In a little less than 20 years, Google has evolved from a search engine experiment at Stanford University to a technology behemoth that's synonymous with the discovery of information.
Many of Google's core services straddle the business and consumer worlds, and as such serve as the backdrop for many of the menial, digital tasks we execute on a daily basis. But despite its relative ubiquity, there are still pivotal areas in tech where Google faces fierce competition.
Machine learning, the concept of training large-scale AI networks to teach and improve themselves over time, is one such area. There's an arms race among public cloud providers to build the best machine learning capabilities for enterprises interested in creating their own intelligent applications. Here's a look at what Google brings to the table.
"Machine learning has been infused into Google products and services for over a decade," said Rob Craft, Google Cloud's product lead for Cloud Machine Learning. "Google first began to use machine learning by applying it to our products that serve billions of users."
Craft said Google's dive into AI and machine learning began as a matter of necessity after the company reached a scalability high mark with its original rules-based programming system. It was then that Google shifted to a learning system that would expand the boundaries of its entire platform.
"Rules are fragile. We built a great business using rules, but at some point we couldn't use it anymore," Craft said, pointing out that two of Google's legacy products -- Search and Maps -- were among the earliest beneficiaries of Google's machine learning advances.
Today machine learning touches nearly every Google product. For instance, the Google app uses speech recognition and natural language processing to understand speech in 55 languages; Google Search uses RankBrain, a ranking signal that uses deep learning to improve results; and Google Photos taps into the company's latest image recognition system.
Internally, the push into machine learning prompted the formation of dedicated teams that are actively using the technology to improve Google's consumer products, cloud platform, and Google's own business.
Google Cloud, Google's enterprise division headed up by Diane Greene, employs dedicated engineering and product teams to create and build machine learning tools and services, Craft said. More recently, Google announced the creation of a new machine learning team -- helmed by Fei-Fei Li, formerly the director of AI at Stanford, and Jia Li, who was previously head of research at Snap, Inc -- as part of an effort to unify some of the disparate machine learning work across Google's cloud.
Google's machine learning portfolio includes a range of cloud services and tools. In 2015, Google helped accelerate adoption of its services by open-sourcing its proprietary machine-learning library TensorFlow. Craft said TensorFlow is now the most popular machine learning on the software repository GitHub, with contributions coming from mostly outside Google.
As a follow up, Google Cloud Platform released a dedicated set of machine learning APIs based on Google created pre-trained machine learning models, which Craft said are meant to give developers access to high-quality cognitive services. The API set includes the Translation, Cloud Vision, Natural Language, Speech and Jobs APIs.
Cloud Machine Learning is Google's fully managed service that lets users create neural network and algorithm models and also run predictions at scale without worrying about the infrastructure. The service utilizes several of Google Cloud's data analytic tools such as BigQuery, DataFlow, and Datalab.
Google is also leveraging machine learning to power its own infrastructure that's used by Google Cloud users.
"For example, in our data centers, we're using machine learning to reduce the amount of electricity needed for cooling by 40 percent," Craft said.
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