Introduction
Artificial Intelligence (AI) has emerged as a game-changer, revolutionizing industries and transforming the way we live. Rather than replacing people, AI complements and augments our abilities, enhancing problem-solving processes and unlocking new possibilities. Moreover, harnessing the untapped potential of unstructured data generated by people opens exciting avenues for gaining deeper insights and addressing complex challenges.
The Pitfall of Structured Data
In the discipline of reliability engineering and failure curves, there have been several studies showing ~20% of equipment failures come from age and wear-dependent causes and the remaining 80% are random or variable. When the root cause is ultimately determined, a human factor is identified often. This is not an indictment of people, but an illustration of the importance of our impact. Age and wear-dependent failures are more easily predicted using structured data such as Industrial Internet of Things (IIoT), meter readings, and alarms. On the other hand, unstructured data is typically human derived such as text, handwritten forms, images, video, and speech. If we only analyze structured data, we take a risk in failing to analyze the human element of a complex situation and miss up to 80% of the context.
Unstructured Data: A Treasure Trove of Insights
Most data generated daily is unstructured, ranging from text documents, images, audio, and videos to social media/collaborative interactions. Unlike structured data found in databases, unstructured data lacks a predefined format, making it challenging to process using conventional methods. However, this treasure trove of information holds incredible potential, and AI is the key to unlocking its insights.
When AI is trained to analyze unstructured data, it can derive patterns, sentiment, and context from text, images, and other sources. Sentiment analysis of customer reviews, for example, helps businesses understand customer preferences and pain points, leading to improved products and services. In healthcare, AI-driven analysis of medical records and research papers can accelerate drug discovery and enhance patient care.
Additionally, AI can process and extract meaningful information from multimedia content, such as images and videos, further enriching the understanding of complex problems. For instance, in autonomous vehicles, AI can analyze real-time camera feeds to recognize and respond to changing road conditions.
Application of unstructured data analysis
Visual Inspections: Situations requiring a person to do visual inspection and make an inference (decision) in less than a second are great candidates for computer vision AI. Computer vision can classify an entire image or detect anomalies within an image. These inferences can be a stand-alone solution or part of a larger AI model. Automated gowning inspection is an example of a standalone solution where an Operator stands in front of a camera prior to entering a cleanroom environment and given real-time feedback on their gowning. More complex solutions include product visual inspection to identify product enclosure failures, cracked vials, and foreign material. The object detection results then feed into a larger AI model with time-series production information, IIoT sensors, and alarms to identify patterns and insights into causes of the defects.
Trending Deviations: Quality non-conformities and deviation records are saturated with images and free text. Trending these can be a laborious task to read and tag each record looking for trends and root causes to increase a plant’s output. Supervised and unsupervised machine leaning methods can be employed to draw patterns and insights from the data.
Reading the Room: Organizations make decision everyday that involve people, their perspectives, and associated information. These decisions are generally written down sometimes, however, the justification is either missing or lacks context. Determining the context of the decision, the psychological temperature of the room, who was passionate and who was guarded are all applications of AI. Using transcription and natural language processing connected with sentiment analysis generates a transcription of the conversation, but also analyzes the human (unspoken) parts of those conversations.
Conclusion
AI augmentation is revolutionizing problem-solving by amplifying human capabilities and efficiency. By leveraging the insights hidden within unstructured data generated by humans, we gain a deeper understanding of complex challenges and make more informed decisions. The collaboration between AI and people creates a powerful synergy that holds the key to addressing the most pressing issues of our time. As we move forward, it is vital to embrace this partnership while upholding ethical principles to ensure a future where AI complements and empowers humanity to achieve greater heights.