How AI understands human cognition to augment operational efficiency?

Advances in artificial intelligence (AI) today go beyond human capabilities of being never getting bored, tired, and continuously learning and improving. Initially, in the development of AI, researchers only realized the power and possibility of machines that understand the meaning and nuances of human speech. The technology is often considered as the ability of a machine to learn and solve cognitive problems. Many businesses are increasingly investing in the latest digital technologies, including AI, but they are not necessarily cracking their trapped value for their growth.

In scientific methodology and laboratory interconnectivity, AI is perceived to mimic human reasoning as well as use it as a model to supplement and augment human observation and decision processes. AI has the potential to grasp the meaning of simple language and speak back to users, but it is restrained by its literal interpretations of human questions. In general, a computer can know the definition of a word, but it doesn't comprehend the meaning of words within a larger context.

Today, researchers are training AI algorithms to construe available data and specified criteria to meet business objectives and drive operational decision making. However, as AI is going beyond human cognition, humans need to distinguish between a machine and another human in conversation; otherwise the machine will exceed the human intelligence level.

In an article by Philippe Desjardins, David Heiger, Ph.D., and Matt Burtch, scientific discovery becomes more complex and demands on lab throughput increase. In this way, AI employed in the lab environment assists to achieve both scientific and economic outcomes. Most enterprises drive quality and expand capacity gather some form of data, while processing consistency and effectively planning future resource allocation. Foresighted businesses use that data for both scientific purposes and operational needs to envisage output and analyze how a process or team is performing. However, the diversity of equipment, coupled with the need for manual analysis of the data collected, means that high-quality data is not consistently available, and whatever data is available is often not helpful for decision-making.

The core reason for introducing AI in the operational lab environment is to monitor operations and continually provide increasingly sophisticated insights. Business models for scientific advancements are demanding higher levels of efficiency. Adopting powerful sources of information will become a necessary component of scientific productivity and is an inevitable next step in the creation of lab management systems that are so efficient, only AI will be able to produce them.

Further, they noted that to effectively manage the growing complexity of the lab and achieve a uniform understanding of operations, AI must be fed universal and high-quality data. Relatively simple sensors that collect basic information such as power, humidity, and vibration can provide surprisingly rich information if correctly interpreted. Collectively, these various sensors can act as a type of nervous system, providing AI the means to understand instrument status, behavior, and utilization.

Once artificial intelligence understands the machine's typical operation, the system can then become predictive in nature. By the time, the technology improves the internal model of the equipment and learns to identify more complex workings and deviations, even to the level of correlating precursors with future behavior, such as predicting when a tool will fail and why. AI can also learn to develop a prescriptive ability, suggesting when maintenance is required and actions that must be taken to avert potential failure.