Artificial intelligence (AI) represents a technology paradigm shift, with the potential to completely revolutionise the way people work over the next few years. Application programming interfaces (APIs) are crucially important in enabling the rapid development of these AI applications. Conversely AI is also being used to validate APIs, themselves, and also to analyse and optimise their performance.
Wikipedia defines an API as a ‘set of subroutine definitions, protocols and tools for building application software’. In slightly less dry terms, an API is basically a gateway to the core capabilities of an application, enabling that functionality to be built into other software. So, for example, if you were creating an app that needed to show geographic location, you might choose to implement Google Maps’ API. It’s obviously much easier, faster and future-proof to do that than to build your own mapping application from scratch.
How APIs are used in AI
And that’s the key strength of API—it’s a hugely efficient way of enabling networked systems to communicate and draw on each other’s functionality, offering major benefits for creating AI applications.
Artificially intelligent machine ‘skills’ are, of course, just applications that can be provided as APIs. So if you ask your voice-activated smart device—whether it’s Siri, Cortana, Google Assistant, or any of the rest—what time you can get to the Town Hall via bus, it’s response will depend on various skills that might include:
- Awareness of where you are—from a geo-location API
- Knowledge of bus routes and service delays in your area—from a publicly available bus company API
- Tracking of general traffic and passenger levels—from APIs that show user locations provided by mobile device manufacturers
- Being able to find the town hall—from a mapping API
None of these APIs needs to know anything about the others. They simply take information in a pre-defined format and output data in their own way. The AI application, itself, has to understand each API’s data parameters, tie all their skills together, apply the intelligence and then process the data.
Everything is possible
That means you can combine the seemingly infinite number of APIs that exist in any way you like, giving you the power to produce highly advanced applications—and create unique sources of value for your business. You could potentially build apps to enhance the customer experience, improve your internal processes, and analyse data more effectively to strengthen decision making—and perhaps even identify whole new areas of business to get into.
How AI is being used to improve APIs
APIs are the ideal way of getting information into AI applications and also helping to streamline analytics—yet artificial intelligence also has a vital role to play within API development itself. For example, AI can be used to automatically create, validate and maintain API software development kits (implementations of APIs in multiple different programming languages).
AI can also be used to monitor API traffic. By analysing calls to APIs using intelligent algorithms, you can identify problems and trends, potentially helping you tailor and improve the APIs over time. Indeed, AI can be used to analyse internal company system APIs, for example, helping you score sales leads, predict customer behaviour, optimise elements of your supply chain, and much more.
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