The use of Artificial Intelligence (AI) is often associated with maverick visions of hover cars, living on the moon, and robots with a tendency towards acquiring life threatening attitudes. But like the paperless office and three-day working week, it seems AI is a remote possibility for most people and most companies.
But actually it isn’t. The chances are you will be using it a lot in the near future, and Narrow AI will be the format that predominates. The most recognised current use is in Apple‘s voice command product Siri.
Human touch
Narrow AI uses a logic driven process that replicates human actions. Typically it sifts through massive amounts of information and accurately extracts only what is needed. However, the real benefits occur when used to contextually layer searches and reporting to build accurate scenarios. It becomes the perfect example of the three Cs – context, context, context.
For example, Siri is actually quite a poor performer in narrow AI. You ask it a question such as: where is the nearest coffee shop? It will give you a list, and by tapping on a particular option you get a map with an accompanying pinpoint. This is a lightweight response compared to what the technology can do.
Narrow AI can be programmed to not only identify the nearest coffee shops, but also different forms of travel to them, travel time, how to access those forms of travel – nearest bus stops, train stations etc. There could also be a map, but with specific driving, walking, skateboarding or bicycling directions, and the journey times for each. In addition, you could be informed what the weather will be like at the destination, nearby attractions, and also alert friends via email or social media that you will be at the coffee shop (and at what time).
The Lowdown
A good illustration of how narrow AI is currently being used contextually in multi-layered form is a mobile first business service for iPhone that my company built.
Lowdown works simply by the user creating or accepting a calendar invitation using any calendar service (Google, Outlook etc). The app then generates information around a meeting. It displays travel options, when to leave for meetings, the time it will take to get there, a map, profiles of individuals and companies that will be present, tweets by them, shared company and personal connections, and recent email exchanges. This happens instantly on an app without spending time searching the web, diaries, timetables, maps or asking for directions. All emails or tweet updates by meeting attendees can easily be monitored, and responded to.
It is perfectly feasible to extend the Lowdown principle to corporate-level diary based systems that include access to internal documentation. This would enable business managers to know exactly how to get to meetings, who will be present, and also to receive any documents needed for meetings.
Research potential
There will also be apps for information searches. This could include the researching of interview candidates, or for personal use finding out about teachers and schools, babysitters, or for just learning about the neighbours.
The possibilities for narrow AI are not infinite, but for better organisation and information gathering it is in a league of its own.
About the author
David Senior is CEO of Lowdownapp Ltd. With nearly 20 years’ experience in IT he has worked for leading global corporations, but in the last two years co-founded two companies, Spark33 Ltd to advise CxO’s on mobile and mobile apps, and Lowdownapp to focus on the use of narrow AI in the creation of multi-layered contextual information based mobile apps.
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