Five major trends in artificial intelligence robots in 2022

In recent years, robotics technology has made tremendous progress. Areas such as robotic process automation are being used in more and more enterprises.

RPA software is needed to integrate enterprise processes with robot actions and artificial intelligence input. RPA software automates repetitive, labor-intensive and time-consuming tasks, minimizing or eliminating human involvement to drive faster, more efficient processes throughout the factory. Instead of dozens of workers in a manufacturing plant, RPA experts can program and run robots to perform these tasks. Often, another person is needed to service, maintain, and repair the hardware. But artificial intelligence is taking RPA’s capabilities to higher and higher levels. Here are some of the top trends in the field of AI robotics:

RPA and Artificial Intelligence

The latest trend is RPA combined with Artificial Intelligence. This is an essential element for RPA to be able to handle high-volume, repeatable tasks. By moving these tasks from humans to robots, they can be handled appropriately, reducing labor costs, making workflows more efficient, and speeding up processes such as assembly lines.

This also simplifies the entire field of robotics. Industrial settings can now combine RPA software and factory automation systems, instead of different teams using different software. In the past, robotics teams used specific programming languages ​​to handle areas such as multi-axis robot kinematics. Factory automation technicians use different languages ​​and tools, such as programmable logic controllers and shop floor systems. Artificial intelligence is helping to integrate these two worlds, adding a greater degree of mobility and autonomy to robots. In order for stationary and mobile robots to work together seamlessly, they must be able to exchange information accurately.

AUTONOMOUS OPERATION

Robots increasingly tend to operate in open, uncontrolled spaces that are also inhabited by humans. Many companies are working hard to build autonomous vehicles that are both powerful and economically viable.

In addition to creating robots that can be used as consumer products (besides entertainment), artificial intelligence and robotics also face challenges. AI will need to consider thousands of parameters and variables happening in real time. A lot of them are constantly changing many times a second.

Neurosymbolic Artificial Intelligence

The current boom in artificial intelligence is triggered by the fusion of data and computation, which enables neural networks to perform on some very challenging tasks. Achieving very impressive results. While significant research is still underway to understand the full capabilities of neural networks, we are now seeing increasing interest in:

(1) Understanding their limitations.

(2) Integrate them with other proven real AI algorithms, including symbolic and probabilistic methods.

In the coming years, the field of hybrid neurosymbolic methods will be extensively explored to enable applications beyond the capabilities of any one method on its own. Just as different areas of the human brain operate differently, next-generation AI systems may integrate different operating modules. Research in this direction will be particularly useful for the development of universal service robots capable of robust perception, natural language communication, task and motion planning for object manipulation, and natural human-robot interaction across a variety of tasks.

Claims Processing

Over time, more and more tasks are becoming automated rather than simply programmed. For example, enterprises are using RPA to automate actions such as understanding what is on the screen, completing keystrokes, identifying and extracting data. Healthcare is a good example, where such systems are used to verify and process patient claims.

Corporate Recruitment

Anyone who posts a job posting typically receives hundreds or even thousands of resumes. AI bots can be used to screen these candidates and even find great candidates who may not meet all the requirements right away. By training the AI ​​to note similar qualifications and other characteristics, it is possible to come up with better candidates and focus on those that might otherwise be missed.

Therefore, RPA will become an important trend in cross-industry artificial intelligence automation in the future.

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