Artificial Intelligence

Machine Learning And Artificial Intelligence In Business: Year In Review, 2018

Much has happened in artificial intelligence (AI) this year. From NVIDIANVDA +0.75%, Intel INTC +2.34% and a host of startups announcing new chips focused on both training and run time inference, to announcements of a wide variety of new algorithms, much of the news has been focused on research and academia. The challenge is to notice what is happening with AI and machine learning (ML) that business can look to for short term adoption to aid in performance.

The technologies and techniques of AI and ML are still so new that the main adopters of the techniques are the large software companies able to hire and to invest in the necessary expertise. Those companies come into focus by looking at two pairs of companies which show the two angles of attack.

There are Google GOOGL +3.21% and Amazon, early movers due to their cloud foundation who are working to figure out how to generalize techniques developed for their products, in order to attract the wider business market. On the other side are Microsoft MSFT +3.46% and IBM IBM +0.11%, companies with long histories of addressing business needs who are layering AI across product offerings. Yes, there are plenty of other companies also working to add machine learning to their product lines, and an even larger body of startups focusing on building solutions with AI and ML techniques at the core, but those four companies are well known and display the two key methods to attack the problem.

Within the new technologies, there are certain areas that have gained more traction than others that are even earlier in their lifecycles. Let us discuss a few of the areas.

Natural Language

For years, search engines have been getting better at understanding more natural syntax, both written and spoken, for questions people ask. The ability to understand more human language is natural language processing (NLP). While the two previous years saw attention on personal assistants, 2018 was the year that serious inroads into business analytic applications have been made in NLP.

Just about every business intelligence (BI) and enterprise software company released versions of NLP for their analytic applications. The ability for business line management and employees to type or speak “what’s the sales last quarter for region X?” rather than have to deal with drop down list boxes and other more technical UX features is helping move BI from shelf-ware to regular use.

On the other side, reporting is almost coming full circle. From early full text reports on mainframes, to basic graphics being added, to the current world of advanced visualizations, images have taken over in much of the analytics world. Yet people see things differently, so combining visualizations and text better serves a wider audience.

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