Low-code AI for mining engineering

By Stephane Marouani, Country Manager – Australia and New Zealand, Mathworks

As artificial intelligence (AI) continues to expand its capabilities and is used to improve existing systems, engineers are increasingly incorporating it into their day-to-day jobs. Although engineers have a range of expertise and familiarity with AI tools, the mining industry needs a solution to help broaden the AI community of users, and ensure speed and efficiency across projects. This is the key driving force behind the rise in popularity of low-code solutions for AI among engineers.

Not only can low-code artificial intelligence (AI) help shorten production cycles and lower project costs, but it can also bridge the gap between the latest work of data scientists and engineers on the ground. As more engineers are using AI, low code will become a natural extension of any AI-driven work – making interdisciplinary teams more productive and effective. But in order to fully benefit from a low-code approach, it is important that mining engineers understand the primary applications, best practices and future trends associated with low-code AI.

Primary applications and benefits 

Low code has an application anywhere a data-centric approach is required – from industrial automation to radar and wireless. As such, its applications stretch throughout the entirety of the AI workflow, covering data preprocessing, modelling, simulation, testing and deployment.

For instance, data labelling is an area that inherently lends itself to a graphical point-and-click approach. As engineers work to ensure that clean training data is used as input to models, point-and-click tools can work to remove noise from applications, such as radar, wireless and signal. For example, DRASS, a marine manufacturing company, used low-code image processing applications to provide clean datasets to train its autonomous surveillance systems in a challenging maritime environment. Using point-and-click tools within MATLAB, DRASS could standardise and manage wave motion and persistent background changes, allowing the company to complete its project within 12 months.

This interactive approach has two primary benefits. First, the ease of use inherent in solutions like point and click allows for interdisciplinary teams with different levels of coding ability to work together. As the playing field is levelled, engineers with varying levels of coding skills can collaborate to tackle the primary issue that their team is facing.

This can be of particular use when monitoring multi-component systems in specific applications. Application-specific, low-code tools provide multifunction graphical environments for intuitive interaction with multi-component projects. For instance, apps such as Diagnostic Feature Designer provide a graphical interface for predictive maintenance scenarios, allowing mining engineers to evaluate potential condition indicators to understand a machine’s health state. 

Secondly, with low-code solutions, engineers may be able to run variations of algorithms and quickly view trade-offs through point-and-click interfaces. When multiple iterations are necessary, as is often the case when working with AI, saving time on writing code across multiple tests can be vital. An example of this is PathPartner, an embedded systems company creating autonomous vehicles, who used machine learning (ML) approaches for radar-based automotive applications to cut development times. Implementing the point-and-click Classification Learner app to compare the efficacy of different classification algorithms in parallel, the firm was able to cut spending and development time from five months to only one month.

Low code at the forefront of innovation 

Low-code solutions also serve to help incorporate leading technology and models produced by data scientists into the graphical interfaces that engineers need. 

As data scientists continue to design new models in various programming languages, these models may be harder for engineers to implement in the low-code tools they prefer. For example, many research models are not built with model size constraints; however, with model compression, engineers can take the totality of that model produced by data scientists, and use point-and-click tools to compress layers to fit onto a low-cost, low-powered embedded system using a graphical environment. Through visualising what happens when a model is compressed through apps, engineers can ensure that the model does not lose fidelity, so that it may function accurately on devices of low-computational power.

This is being done with the latest models in Python, yet incorporating them into MATLAB allows the transfer of the latest innovation to engineers as fast as possible. 

Considerations before applying low code  

While the use of low-code solutions has made it easier to manage multi-component engineering projects, it does not make incorporating AI an easy task on its own. Approaching projects with clarity of objective is still paramount. While point-and-click low-code tools are often used in data labelling, those tools cannot be effective without knowledge of what to label. 

Similarly, low-code solutions need to be able to incorporate the technology that engineers need. If an engineer pivots from implementing ML algorithms to deep learning algorithms, the low-code solutions that they are using need to be able to incorporate the respective algorithms.  

Another aspect to consider is time and resource allocation. As low-code solutions bring much-needed speed to deadline-driven engineers, it means engineers also need to decide how to allocate the time saved in a way that benefits the overall project. For instance, with the use of low-code tools, engineers are freed up to do more sophisticated work, such as driving greater accuracy through more iterative testing.   

The future of low code

As more mining companies incorporate AI into their work, implementing low code will become a natural extension of any AI-driven work, making interdisciplinary team work more productive and effective. 

Used across applications and industries – from medical to defence – low-code solutions are likely to bring an added layer of sophistication to the work that engineers can produce across numerous data types. As greater involvement from an industry perspective continues, the importance of incorporating the newest ML and deep learning models into applications for engineers will become more important. 

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