AI is finding its way into the cloud, as well, where Microsoft and Google are already purporting to change the way we work. Using the artificial intelligence behind Microsoft’s Office Graph, the Office 365 app Delve presents users with recommendations for documents and conversations they may want to view. Google’s recommendations in apps like Google Now use the AI embedded in the Google Knowledge Graph to present information it thinks users will want to see, including nearby restaurants, shops, and museums.
This is just the beginning. Expect to see an endless stream of AI apps aiming to solve every problem you have ever had.
So that's It for IA, right?
After 50 years, has IA finally been vanquished? Are we ready to relinquish control to autonomous cars, software bots, and AI-based recommendation engines?
The answer is yes … and no.
While AI will clearly play a larger role in our daily lives, it is not a panacea. AI-based solutions work best in structured environments where all relevant information can be considered and where the goals of the system are clearly defined – ordering a pizza, setting a meeting, playing chess.
In all these cases, while the number of possible outcomes that has to be considered may be enormous, the outcome can be predicted with a high degree of confidence (and can be tweaked based on user response to improve results in the future). This is exactly the situation where a powerful computer has an advantage over the human mind.
On the other hand, artificial intelligence is not well suited to situations where goals and inputs are not well defined; it’s here where intelligence augmentation will continue to play a major role.
Let’s look at an example. A salesperson focused on closing business relies upon many different systems to do their job. Email is the main source of information today, but others include SharePoint, Box, or Dropbox for documents; Skype, Slack, Yammer, Chatter, text messaging, and the phone for real-time communications; and business apps for order processing, trouble ticketing, and customer relationship management.
On any given day, integrating that disparate information is an exhausting task. AI-based machine learning systems can extract topics from messages on each of these systems and even match them across systems. But then what does the salesperson do with that information?
Here is where AI reaches its limits and IA excels: assisting the human operator in evaluating what action should to be taken next.
Should they contact the prospect to offer a discount, reach out to the internal support team for help in solving a customer problem, or research a competitor’s offering to develop a competitive comparison?
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