Artificial intelligence took center stage at Google’s annual user conference, Cloud Next 2018. The company made several announcements that make machine learning and artificial intelligence accessible to both developers and businesses.
One of the first announcements came in the form of Cloud AutoML, a managed service that lets developers build machine learning models without requiring any specialized knowledge in machine learning or coding. AutoML Vision, along with other automated ML services became publicly available. According to Google, it is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs, by leveraging Google’s state-of-the-art transfer learning, and Neural Architecture Search technology.
With AutoML, developers use a simple graphical user interface (GUI) to train, evaluate, improve, and deploy models based on their own data. Apart from computer vision, AutoML also offers translation and natural language models. AutoML Natural Language helps customers to predict custom text categories specific to domains automatically. With AutoML Translation, they can upload translated language pairs to train custom translation models.
Google has also enhanced its cognitive computing APIs. Cloud Vision API now recognizes handwriting, supports additional file types (PDF and TIFF) and product search, and can identify where an object is located within an image. The improvements to Cloud Text-to-Speech include multilingual access to voices generated by DeepMind WaveNet technology and the ability to optimize for the type of speaker from which the speech is intended to play. Cloud Speech-to-Text added the ability to identify what language is spoken as well as different speakers in a conversation, word-level confidence scores, and multi-channel recognition. With this enhancement, customers can record each participant separately in multi-participant recordings.
Dialogflow, the platform to build bots, can now be used to build AI-powered virtual agents for the contact center, including phone-based conversational agents known as interactive voice response (IVR). Google Cloud Contact Center, an AI solution based on Dialogflow, includes new features alongside other tools to assist live agents and to perform analytics.
With Dialogflow Phone Gateway, customers can assign a working phone number to the virtual agent and begin taking calls. The dynamic platform can scale based on the utilization patterns. Behind the scenes, all of the telephony infrastructure, speech recognition, speech synthesis, natural language understanding and orchestration are managed automatically.
Another component of Dialogflow Enterprise, the Dialogflow Knowledge Connector understands unstructured documents like FAQs or knowledge base articles to automatically build intents with automated responses sourced from internal document collections, enriching the conversational experience with little extra effort. The added information extracted from the knowledge base is integrated with the Dialogflow agent to deliver conversational user experience.
Apart from the above enhancements, Dialogflow now includes automatic spelling correction, sentiment analysis and text-to-speech capabilities.
Google is integrating its cloud-based machine learning assets with Dialogflow to build an intelligent contact center. The platform includes an agent assist system to provide the call center agents with relevant information through suggested articles and shortcuts for fulfilling relevant tasks in real time. Another feature called the Conversational Topic Modeler uses Google AI to analyze historical audio and chat logs to uncover insights about topics and trends in customer interactions.
Google is working with several industry players to integrate Cloud Contact Center AI with mainstream contact center platforms.
From automated ML to AI-based contact center, Google wants AI to become accessible to both developers and enterprises.
