Intelligence
The field of artificial intelligence (AI) aims to study the extent to which machines and computers can be developed with aspects of intelligence. As general intelligence is a rather complex concept to tie down, AI helpfully breaks it into a series of central problems (or goals) – including reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.
In understanding what an intelligent campus is - or could be - these AI topics are a useful starting point to describe, evaluate or even design 'intelligent' actions or devices.
Perception and action
Consider the use of sensors for measuring changes such as temperature or motion and more complex devices such as cameras or GPS-enabled equipment.
Data from sensors could be combined with knowledge about the environment and used to make logical deductions. In order to use this data more effectively, we could set goals and take action to adapt the environment, in turn using this learning to help devices improve.
This cycle of perception, reasoning and action is a simplified version of what humans do and AI attempts to replicate this, or parts of it, with computers and machines.
Several things are central to this discussion, including data, devices and connectivity. Data collected by sensors can be more valuable when devices are connected and the data is processed effectively.
The Internet of Things (IoT)
Current everyday devices like mobile phones can collect data on various aspects of activity, including location. Mobile phones also have connectivity through the telecommunications network and wifi to the internet.
So do many other common devices, from webcams and printers to central heating systems and baby monitors. Out on the street we can see connected vehicles, ticket machines and lighting, as well as engine maintenance and healthcare tools in industry and public services.
An interesting extension to this is the concept of wearable devices, for example used for health monitoring or fitness applications.
This has become known as the Internet of Things – a wide variety of devices connected to the internet with the ability to collect and transmit data. IoT provides the potential to integrate all manner of data and use it in aspects of the intelligence concept, for example reasoning or adaptation of the environment.
Data and analytics
Data is all around us and it’s the subject of much topical debate, including work on open data, big data and analytics, not to mention ethical issues including privacy and security.
In some ways the physical collection of data is easy. It is when we try to interpret and make sense of it that we hit many of the challenges. 'Analytics' has become a common term, referring to the identification of patterns and interpretation of data. Its sophistication varies from presenting and describing data right through to developing insights and making predictions.
Notifications and alerts are known as 'push' data, meaning they are initiated from the system or source rather than requested. 'Pushing' data to an individual or group of individuals may have limited value without understanding what the data is and what it might mean. Equally, it is possible to misinterpret data and reach conclusions that don’t represent the whole picture.
Data analysis is critical in providing meaningful information and services.
Becoming smart
The concept of an intelligent campus hinges on several key points:
- The availability of connected devices and sensors
- The ability to collect, store and process data (including combining it with other data)
- An understanding of what the data is and how it can be used
- A set of goals to benefit campus users
The last of these is crucial in making this a useful topic to explore. It is also important to consider the combination of these aspects – for example, having high-quality network infrastructure is only part of the jigsaw.
Sometimes the term 'smart' is used in relation to using smart devices on campus. In many ways this is synonymous with intelligent campus, although there are subtle differences in meaning between the two terms.
Some technological definitions, for example, refer to smart sensors that can collect and transmit data autonomously but lack the analysis and reasoning aspects of intelligence. Perhaps the important distinction here is the ability to see beyond the data and the technical capability to understand the purpose and benefits fully. The combination of different datasets adds another dimension to the perception of intelligence, where cross-referencing between datasets can lead to reasoned conclusions.
As an example, data from weather sensors could combine with building occupancy data and historical patterns of room temperature to predict the necessary level of heating.