Sharpening AI with contextual awareness
The great promise of technology has always been about making things better. Whether that means transforming businesses for success, improving communities and lives, protecting the environment or just making things more convenient, technology can be a force for good. Advances in artificial intelligence and connected devices are blurring the lines between the physical and digital, making it easier than ever before to “do good” faster and more efficiently. For example, intelligent systems like EarthRanger and Connected Conservation are transforming protected area management from push pins on wall maps into a single, integrated real-time operational platform. By combining data from various sensors with ranger observations and historic data on poaching incidents, these platforms can pinpoint and predict threats, sometimes before they happen, enabling park managers to respond more quickly. With limited resources to deploy, it’s paramount that park managers respond to real threats and not just animals breaking through the fence or going to sleep. In other words, context is everything when it comes to interpreting the data.
What is contextual awareness?
Contextual awareness is the ability for a given application to access information about the physical environment and automatically adapt its behavior appropriately in real time. The brains of living organisms are designed to process contextual information from the outside world and generate adaptive responses, such as seeking food or running from danger. Likewise, a contextually aware system is capable of detecting and anticipating, changing circumstances in the environment and reacting to them in real time with the right response. Context is any information that can be used to characterize the situation of an entity (person, place or thing). Most use cases focus on taking contextual information from the environment (computing, user, physical) and combining it with knowledge about the entity to determine what the situation is to generate the right response. Responses can range from tailored (personalized, adaptive, more precise) presentation of information and services to a user to automatic execution of a service.
Here are a few public safety examples where contextual awareness made a difference:
Natural disasters are on the rise, making it increasingly difficult for municipalities to manage spiraling costs. Wildfires burned through nearly 2 million acres of forest this year in California, the largest amount of burned acreage recorded in a fire season. While natural disasters cannot be avoided, interconnected smart cities will empower governments to do more with less and better safeguard private and public assets. In fact, interconnected technology brought a new level of precision and a data-driven approach to managing the California wildfires. Real-time data about fire conditions can be collected and blended with predictive weather data about wind, humidity and temperature to provide fire personnel with comprehensive situation awareness. Drones can be deployed and operated at a fraction of the cost of traditional methods and provide real-time updates to emergency response personnel. That’s a critical new level of insight for answering questions in a wildfire situation like where to dispatch personnel, when and where evacuations should be ordered, and where and when to deploy fire retardant.
Detecting air pollution
More than 80% of people living in urban areas that monitor air pollution are exposed to air quality levels that exceed the World Health Organization limits. Scientists at the University of California, San Diego built a prototype of an air quality monitoring system that used small, portable sensors to monitor air pollution throughout the city. The CitiSense sensors detect pollution levels in that location and transmit the air quality readings to any smartphones in the vicinity. Participants in the pilot discovered that pollution might vary by location and time of day, and many users took action to limit their exposure, such as taking a different route.
How contextual awareness hones AI performance
When applied effectively, contextual awareness frames the range of outcomes or behaviors that AI should suggest by narrowing the field of possible outcomes. This is particularly important for IoT applications, where targeted behaviors should be achieved quickly, with minimal data processing and power usage. And, as data sets keep growing exponentially, it’s also an inexpensive way to make AI systems faster and more accurate. Context is, in effect, a multiplier for data and what gives it meaning, according to IBM. The more context, the higher the value of the data.
The context-aware computing market
The context-aware computing (CAC) market is expected to reach over $125 billion by 2023, growing at over a 30% compound annual growth rate from 2016 to 2023. The proliferation of mobile computing devices and rising demand for more personalized user experiences is helping to fuel the growth. Enterprise desire to augment productivity and collaboration will also play a large part. CAC is generally categorized into the following product types: adaptive phones, active maps, augmented reality and guided systems, cyber guides, conference assistants, fieldwork, web browsers, location-aware information delivery, office assistants, shopping assistants, people and object pagers.
Other CAC for good use cases
Macro challenges, such as growing urbanization and elderly populations, climate change impacts and cybersecurity threats, are bringing heightened attention to the potential of technology to be a force for good. IBM, Microsoft, Google, the United Nations and others are exploring the ways in which intelligent, interconnected technology can benefit society. Solving such large challenges won’t be easy, however, which is why putting them into context is key. “Intelligent” behavior often has more to do with simple situational understanding than complex reasoning. Contextual awareness provides a solution to “frame” the problem, opening the door to some other fascinating innovations such as:
- Caring for the elderly with a home-based fall detection system and [email protected];
- Lowering energy usage and preventing fires in buildings;
- Improving the safety of drivers by monitoring their health status and driving conditions;
- Making airlines safer through real-time flight tracking;
- Protecting the earth by reducing air pollution, conserving water and saving critical species;
- Advancing healthcare with remote patient monitoring, chronic disease management, reduced emergency room waits and safer operating rooms;
- Reducing gun violence by automatically detecting, locating and alerting police to gunfire; and
- Protecting maritime trade routes, critical to national security, through improved threat prediction.
All of these examples share one thing in common — interconnected IoT ecosystems that are contextually aware. While AI, IoT and CAC may be considered different technologies today, eventually they will be so interconnected that it will be hard to differentiate them. Xerox PARC visionary Mark Weiser may have put it best when he said, “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.”
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