Practical AI: helping customers ‘cross the chasm’ and solve important problems

One of the big reasons I joined Google was their leadership in addressing the shift from a "mobile first world to an AI first world". Fast forward to early September 2020: Artificial Intelligence and Machine Learning were on center stage during Google Cloud Next ‘20 OnAir, with a multitude of important announcements, showcasing the practical ways in which Machine Learning is used today. Andrew Moore, head of Google Cloud AI, said: "We are steadily transferring advancements from Google AI research into cloud solutions that help you create better experiences for your customers.” 

So what’s the latest and how will we continue helping customers ‘cross the chasm’ to adopt and successfully implement practical AI for their most-pressing business problems? Answer: by surfacing a world class platform, unlocking breakthroughs with people and by surfacing practical examples to inspire the builders in every organization. Let’s look at those one by one:

Platform   

Customers at all stages of the AI journey are using our tools and solutions to fundamentally change how their businesses run. We introduced several new products and capabilities within the Cloud AI portfolio, including new products and features in Contact Center AI (CCAI) and new versions of Document AI. We also announced improvements to the AI Platform for Machine Learning operations (MLOps) practitioners. And these enhancements have a couple of key things in common—they are all practical and pragmatic applications of AI. 

• CCAI: new CCAI features include Dialogflow CX, the latest version of Dialogflow, available in beta. Dialogflow is the development suite for building conversational interfaces such as chatbots and interactive voice responses (IVR). Dialogflow CX is optimized for large contact centers that deal with complex (multi-turn) conversations. It makes it easy to deploy virtual agents in contact centers and digital channels, and it offers a new visual builder for creating and managing virtual agents. It's available now.
• We also updated the ‘agent assist’ feature in CCAI, which transcribes calls, recommends workflows and provides other kinds of AI-driven assistance to human call center agents. The Agent Assist for Chat module now provides agents with support over chat in addition to voice calls, identifying caller intent and providing real-time, step-by-step assistance.
• CCAI customers can now also create a unique voice for their virtual agents, make changes and add new phrases with Custom Voice, now available in beta. 
• Document AI: we announced new industry-specific tools, starting with Lending Document AI, a new version of Document AI tailored for the mortgage industry. Now in alpha, it specifically processes borrowers' income and asset documents. This can speed up the loan application process.
• Additionally, we announced Procure-to-Pay DocAI, now in beta. This helps companies automate the procurement cycle, typically one of the highest volume, highest value business processes.
• MLOps: as Andrew Moore wrote in his September 1 post: “Even for the ML experts, the long-term success of ML projects hinges on making the jump from science project and analysis to repeatable, scalable operations. Often, analyst teams will hack together an activation process that can be extremely manual and error-prone with too many parameters, decoupled workflow dependencies, and security vulnerabilities.”
• That’s why an entire discipline called MLOps has emerged to solve this issue by operationalizing machine learning workflows. To improve the MLOps experience, we pre-announced: Prediction backend GA, Managed Pipelines, Metadata, Experiments, and Model Evaluation. These features—part of AI Platform—provide automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment and infrastructure management.
• More improvements for MLOps practitioners include: Vizier, now in beta, which auto tunes the hyperparameters of your model to get the best output, and AI Platform Notebooks service is now available.

People

At Google Cloud, we start the AI journey at problem definition, which is inherently interactive in nature and depends on people. We bring teams of people together to collaborate, iterate and define the problem at hand. By exploring the why behind your request and then understanding what problem you want to solve, we then figure out [together] how AI solves it.  For our largest customers, we even have focused teams that help translate our own production ML efforts into best practices and methodologies that we explore and apply collaboratively.

Many AI projects fail because they have not identified a clear use case. This is why we are so focused on building industry-specific solutions, to create real value. And this solutions-driven approach is why we’ve had success in so many practical AI and ML applications to date. 

Practical Examples

Telecommunications leader, Verizon, chose CCAI to create intuitive, consistent customer experiences across all its channels. Tapping into enhanced natural language recognition technologies, coupled with faster processing and real-time access to customer insights and product information, Verizon uses CCAI to help customers quickly find the answers to their questions while enabling agents to better assist with customer requests. Here’s more about the partnership.

Most organizations are sitting on a document goldmine today. That’s because nearly all business processes begin, involve or end with a document. Our partnership with Iron Mountain combined their content analytics, data management and information governance expertise with Google Cloud’s Document AI capabilities, so you can now mine your own archives to uncover new revenue streams and cost savings.

Etsy is using AI MLOps tools to build a more curated shopping experience for their customers. Their marketplace includes more than 65 million seller-generated listings. They're using AI to build sophisticated workflows to help buyers find exactly what they’re searching for, and to deliver enriched recommendations that better reflect their buyers’ unique styles and tastes.

Google is now an AI company

As Google CEO Sundar Pichai said in 2018, “AI is one of the most important things humanity is working on.” And that’s why we at Google Cloud continue working together with our customers, ecosystems and partners to double down on AI solutions that will solve important business problems and change the way all organizations do business.