Applications of Machine Learning and AI in Electrical and Computer Engineering
In electrical and computer engineering, the rate of technological innovation is astounding both experts and novices. Amid breakthroughs in hardware and software engineering, every year brings new innovations — and new challenges. In particular, recent years have seen breakthroughs in machine learning and AI, which are shifting the limits of technology in novel ways.
These developments have broadly and impactfully altered the ways we approach electrical and computer engineering. It has also led to shifts in the way we are able to build and maintain cybersecurity frameworks, smart grids, power systems, processing architecture and more.
Emerging Applications in AI for Electrical and Computer Engineering
Recent breakthroughs in AI have driven widespread adoption of AI algorithms, Large Language Models (LLMs) and other forms of deep learning and novel computing methods. A few breakthroughs include:
- Transformer models that enable complex relationships between AI and contextual language
- Edge AI, which refers to when AI algorithms or models are deployed directly into edge devices (such as Internet of Things devices)
- Federated learning frameworks that allow engineers to train AI models without accessing personal data from users
These and other technological developments play an important role in electrical and computer engineering: they give engineers new ways to tangibly harness technology.
How Innovations Support Engineers
For example, running large language models requires a massive amount of data processing in near real-time. This has long required developers to use the cloud and decentralized computing to keep up with the speed, meaning that the importance of functional APIs and stable connectivity technologies is difficult to overstate.
Edge AI and edge computing, along with federated learning frameworks, allows engineers to train and develop their AI models on local devices. This mitigates potential privacy concerns and allows for the AI to use less computing power for a specific purpose. For engineers, this means they can leverage computing power more efficiently, and they don’t have to consider the complications that come with maintaining massive data centers. The developments may even inspire further innovations in the efficiency of hardware and processor technology.
Future Technologies: What Might We See in the Next 5 Years?
Given this landscape, computer and electrical engineers must navigate a rapidly shifting environment and will very likely have their work affected by the development of emergent technologies.
One of the key areas where we expect advancements is increases in efficiency in AI hardware. Perhaps most importantly is energy-efficient AI hardware, which aims to vastly improve engineering capability without creating an unsustainable drain on energy and water. In addition to improvements in AI’s energy usage, many engineers are working in this area to make AI hardware more model efficient (also referred to as parameter efficient), which involves making AI/ML models more optimized and simplified for better performance. Engineers are also making models more data efficient by designing them in such a way that they can better handle data sparsity.
In addition to the AI developments we’re seeing now, we may see further developments that benefit engineers:
- Quantum computing stands to revolutionize how many flops (a measure of computing power) we can squeeze into a single machine
- Integrating AI into connectivity protocols (such as 6G) may optimize performance, security protocols, network management, bandwidth allocation and threat detection
AI in Power Systems, Smart Grids and Cybersecurity
Evolving technologies driven by machine learning and AI can help us achieve goals in a wide range of fields: power systems and smart grids, cybersecurity frameworks and even driverless vehicles.
Power Systems and Smart Grids
Engineers who work on power systems and smart grids have been dealing with a range of challenges for a long time, including:
- Electrical faults, which can lead to local outages and unsafe conditions
- The need for ongoing maintenance and continuous monitoring
- Reliance on aging materials that degrade over time
AI and machine learning algorithms can optimize the way we maintain these systems. With AI tools that support real-time detection, engineers can rapidly respond to faults or short-circuits. And, with predictive AI algorithms, engineers can rely on predictive maintenance and optimization information, allowing them to perform improvements proactively.
AI in Cybersecurity: Threat Detection, Response Execution and Predictive Analytics
AI can play a critical role in helping to improve cybersecurity frameworks and efforts, and many organizations have moved to adopt AI frameworks to improve their cybersecurity. The Cybersecurity & Infrastructure Security Agency (CISA) has presented a roadmap for the use of AI in cybersecurity, outlining five lines of effort. Notably, the roadmap includes expanding AI expertise to enable the safe, responsible and effective use of AI in cybersecurity and technological infrastructure.
Given this emphasis on expertise, well-educated electrical and computer engineers with a master’s degree are uniquely positioned to support this safe and responsible AI-supported cybersecurity measures. Michigan State University’s M.S. in Electrical and Computer Engineering curriculum incorporates AI and cybersecurity training, allowing graduates to prepare for these increasingly necessary roles.
Some of the ways in which AI can be implemented in cybersecurity efforts include:
- Automating threat detection to allow for more robust monitoring and faster response times
- Predictive threat modelling, which can bolster resilience efforts
- Automated incident response documentation to help cybersecurity teams better understand how to improve responses in the future
AI-Optimized Hardware and Neuromorphic Computing
Signal Processing and AI-Optimized Hardware
Another way that AI and machine learning can impact computer and electrical engineering is through hardware optimization and signal processing. In signal processing, AI developments enable more accurate signal interpretation without the need for brute force hardware power. In addition, incorporating AI and ML frameworks into this area can help filter noise, therefore reducing processing times and improving signal accuracy.
Integrating AI-based software can optimize hardware, allowing for more computing power even on older or less advanced hardware. Integrating these optimizations can majorly reduce the power consumption of large systems. Similarly, AI software optimization aims to utilize existing hardware frameworks and architectures more effectively to similar ends, meaning that hardware will require less upgrades, updates and maintenance.
Neuromorphic Computing
Another way in which AI and machine learning have impacted broader computing capabilities is through the use of “neuromorphic computing.” This complicated area of engineering seeks to use silicon and computing power in a way that mimics the neurological frameworks of the human brain.
These and other similar technologies allow for more energy-efficient hardware, which can help improve our capabilities for reliable data and power transmission. It can also make embedded systems on boards more effective, shaping the next generation of AI-supported processor architectures.
How Can an M.S. in Electrical and Computer Engineering Prepare You for the AI-Enabled Future?
Given this increasingly complex landscape, it is more important than ever for electrical and computer engineers to have expertise in their field. A master’s degree in electrical and computer engineering offers engineers the opportunity to expand their skills and knowledge and learn to apply electrical and computer engineering concepts to multiple disciplines.
At MSU, the M.S. in Electrical and Computer Engineering emphasizes practical experience and real-world topics so that graduates are ready to pursue in-demand engineering careers. By the time they graduate, MSU electrical and computer engineering students will be ready to lead advances in AI-enabled engineering, especially as it relates to computer hardware, automation and communication networks.
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. Online students can choose from two plans of study — selecting a thesis or non-thesis option — in order to tailor their degree to their interests. Students can also choose to take an optional graduate certificate using their elective requirements.
Courses in the online program are taught by MSU’s faculty of pioneering researchers and experienced educators. At MSU, students complete a career-focused graduate education from an R1 research institution ranked among the Top 100 Global Universities by U.S. News & World Report.
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