AI software is booming
THIS is the year artificial intelligence came into its own for mainstream businesses, at least as a marketing feature.
On Sunday, Salesforce.com, which sells online software for salesandmarketing,announced it would be adding AI to its products. Its system, called Einstein, promises to provide insights into what sales leads to follow and what products to make next.
Salesforce chose this date to pre-empt Oracle, the world’s largest business software company, which on Sunday evening began its annual customer event in San Francisco. High on Oracle’s list of new features: real-time analysis of enormous amounts of data. Oracle calls its product Oracle AI.
Elsewhere, General Electric is pushing its AI business, called Predix. IBM has ads featuring its Watson technology talking with Bob Dylan. These moves, along with similar projects at most major tech companies and consulting firms, represent years of work and billions in investment.
It’s all very exciting, the way great possibilities are, and clearly full of great buzzwords and slogans. But will other companies see any value in all this or understand if AI has value for them?
“No one really knows where the value is,” said Marc Benioff, co-founder and chief executive of Salesforce. “I think I know – it’s in helping people do the things that people are good at, and turning more things over to machines.”
Benioff wasn’t selling Einstein’s capabilities short. He was talking about the long-term value of artificial intelligence, which is passing through a familiar phase – a technology that is strange and new, that sometimes overpromises what it can do and is headed for uses not easily seen at the start.
Perhaps a better question than “What is the value?” of this explosion of advanced statistics is “Why now?” That shows both the opportunity and why many companies are scared about missing out.
Much of today’s AI boom goes back to 2006, when Amazon started selling cheap computing over the internet. Those measures built the public clouds of Amazon, Google, IBM and Microsoft, among others. That same year, Google and Yahoo released statistical methods for dealing with the unruly data of human behaviour. In 2007, Apple released the first iPhone, a device that began a boom in unruly data collection everywhere.
Suddenly, old AI experiments were relevant, and money and cheap data resources were available for building new algorithms. Ten years later, computing is cheaper than ever, companies live online and in their phone apps, and sensors are bringing even more unruly data from more places.
Amazon, Google and the rest have AI resources for sale, but many older companies are wary of turning their data over to these upstarts. That, along with fear of a competitor getting on top of AI first, is a big motivation for some to try things out.