Most advanced analytics applications today rely on statistical models to project an outcome based on all the data currently available. While not always 100 percent accurate, those models provide much higher degrees of certainty than previous generations of enterprise applications.
Now, however, vendors are embedding machine and deep-learning algorithms into applications that promise to transform IT by using artificial intelligence (AI) that’s enabled by advanced algorithms.
Many of the machine and deep-learning algorithms employed in the latest generation of applications are not new, but, previously, there was not enough data collected centrally to enable these algorithms to establish relationships between events consistently.
With the advent of the cloud and data lakes based on Hadoop, the cost of aggregating massive amounts of data has dropped dramatically. It’s now economically feasible to apply algorithms to massive amounts of data to enable machines to understand the relationships between various sets of data and then determine actions based on what has occurred.
A New Generation of Intelligent Applications
Over the next few years, just about every application will be replaced by a new generation of intelligent applications. That will create an upgrade cycle for solution providers that is likely to be unrivaled in the history of IT in terms of size and scope.
In fact, Accenture predicts that the IT industry is about to enable a new era of the “intelligent enterprise.” Accenture CTO Paul Daugherty said the most striking aspect of that transformation will be the disappearance of traditional user interfaces. Instead of engaging with intelligent applications using a graphical user interface (UI), natural language interfaces will enable users to interact directly with algorithms using bots that understand spoken words.
“AI is the UI,” Daugherty said. “The interface disappears.”
The two best-known examples of applications making use of advanced algorithms to embed AI functionality based on analytics are the IBM Watson and the Salesforce Einstein platforms. In both cases, IBM and Salesforce are taking advantage of large amounts of data in the cloud and applying algorithms to analyze data to enable cloud applications.
“Because of the cloud, it’s actually possible to do something with big data,” noted Jeff Kaplan, managing director of THINKstrategies, a consulting firm that specializes in cloud computing.
But those results are not achieved via traditional programming models. Rather, Watson and Einstein are taught to recognize the relationships between various sets of data. Over time, the algorithms enable the machines to continually extrapolate relationships at scales traditional analytics applications can’t match.