All you need to know about AI and Robotics

Artificial intelligence (AI) is revolutionizing many sectors by boosting efficiency, accuracy, and productivity through innovative applications. Since the explosion of AI in 2023, this technology has become more accessible and integrated into everyday operations across various industries, such as robotics and manufacturing.

Reports indicate that the global AI manufacturing market is projected to reach a staggering $9.89 billion by 2027, highlighting the rapid growth and adoption of AI solutions.

Let’s take a walk.

Why is Artificial Intelligence Important?

AI cannot (at the moment) fully mimic human thinking. As such, calling it "intelligence" might be a stretch. AI doesn’t think as much as it analyzes. What does it analyze?

Data.

AI is a tool capable of consuming and analyzing an unimaginable quantity of deep data to find patterns and determine the best possible course of action based on all past recorded actions. AI will run through everything it knows has happened and will anticipate, through that data, all that could happen. From there, it will draw the best possible course of action. (Think about Paul Atreides in Dune part two when he ends up drinking a little too much of the Kool-Aid, sees Everything, and goes, “I see a narrow way through.”)

So, what does this mean?

It means AI can automate repetitive learning and discovery through data. It can perform high-volume, computerized tasks reliably and tirelessly once the system has been set up by humans. This doesn’t mean the task becomes more intelligent, but it does mean AI adds “intelligence” to what already exists, improving the process and accelerating it without compromising the quality of the output.

AI finds structure and regularities in data so that algorithms can acquire skills.

The repetitive learning comes in handy when AI is able to learn progressively from the information it is fed through algorithms, to improve its performance. It will identify patterns and structures in the data, enabling self-improvement and adaptation to new data (the same sort you will find while playing against an AI computer in an online game of chess, or the way your buying patterns will enable your recommended products on Amazon or Instagram to be updated regularly).

The models adapt continuously when given new data, and will keep getting more accurate the more you use them. This means that data is the single most important thing you can give to an AI (and why some companies are knowingly encroaching on set laws that forbid website scraping by scraping websites for information, regardless of its ethicality). The data is the answer, and the AI just applies algorithmic logic to pull the answer to the surface quickly and accurately (as long as the data fed to it is also accurate).

There have been talks of Big Data for years now, and this is where it matters. The company with the biggest and most accurate data pool is bound to have the competitive advantage. If you have the best data in a competitive industry, even if everyone is applying similar techniques, the best data will win.

In robotics, AI allows conventional robots to collect and interpret data.

A regular robot moves thanks to its preprogrammed back-end technology. But AI allows it to become self-reliant through data processing and analysis. So, an AI robot deployed in the industrial field can gather data about what tasks have to be done and then find the most optimal method to do the job.

AI in Robotic Manufacturing

Robots have been used in the manufacturing industry for decades, performing tasks like assembling, welding, packaging, and shipping. Based on the specific tasks robots are used for, the AI algorithm is designed and applied to the machines.

Although there are quite a few differences between robotics and AI, they are two branches that benefit each other: Robotics enhances productivity by performing repetitive tasks more efficiently than humans. AI is primarily used to improve skills like movement, adapting to the environment, optimizing performance, diagnosing errors, and performing autonomous tasks.

Both robotics and AI aim to automate tasks and facilitate processes for humans, using data collected by input and output sensors to facilitate decision-making.

Every decade or so, new technologies emerge to facilitate these processes. A little under twenty years ago, cobots began to make significant inroads on manufacturing floors. More recently, AI has transitioned from being a trend to a necessity in automation, makeing computer vision and flexible robots accessible to everyone and setting the stage for tomorrow’s industry. Recent developments in AI in manufacturing have introduced intelligent robots that can assess situations in real time and perform dynamic actions, such as those developed by French Startup Inbolt.

Find out more about inbolt here.

Currently, the widespread communication about AI has led customers to actively seek AI integration into production lines. This is a significant shift from just five years ago when there was reluctance to adopt AI in manufacturing.

Now, manufacturers are asking for AI implementation, recognizing its incredible capacity.

Research suggests that European manufacturers are already embracing the AI surge, with 69% of German manufacturers indicating they are ready to implement some form of AI in their operations soon.

As the workforce ages, AI in robotic manufacturing becomes even more crucial, ensuring that the industry can maintain and even enhance productivity despite demographic challenges.

The Aging Workforce

There are 10,000 baby boomers retiring every day in the US alone, making it increasingly difficult to find a skilled workforce. Gearoid Reidy, a Bloomberg columnist, recently wrote, "Over half of [Japan's] businesses say they can't find enough full-time staff." With one in ten people now over eighty, there aren't enough workers to go around.

This issue isn't limited to Japan. Germany's industries face similar challenges, with baby boomers retiring and fewer workers entering the job market due to low birth rates. Currently, with 45.8 million workers, Germany expects to lose 7 million by 2035. Ralf Winkelmann, managing director of FANUC Germany, said that "robots [will] enable the survival of companies that see their future at risk due to staff shortages."

The situation in the United States is similar. Despite political concerns about immigrants taking jobs, the US Chamber of Commerce reports that Americans are aging out of the workforce, while the growing number of elderly will need more care and support.

So if we really want to keep the industries in europe and in the USA, there is no choice but to automate. However, automation must be cost-effective, offering a faster return on investment. This is where AI plays a crucial role. It's not viable to wait for years to break even on an investment. In most cases, a 12-month return on investment is necessary, and achieving this requires computer vision and AI.

Computer Vision & AI

AI robots can see and understand their environments, which enables them to complete complex tasks. The primary function of AI in robotics is to provide better interpretation and less human reliance on robots.

AI is mainly being used to teach important functions to robots, such as vision, utilizing algorithms that enhance visual interpretation for robots. For instance, Inbolt’s GuideNOW is today’s most efficient 3D matching vision technology. AI-based algorithms process massive amounts of 3D data at high frequency and identify the position and orientation of a workpiece, adapting the robot trajectory in real time, making it ideal for automated manufacturing.

GuideNOW uses the 3D camera to track the workpiece as soon as it enters the field of view, providing continuous offsets to the robot's trajectory. The robot can figure out the best path for itself through the analysis of the surrounding.

This technology works on industrial robots and cobots in the automation manufacturing industries.

Conclusion

The more AI evolves, the more it’ll allow new and improved integration and innovation, offering significant benefits in productivity, efficiency, and quality across industries. The overlapping field between manufacturing and AI is rather large, and is bound to get larger still as industries increasingly demand AI-enabled products.

The application of artificial intelligence in robotics remains challenging, both technologically and ethically. Ensuring the safety of autonomous decision-making by robots is still a significant hurdle. And while machines generally operate with high precision, a certain margin of error persists.

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