started even if they are not AI experts, allowing them to leverage their existing areas of expertise.

The AI-driven total process consists of four parts, each of which plays a vital role in successfully adopting AI into a project.

Step 1: Data Preparation

The most crucial phase in the AI workflow is arguably data preparation. Projects are more likely to fail if robust and reliable data is not used to train a model. If an engineer feeds the model “poor” data, they will not get insightful results and will most likely spend many hours figuring out why the model isn’t working.

To train a model, you should start with as much clean, tagged data as possible. This could be one of the most time-consuming procedures in the process. When deep learning models fail to perform as predicted, many people focus on how to improve the model by adjusting parameters, fine-tuning the model, and running many training iterations. Engineers would be better served, however, focusing on the input data: pre-processing and labeling the data being fed into a model to ensure that the model can learn from the data.

Automatic labeling and integration are now possible and necessary for completing the first stage efficiently. We created MATLAB tools to quickly clean and label data for use in machine learning models, resulting in more promising insights from field machinery. The technique is scalable and allows individuals to apply their topic expertise without needing to become AI experts.

Step 2: AI Modelling

After the data has been cleaned and appropriately labeled, the workflow moves on to the modelling stage, where data is utilized as input and the model learns from it. The goal of a successful modeling stage is to construct a robust, accurate model capable of making intelligent judgments based on the data – and, crucially, on previously unseen data. Deep learning (neural networks), machine learning (SVM, decision trees, etc.), or a combination of the two can be used in AI models as engineers seek the most accurate, resilient output. Deep learning is a kind of machine learning that trains computers to do what people do instinctively: learn from experience. Machine learning algorithms employ computer methods to “learn” information directly from data, rather than using a preconceived equation as a model. Deep learning is a subset of machine learning that employs neural networks, which have a layered structure. They can be extremely powerful, but they sometimes necessitate massive volumes of data. The decision between machine learning and deep learning is influenced by the data and the challenge at hand.

At this level, regardless of whether you use deep learning (neural networks) or machine learning models (SVM, decision trees, etc.), it’s critical to have access to the numerous AI workflow techniques, such as classification, prediction, and regression. As a starting point or for comparison, you might potentially use a selection of prebuilt models created by the larger community. Starting with existing similar models can greatly jump-start the job. Deep learning is particularly well adapted to image identification, which is vital for solving problems like facial recognition, motion detection, and many advanced driver assistance technologies including autonomous driving, lane detection, pedestrian detection, and autonomous parking.

AI modeling is an iterative stage in the whole workflow, and engineers must keep track of the modifications they make to the model at all times. Tracking changes and recording training iterations is critical since it allows the engineer to explain the factors that result in the most accurate model and reliable outcomes.


Step 3: Simulation and Test

AI models live within a broader system and must interact with all other components. Consider the following scenario: You begin with a perception system for identifying objects (pedestrians, cars, stop signs), but this must also interface with other systems for localization, path planning, controls, and so on. Before deploying a model into the real world, it is critical to validate that the AI model is working properly and that everything works well with other systems.

Trust is earned after you have successfully simulated and tested all of the scenarios you expect the model to encounter and can verify that the model works as expected. We designed Simulink tools to allow engineers to validate that the model works as expected for all predicted use scenarios, avoiding costly redesigns in both money and effort.

Step 4: Deployment

When you are ready to deploy, the following step is to prepare the model in the final language in which it will be implemented. This stage often necessitates design engineers sharing an implementation-ready model that allows them to fit the model into the selected hardware environment.

Engineers will set themselves up for success if they follow these four steps. Using the tools and resources available to them can assist them in navigating what can be a frightening environment.

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