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Applications of AI and Machine Learning in Electrical and Computer Engineering

Applications of AI and Machine Learning in Electrical and Computer EngineeringElectrical and computer engineers work at the forefront of technological innovation, contributing to the design, development, testing, and manufacturing processes for new generations of devices and equipment. As these professionals strive for innovation, their pursuits may overlap with the rapidly expanding applications for artificial intelligence.

Recent progress in areas like machine learning and natural language processing have affected almost every industry and area of scientific research, including engineering. Machine learning and electrical engineering professionals leverage AI to build and optimize systems and also provide AI technology with new data inputs for interpretation. For example, engineers build systems of connected sensors and cameras that ensure that an autonomous vehicle’s AI can “see” the environment. They must also ensure that the information is communicated from these on-board sensors at lightning speed, as any delay in processing could result in a severe accident.

In addition, harnessing artificial intelligence’s potential may reveal chances to boost system performance while addressing problems more efficiently: AI could be used to automatically flag errors or performance degradation so that engineers can fix problems sooner. Electrical and computer engineering leaders have opportunities to realign how their organizations manage daily operations and grow over time.

Discovering and implementing the latest engineering applications of artificial intelligence may prove to be a path for career advancement on the cutting edge of the field.

Related Article: Master’s in Computer Science vs. Electrical and Computer Engineering

How is Artificial Intelligence Used in Electrical Engineering and Computer Engineering?

The term “artificial intelligence” describes a wide variety of systems built to imitate how a human mind makes decisions and solves problems. For decades, researchers and engineers have explored how different types of AI can be applied to electrical and computer systems. These are some of the forms of AI that are most commonly incorporated into electrical and computer engineering:

  • Expert systems solve problems with an inference engine that draws from a knowledge base equipped with information about a specialized domain, mainly in the form of if-then rules. In use since the 1970s, these systems are less versatile than newer forms of AI but generally easier to program and maintain.
  • Fuzzy logic control systems make it possible to create rules for how machines respond to inputs that account for a continuum of possible conditions, rather than straightforward binary.
  • Machine learning includes a broad range of algorithms and statistical models that make it possible for systems to find patterns, draw inferences, and learn to perform tasks without specific instructions.
  • Artificial neural networks are specific types of machine learning systems that consist of artificial synapses designed to imitate the structure and function of brains. The network observes and learns as the synapses transmit data to one another, processing information as it passes through multiple layers.
  • Deep learning is a form of machine learning based on artificial neural networks. Deep learning architectures are able to process hierarchies of increasingly abstract features, making them especially useful for purposes like speech and image recognition and natural language processing.

Many of the most promising achievements at the intersection of AI and electrical engineering have focused on power systems. For example, the Electric Power Research Institute reported on its efforts to create algorithms capable of identifying malfunctions in transmission and distribution infrastructure based on images collected by drones. Further initiatives from the organization include using AI to forecast how weather conditions will affect solar and wind power generation and adjust to meet demand.

Other proposed AI applications in power systems include implementing expert systems that reduce the workload of human operators in power plants by taking on tasks in routine maintenance, data processing, training, and schedule optimization. Or, a fuzzy logic controller can serve to improve the performance, consistency and fault detection of grid control systems

Engineering the Next Wave of Artificial Intelligence

Automating tasks through machine learning models, such as artificial neural networks or decision trees, results in systems that can often make decisions and predictions more accurately than humans can. As these systems continue to evolve, it is likely they will fundamentally transform our ability to leverage information at scale.

But the tasks involved in implementing machine learning algorithms for an ever-growing number of diverse applications, from agriculture to telecommunications, are highly resource-intensive. It takes a robust, customized network architecture to optimize the performance of deep learning algorithms that may rely on billions of training examples. Furthermore, once an algorithm is trained, it may be required to continue processing an ever-growing volume of data — some of the sensors currently embedded in autonomous vehicles are capable of generating 19 terabytes of data per hour.

Electrical and computer engineers play a vital part in enabling AI’s ongoing evolution by developing computer and communications systems that match the growing power of artificial neural networks. Creating hardware that’s optimized to perform machine learning tasks at high speed and efficiency opens the door for new possibilities in autonomous vehicle guidance, customer relationship management, fraud detection and countless other applications.

Signal Processing and Machine Learning for Electrical Engineering

The adoption of machine learning in engineering has been especially valuable for expanding the horizons of signal processing. These systems function efficiently to increase the accuracy and subjective quality when sound, images, and other inputs are transmitted. Machine learning algorithms make it possible to model signals, detect meaningful patterns, develop useful inferences, and make highly precise adjustments to signal output.

In turn, signal processing techniques can also be used to improve the data fed into machine learning systems. By cutting out much of the noise that would otherwise be included in these inputs, engineers achieve cleaner results in the performance of Internet-of-Things devices and other AI-enabled systems.

The Department of Electrical and Computer Engineering at MSU demonstrates the innovative, life-changing possibilities that can come from applying AI to investigations in signal processing. Multidisciplinary researchers synthesize concepts from both electrical and computer engineering, artificial intelligence and other fields in an effort to simulate the way biological eyes process visual information. These efforts serve to deepen our understanding of how our senses function while leading to greater capabilities for visual prosthetics, brain-computer interfaces, motion sensors, and computer vision algorithms.

About Michigan State University’s Online Master of Science in Electrical & Computer Engineering

Michigan State University’s online Master of Science in Electrical & Computer Engineering program readies students to excel in the field by expanding their technical knowledge and focusing on the real problems spurring technical innovation. Courses in the online program are taught by MSU’s faculty of pioneering researchers and experienced educators. Online students can choose from two plans of study, selecting a thesis or non-thesis option as they complete a graduate education from an R1 research institution ranked among the Top 100 Global Universities by U.S. News & World Report.

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