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AI Comes to Life in Industrial Automation

AI Comes to Life in Industrial Automation


Artificial intelligence (AI) is starting to have an impact on the manufacturing industry, including industrial automation malaysia. Predictive maintenance data is being transformed into valuable insights for the first time. It is becoming more efficient to operate in the manufacturing supply chain.

Artificial intelligence is assisting manufacturers in increasing uptime, increasing yield, and reducing downtime. Machine learning has recently been shown to provide considerable benefits to the manufacturing industry. Smarter robots, as well as plant and warehouse efficiency, are examples of how this can be accomplished.

Reinforcement Learning in Robots

Industrial automation malaysia

Deep reinforcement learning (RL) is a set of techniques that make it easier for manufacturers to transition robots from simulation to the real world. Deep reinforcement learning (RL) is a subset of reinforcement learning (RL). Nicholson reiterated a Google comment, stating that if they can crack AI with deep RL, they will be able to crack the entire problem.

RL is a subset of the greater field of artificial intelligence and machine learning (ML). “Reinforcement learning is an area of artificial intelligence. AI is a broad phrase that encompasses many different concepts. 

Machine learning is a component of artificial intelligence. In RL, deep learning is implemented within a deep neural network, which is then integrated into an ML learning network,” Nicholson explained. “You can use the algorithms to program robots,” says the author. As the robots become more familiar with their tasks, they are rewarded numerically. When they make a mistake, they are subjected to an RL penalty. “Those procedures aid in their learning.” 

The Start of Embodied AI

Having the robot make decisions without having to send signals to a cloud will make the decisions the robot makes more effective. As a result, edge computing is frequently the most effective solution. “You have the ability to choose which mechanical bodies artificial intelligence can inhabit. The majority of the time, artificial intelligence (AI) exists on the cloud rather than in the four-sided world. “With robots, it is possible to have embodied AI at the edge,” Nicholson explained. “Any processes that the robot is capable of performing can be added to the robot to give it more confidence. “The AI can become like a child playing in a sandbox, discovering what it is capable of.”

The Digital Twins Growing

While AI/ML alone will not be able to address the problem of a boat blocking the Suez Canal, ML and simulation combined with one another will be able to accomplish so by creating a digital twin. The use of digital twins is increasing the ability to respond to occurrences that are not recorded in historical data. 

They can supply examples of “what-if” inquiries that they can then follow up on and answer further down the line. The digital twin enables artificial intelligence to function in a complicated context where numerous systems are working at the same time. 

Before putting the digital twin into actual operation, it can be used to mimic modifications and see the outcomes. “When it comes to digital twins, different people mean different things. It begins as a tool for deliberation, a digital representation. “That representation of the world is a complicated system,” 

Nicholson explained. “The simulation in the digital twin demonstrates that if you change this one thing, you will see what happens. ” It enables artificial intelligence to explore a complex space. Digital twins are “what-if” machines that simulate different scenarios. They allow for the investigation of potential outcomes.”


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Darren Bryant