Google ups its AI services with new Contact Center solution and developer tools
The tech giant’s conversational platform is now turning into an intelligent virtual agent and agent helper.
Google is boosting its AI-as-a-service offerings this week, most notably with the alpha release of a new Contact Center AI solution.
Contact Center AI is built around its Dialogflow development suite for conversational agents, which was launched last fall and already in wide use. Dialogflow Enterprise Edition now has the ability to build AI-powered virtual agents for contact centers, a Phone Gateway for taking calls without infrastructure, Knowledge Connectors for understanding unstructured data like FAQs and Sentiment Analysis.
In Contact Center AI, a Virtual Agent first answers the call and handles it if possible. If not, it passes the call to a human representative, who is helped by an Agent Assist system that continues to monitor the call and provide supporting info as needed. There is also a Conversational Topic Modeler to analyze topics from audio recordings and chat logs.
In outline, it sounds like some other systems already out there, but the implication is that this is a contact center offering Duplex-like capability. Google is clearly wary of the negative feedback it received when Duplex — a human-sounding bot — was demoed in the spring. Its announcement made clear that Contact Center AI will observe “best practices,” which now include telling people when they are talking to a bot.
Earlier this year, the tech giant announced Cloud AutoML, which the company said “helps businesses with limited [machine learning] expertise start building their own high-quality custom models.”
Vision, Natural Language and Translation
It’s supposed to sit between highly customizable machine learning tools like TensorFlow on one end and pre-trained machine learning functionality like Cloud Vision API on the other.
The first service for Cloud AutoML is Cloud AutoML Vision, now available in beta. Employing a drag-and-drop interface, it supports the ability to create machine learning models for image recognition and then quickly deploy them on Google Cloud.
Google points to real estate firm Keller Williams Realty, which says it is using AutoML Vision for training custom models to recognize furnishings and architecture so customers can search for home listing photos by, say, “granite countertops.”
Also announced in beta releases this week are AutoML Natural Language and AutoML Translation.
The Natural Language service can be employed to sift information about people, places, events and other topic categories from documents, news stories or blog posts, or it can determine sentiment or intent in call center conversations or messages in an app.
The new AutoML Translation lets developers, who may have limited machine learning training, create high-end custom models for specific purposes.
Among a variety of other improvements to various AI-based cloud technologies, Google also said that its designed-from-scratch Cloud TPU customer processors, optimized for machine learning and first announced last year, are now available in alpha as third-generation versions.
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