Narrow AI Is Currently In Work Where It Has Enough Semblance Of The 4 V’s To Move Us Forward. Here Are Some Examples.

Following up from our previous blog, we will now provide some examples. First take a moment to recall machine learning. While machine learning focuses on the idea that machines/technology can learn and adapt through experience, AI takes it a step further with the idea that it is possible for machines to execute tasks ‘smartly’, through the use of concepts such as machine and deep learning. Then we use said concepts to solve a variety of problems. The problems that are solved, of course, vary from industry to industry. And we much prefer how these industries use AI in their activities to how AI is used in the movies. With that in mind, let’s examine the many different ways industries use AI.

Narrow AI Examples: Surface Natural Resource Management 

Narrow AI Examples: Transportation

Department of Transportation Applications

  • State DOTS are uncovering multiple applications, such as:
    • Freight and multi-modal truck parking analysis app – Recommend where truck parking is needed using corridor freight analysis.
    • Analytics and optimization for maintenance schedule improvements – Maintenance decision support customizable tool to provide treatment recommendations for winter maintenance personnel. This includes route-specific weather forecast information. Also, providing route-specific weather and road condition forecasts at various intervals; consequently offering optimized treatment recommendations with respect to type and amount of material, and application times; and furthermore, training new maintenance personnel using the customized rules of practice and historical playback capabilities.
    • Analytics and optimization to predict areas for mud and snow slides and treatment – Recommendations incorporate LiDAR for improved slope. Also aperture analysis, landforms, soils, erosion, events such as land cover data. Finally, classifications of previous events (e.g., landslide scar, wildland fire), hydrography and river hydrology information.
    • Asset maintenance to analyze – High hit guardrails locations for decision making on what types of end treatments to use in the design process. Assume work with volume of crashes, not crashes that involved the guardrail.
    • ITS capabilities dynamic ramp metering based on variable speed analysis.
    • Analyze video for ITS capabilities wrong way driver notifications – Inform roadway operations and emergency dispatch/response.
    • Multiple AI predictors and analyzers for combining unstructured data, real-time data, spatial data, and asset data. This provides outliers, clusters, and data visualizations of confidence indexes for appropriate treatment, projects, or real-time roadway operations actions.
    • Improved traffic signal response communications with vehicles to inform signal patterns and indices over time.
    • Safety road safety curve analysis to recommend based on safety index and confidence. Internal scoring to advise project planners on areas creating unacceptable current safety compliance, combining surface conditions and materials. Also past and weather trends, geometric LRS analytics, used to help predict likelihood of future incidents without repair.