The difference and interplay between artificial intelligence and machine learning
It’s hard to look anywhere and not be bombarded by advancements in artificial intelligence (AI) in media, politics and business. All our devices, appliances, utilities, vehicles, you name it, are enhanced with “smart” features and only getting smarter, all distributed and embedded at scale. As a consumer sometimes it feels like you’re just along for the ride, but machine builders and system integrators have the opportunity to explore AI solutions to help advance automation and machine engineering. But the plethora of AI offerings makes it difficult to know where to start and what will work for you. Because the AI buzzword is everywhere, you have to be skeptical of its use or approach decisions about its use with a clear knowledge of what artificial intelligence is and how it can benefit industry.
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What is artificial intelligence, machine learning and deep learning?
What is artificial intelligence? It’s a hard concept to comprehend without also defining machine learning (ML), which is a subset of AI, and deep learning, which is a subset of ML. “While AI captures much of the spotlight, understanding its interplay with machine learning (ML) is crucial. AI is a broad field aimed at creating systems capable of tasks requiring human intelligence,” says Milton Guerry, president of Schunk.
“Artificial intelligence is the overarching field dedicated to creating systems that can learn, reason and act autonomously. Machine learning is a subset of AI, providing the tools and techniques that make learning possible,” says Pradeep Paul, director of manufacturing intelligence at E Tech Group.
Artificial intelligence encompasses a broad field for creating many systems to perform tasks that require human intelligence. “ML, a subset of AI, involves training algorithms on data to improve task performance without explicit programming,” Guerry adds. AI and ML work together to develop smarter, adaptive systems.
“Deep learning, in turn, is a subfield of ML, focusing on complex models that drive many of today's AI advancements,” Paul says. Deep learning models enable the algorithm training for machine learning.
“AI provides the intelligence framework, while ML enables continuous learning from data,” Guerry adds.
Data analytics: the role of smart equipment and IoT
Does that mean that all our smart devices and appliances are using AI? Mostly, the answer is no, but “smart” equipment is a necessary step to the proliferation of AI. A smart device or equipment is one that is connected to a network and can collect and analyze data from sensors. That network of equipment and data forms the Internet of Things (IoT).
Data analysis alone can perform some decision-making without human interaction and also without using artificial intelligence. Likewise, we also couldn’t have AI without smart devices networked together collecting massive amounts of data.
Many industries have been using machine learning for decades. The power of ML has expanded as compute power advanced quite rapidly. However, the use of algorithms to perform data analytics isn’t new; the computation power behind ML algorithms has increased rapidly, allowing ML to power AI features. But it’s important to remember, just because it’s ML, doesn’t mean it’s AI.
As an example, E Tech Group’s manufacturing customers are typically asking for actionable production insights for their individual sites. Manufacturers want to understand asset performance, identify downtime drivers and optimize factors that influence yield and resource consumption. “While the potential of AI, particularly large language models, is significant, practical and deployable solutions for process automation and equipment integration are still emerging. Currently, machine learning models and analytics effectively address many of these needs,” Paul says.
He points to energy-intensive industries, such as mining and data centers, which benefit greatly from energy-management analytics. Process industries, like pulp and paper and plastics, leverage process models to improve yield and reduce resource consumptions. None of those employs artificial intelligence, and not every application needs AI, where basic analytics will do the trick. However, AI does have potential for machine automation and the design of machines in manufacturing in the right applications.
