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Frequently Asked Questions
General
Utiliz uses AI and ML to predict water infrastructure pipe failures by gathering data about the pipes, like age, material, and maintenance history.
This data is processed and used to train a machine learning model, which learns to identify patterns between these variables and pipe failures. The trained model can then predict the likelihood of future failures.
As new data is collected, the model continually refines its predictions, enhancing its accuracy over time.
Our models stand out due to their combination of spatial and tabular data, along with customer-inputted subjective values.
This blend allows for a comprehensive, personalized analysis that is designed to closely reflect each community's unique circumstances and values.
Artificial Intelligence (AI) is a technology that allows machines to mimic human intelligence. It uses large amounts of data, fast processing, and algorithms to learn from patterns and features in the data.
AI applications can range from recognizing speech and learning, to problem-solving and making decisions, and it's used in a variety of fields, including voice assistants, autonomous vehicles, and healthcare.
Machine Learning (ML) is a subset of AI where computers are programmed to learn from and make decisions based on data.
Instead of being explicitly programmed, these systems 'learn' from patterns and insights derived from data, improving their performance over time.
AI and ML work together to create intelligent systems that improve over time.
AI is the broader concept of machines mimicking human intelligence, while ML is a method used to achieve this. Machine Learning allows an AI system to learn from data, identify patterns, and make decisions or predictions. Over time, the AI system improves its performance as it learns more from new data.
Essentially, ML provides the learning and adaptation mechanisms that enable AI systems to simulate human-like intelligence.
Many individuals do not have "clean" data that we can source from, however this is not an issue for Utiliz. As we work alongside your team, we will curate a method best suited for each customers unique issue.
Our solutions include data augmentation (modifying existing data to create new instances), transfer learning (using pre-trained models on similar problems), synthetic data generation, supplementing with external data, and ongoing data collection over time to gradually improve the model's accuracy.
All data you provide to us, whether spatial, tabular, or otherwise, remains your property. We simply leverage it to generate predictive models and data insights through our products.
Products and Services
Our LoF model is designed to predict when a water pipe might fail next. This helps utilities and civil engineering firms in proactive maintenance, reducing costs and minimizing potential service disruptions.
The CoF model uses a combination of spatial and tabular data to calculate the impact of a pipe failure in terms of cost per hour. This includes direct costs such as pipe replacement and labor, as well as the broader impact on the community, like disruptions to parks, railways, or hospitals.
The model combines objective and subjective inputs to calculate a cost per hour for a potential pipe failure.
Objective costs can include things like the cost of replacement parts and labor for repairs. Subjective costs are provided by the customer and reflect the value of different areas of town, such as the importance of water supply to hospitals, parks, or railway stations.
Subjective values are factored into the CoF model through customer input. We will ask customers to provide their assessments of the importance of various areas in the community, such as hospitals, parks, or railways. This allows the model to reflect the unique needs and priorities of each community.
While it's impossible to predict failures with 100% accuracy, our models are based on extensive research and data analysis, and have been designed to provide the most accurate estimations currently possible in the industry.
Our team will work closely with you to integrate these models into your current systems. This process can vary depending on your existing infrastructure, but we're committed to ensuring a smooth transition.
For the LoF model, we would typically need data on your pipe system, such as age, material, past failures, etc. For the CoF model, we would require both spatial and tabular data about your service area, as well as your input on the subjective importance of different areas.