Over the last decade, businesses have realized data is one of the most crucial corporate assets a company can own. The realization should come as no surprise, as the ongoing growth of IoT has fueled a data explosion, with experts now predicting that by 2025 there will be over 41 billion connected IoT devices generating nearly 80 zettabytes of data. As a result of the need to comb through the increasing amount of datasets, companies have turned to emerging technologies such as Artificial Intelligence (AI) to more efficiently extract insights that will help move their businesses forward.
Today, AI has become a key technology used to parse through location data to inform the creation of more accurate maps. Further, increasing competition among companies within the mobility, automobile transportation and logistics (T&L) and public sectors has spurred even further demand for advanced AI solutions, particularly location-aware AI. This means AI that is able to understand the properties of location information and allows developers to leverage these insights into their applications and products.
Location data-infused AI is used to create pattern recognition and location signatures from the data it collects, and serves as a key component associated with generating high-definition (HD) maps and realistic simulators to visualize this data. From understanding how consumers move, to powering the autonomous driving movement, these intelligent visualizations are making next-generation mobility possible.
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The AI value chain: then & now
The AI value chain within the enterprise industry has dramatically shifted over the past few years. Organizations previously focused on using machine learning (ML) technology for building solution model architectures and algorithms, but more recently utilization of AI and ML technologies has shifted toward solutions allowing users to create benchmarked models to evolve, compose and scale datasets.
The enterprise industry has also recently standardized its usage of AI and ML for gaining location intelligence, making it more commonplace for sensors, aerials or satellites to be used to produce standard definition maps. However, usage of these same emerging technologies has shifted to produce more accurate HD maps, which are built by machines to serve machines, and has become an integral component of the autonomous driving movement. HD maps have made it more possible to conflate multiple sources to recognize features and patterns, dealing with both static and real-time events to predict behaviors and conditions.
A typical example of this is AI/ML-powered maps in which the end-to-end process creates a self-healing map, which continuously gathers ‘low-level’ and ‘high-level’ observations and aggregated map features. These technologies work in unison to evolve and adapt each map feature (e.g., signs, lanes, and pavements) and are customized for each geographic region. However, where these maps fall short is when it comes to negating the nuances affiliated with accurate data collection itself, making it more important for location-aware AI to become implemented to address this issue.
In short, location-aware AI is designed to understand the dependencies and properties of the location information it receives and generate more advanced insights. Real-time semantic relationships across physical objects are a key element of this and can be used to build location graphs capable of laying out more accurate geospatio-temporal representations of the world.
The real-time data, such as weather, traffic or sensor data, captured from more advanced location graphs can be used to drive more informed business decisions. Coupled with location-aware AI, location graphs allow professionals to develop new data patterns and generate more accurate samples of the data it collects. Further, location-aware AI can help expose key features and be composed with other data in ways that are unachievable with traditional AI approaches.
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Application of location-aware AI within the transportation and logistics industry
One of the ongoing challenges facing the T&L industry has been how to approach large-scale optimization of data. The sheer volume of data collected from providers, consumers and vendors has continued to serve as a major challenge for supply chain networks, and has led to T&L vendors seeking new solutions to address this issue. Despite the vast amount of solutions adopted over the years, location-aware AI has seen the most traction in the T&L space as it has become a popular technology implemented within supply chain networks.
Specifically, reinforcement learning (RL) technology has been a popular AI-powered solution adopted within the T&L space to address data optimization issues. This technology provides users with simulation and sensitivity analysis capabilities, allowing professionals to create predictive models and simulations for advanced business intelligence. In short, companies can utilize RL technology to make better business decisions related to better fleet management and more efficient distribution networks.
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Collaboration between the public and private sectors
While location-aware AI’s potential has grown within the T&L, automotive and smart cities spaces, no specific industry has fully capitalized on this technology just yet. As a result, it’s become evident advancements in AI/ML are not likely to occur behind closed doors, but rather environments open to innovation and collaboration.
One way to spur further adoption of location-aware AI technologies is through stronger collaboration between the public and private sectors, especially when it comes to creating a ‘smarter planet.’ A stronger partnership between location intelligence platforms and government entities would enable more smart city initiatives to be deployed related to public safety, and allow vendors within the automobile and mobility sectors to create more reliable and accurate AI-based location intelligence offerings.
It’s clear that location-aware AI has the potential to revolutionize the enterprise industry if widely adopted. Location-aware AI has already shown its value in creating more accurate HD maps and delivering more robust location intelligence to vendors across multiple industries. As a result, it’s imperative that professionals across the enterprise take a closer look at location-aware AI solutions as they strive for more reliable and accurate digital solutions for their business.
