Understand AI Versus ML, Especially for Industrial and Embedded Applications

September 28, 2022

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Artificial intelligence has been around for quite some time, both real and the science-fiction variety. Real AI, the kind that’s useful in a host of embedded applications today, arrived in full form about a decade ago. Early examples of AI use include speech and other sound recognition, and for minimal levels of autonomous driving.

Following those advances came machine learning (ML), which is a subset of AI. From a high level, AI usually solves tasks that require human intelligence. Alternatively, ML solves specific tasks by learning from data and making predictions.

 

The definition of ML for the purposes of the embedded computing space involves the use of data and algorithms to gradually improve the accuracy of the embedded computer. Using statistical methods, algorithms are trained to make predictions, and also to find key insights related to the equipment in use. Depending on the application, the user, the environment, and other parameters, these insights can be used to dynamically drive decision making within an application.

Machine learning algorithms are typically created using frameworks that accelerate solution development, such as TensorFlow and PyTorch. Such frameworks have grown in popularity over the past few years.

Add Deep Learning to the List

A term that’s also sometimes used interchangeably with machine learning is deep learning. You could make the argument that ML and deep learning are synonymous as deep learning is also a subset of AI. Deep learning and machine learning differ in the way that each algorithm “learns.” Deep learning is more constrained, as it typically operates with predefined data. Not to muddy the waters further, but deep learning also has the ability to alter those predefined data sets, thereby removing human intervention (and human error). Some refer to deep learning as scalable machine learning.

Traditional machine learning is more dependent on human intervention to learn, as it’s the humans who determine the set of features used by the algorithms. In most cases, the “learning” requires more structured data.

Components of ML

The main parts of the machine-learning process include a decision process, an error function, and an optimization period. In the decision process, the machine learning algorithms first make a prediction, based on the data it has received to date. The error function evaluates that prediction. Over time, there are more examples to fall back on, thereby increasing accuracy. After evaluation, optimization occurs where it can. The algorithm should continually repeat the evaluation process and continually optimize the model that’s been created.

The biggest challenges associated with machine learning accuracy have to do with the data that’s used to create and then evaluate the models. The issues could arise from a lack of data, data that’s of poor quality, and data that’s irrelevant to the task at hand. These issues should work themselves out over time, but it’s important to be on the lookout for improper decisions when the machine-learning process commences.

Machine learning can be a boon to an industrial application, and that’s easily attainable using the ITX-P-C444 industrial Pico-ITX SBC.

Examples of two SBCs that are capable of handling machine-learning applications are the WINSYSTEMS’ ITX-P-C444 and COMeT10-3900. Let’s start with the ITX-P-C444 industrial Pico-ITX board, which is based on NXP’s i.MX8M applications processor. A second CPU, an Arm M4 core, is employed to handle real-time tasks for such applications as digital signage, industrial automation, energy, and building automation, all areas that are making use of machine learning. The processing capability combines with extensive I/O options, including dual Ethernet and USB 2.0 and 3.1 ports.

 

WINSYSTEMS’ COMeT10-3900 is a low power industrial COM Express Type 10 Mini module with Intel’s Atom E3900 SoC processor fully capable of enabling machine-learning.

An Intel Atom E3900 processor provides the smarts for the COMeT10-3900 industrial SBC. It conforms to the COM Express Type 10 Mini module form factor. This low power module is designed as a processor mezzanine that can be plugged onto a carrier board that contains user-specific I/O requirements. Hence, the designer gets just the features and functions that are needed for the application. That also exhibits the flexibility of the COMs, especially in terms of processor options and upgradeability.

If machine learning is in your short- or long-term design plans, consult the experts at WINSYSTEMS so you (and your embedded computer) can make the right decisions.