Artificial Intelligence is Improving Fast, Enhancing the Intelligence of Automation

New generative artificial intelligence (AI) chatbots like ChatGPT and Bard have become enormously popular since their debut late last year, attracting millions of users for their ability to engage in human-like conversations. It represents a significant advancement in AI systems, particularly in the field of natural language processing.

These chatbots have elevated conversations about AI into the mainstream. But it is not just buzz – they have changed the way people interact with AI, demonstrating its extraordinary potential. 

The infinite possibilities should inspire one to think long and hard about how business is embracing AI, since companies will distinguish themselves by how well they use it. 

I’m not just talking about large language models like these chatbots. AI and machine learning (ML) technologies are automating repetitive tasks, streamlining operations and personalizing customer experiences. It is and will increase innovation speed and productivity at organizations that have or will deploy them.  

Much like these new chatbots, the performance of AI models depends on the quality of the data it’s fed. It’s not just about volume. The data also must be accurate, consistent and contextual. When models are trained on transparent and well-documented data sources, organizations can demonstrate the integrity and reliability of their AI applications. Understanding the origin, quality and processing of the data helps build trust among users and stakeholders.

In many industrial applications, the data that powers AI comes from a sensory engine. Sensors serve as the crucial link between the physical world and AI models. Just as our eyes and ears enable us to perceive and understand the world around us, sensors provide machines with the ability to gather valuable information about their environment and operation. The data they capture can then be processed and used to make informed decisions. 

Just as AI has become better at sounding human, the technology also has improved at working in complex, chaotic environments, such as distribution centers and factory floors. Automation has been used in these environments for years, but it involved simple rule-based systems that executed predefined instructions or followed predetermined processes.

AI takes robotics and automation to the next level. Machine learning algorithms enable robots to acquire knowledge, improve performance through experiences and adapt to changing environments and operating constraints. AI-driven perception systems enable robots to interpret visual and sensory data, enabling object recognition, navigation, and pick-and-place. Natural language processing allows robots to understand human commands and improves human-robot interaction.

The combination of AI and robotics is transforming numerous industries.

• In warehouses, automated storage and retrieval systems allow for more efficient use of floor space, increased order-picking accuracy and fewer labor constraints due to labor shortages and re-training. Robots use computer vision algorithms and ML to automate the very manual task of unloading and loading freight – in some cases, the use of robotics has cut time of unloading a 53-foot trailer from eight hours to less than 90 minutes.
• In facilities, AI-powered systems monitor and analyze data from sensors, machines and equipment to predict maintenance needs and prevent failures. By analyzing patterns, detecting anomalies and forecasting potential issues, organizations can make the transition from traditional maintenance strategies of “react and respond” to a new paradigm of “analyze and predict.”
• In healthcare, remote patient monitoring devices use sensors to collect real-time data such as vital signs. To prevent overwhelming providers with excessive information, AI and ML are used to aid in flagging what is immediately actionable and inform decision making. With this new continuous monitoring and data sharing approach, a doctor who would normally handle 10-15 hospitalized patients may now receive data from hundreds or even thousands of patients simultaneously. This is where AI and ML play a crucial role in sifting through the data and highlighting only the information that necessitates a decision from the physician.

These are just a few of the use cases. The abilities of AI will only continue to improve over the coming years, unlocking seemingly limitless ways to take advantage of the technology. I’m looking forward to the next 10 years. It’s time to get on board.

Written by Mehul Patel.

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