New Visual Interface for Google Cloud’s Speech-to-Text API Makes API Easy to Use !

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At Google Cloud, we’re committed to making artificial intelligence (AI) accessible to everyone and easier to harness for new use cases. That’s why we’re excited to announce the general availability of our intuitive, new visual user interface for Google Cloud’s Speech-to-Text (STT) API, right in Google Cloud Console, which makes the API much simpler and easier for developers to use.
The STT API lets developers convert speech into text by leveraging Google’s years of research in automatic speech recognition and transcription technology. As advancements in AI continue to bring speech to new interfaces and devices, the STT API helps developers add speech functionality to their applications in order to better meet consumer demands.
The STT API covers a wide variety of use cases, from dictation and short commands, to captioning and subtitles. Getting the most of STT, however, can be a complicated process. To achieve the highest accuracy on any AI use case requires careful testing and tuning.
Previously, developers building on the STT API had to do this work manually by carefully experimenting with our API. Just to get started, developers needed familiarity with GCP integration concepts and had to either build their own tools or manage various scripts and API calls to fully understand the API documentation. These actions required cumbersome and time-consuming effort and made measuring, customizing, and improving models even more difficult.
Today’s announcement significantly simplifies the process, facilitating iteration and integration of models into developers’ applications by letting developers perform every API function from within the Google Cloud Console. These tools will make it easier for developers to integrate the STT API with their products or services. This update also gives developers the ability to manage and quickly iterate on their STT model customizations with Model Adaptation.

Model Adaptation allows developers to customize STT specifically for their domains or use cases. Developers can maintain lists of words and weights that will be applied to either every request or just single requests, depending on their needs. Model adaptations are reusable and composable, so once developers have seen good results in the STT Cloud Console, they can deploy to their entire solution.
The Speech-to-Text Cloud Console and Model Adaptation API is available now in all Google Cloud regions and languages and is accessible to all GCP users with no additional cost to that of the underlying API usage. The STT API supports over 70 languages in 120 different local variants. If you’re a developer looking for an easy to use, easy to integrate, and high-quality STT experience, sign up for our free trial and try our new interface on your own datasets today!
Google Cloud Next ’22 to Commence in October: Block Your Calendar!

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We’re excited to announce that Google Cloud Next returns on October 11–13, 2022.
Join us for keynotes from industry luminaries and engage live with Google developers. Explore dynamic content across various learning levels, and dive deep into technologies and solutions spanning the Google Cloud and Google Workspace portfolios. Participate in breakout sessions, demos, and hands-on training. Hear from the world’s leading companies about their digital transformation journeys. You’ll have opportunities to connect with experts, get inspired, and boost your skills. We can’t wait to see you at Next ’22!
It’s too early to determine how the event experience will span the digital and physical worlds, so please stay tuned for updates as we plan with the health and safety of the attendees in mind. In the meantime, mark October 11–13 in your calendar, and visit our event site for updates. For more inspiration, rediscover Next ’21, now available on demand.
Contact Center AI (CCAI) with Agent Assist can Lower Opex and Handle 28% More Chats

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Contact Center AI (CCAI) brings Google’s innovation in conversational AI to solve the most challenging customer service needs while lowering operational costs. More than a thousand customers have deployed CCAI and are steadily turning it on to power their production contact centers.
Today, we’re excited to announce that we’ve made CCAI even stronger with Agent Assist for Chat, now in public preview.
Agent Assist provides your human agents with continuous support during their calls and now chats by identifying the customers’ intent and providing them with real-time recommendations such as articles and FAQs as well as responses to customer messages to more effectively resolve the conversation.
Customers using Agent Assist for Chat have been able to manage up to 28% more conversations concurrently, while also driving up customer satisfaction by 10%. Additionally, we’ve seen them respond up to 15% faster to chats, reducing chat abandonment rates and solving more customer problems.
Agent Assist provides two key components to help agents manage conversations better:
- Smart Reply provides response suggestions to agents so they can quickly and appropriately respond to customer messages. These suggestions can be taken from your top performing agents as well as modified even further to ensure suggestions properly reflect the tone and voice of your brand. Agent Assist learns when and what recommendations to make by building a custom model that’s trained on your (and only your) data.
- Knowledge Assist unlocks the power of your knowledge base to provide articles and FAQ suggestions to agents in real-time as the conversation progresses. When using Knowledge Assist, agents no longer need to make the customer wait while they navigate multiple applications and data to find the resolution to the customer’s issue — the answer is delivered right to them.
“We’ve been very impressed by the chat capabilities of Agent Assist,” said Chris Smith, Vice President of Digital Service at Optus, one of the largest telecommunications companies in Australia
Optus has been using CCAI Dialogflow CX to send queries to virtual agents and sees great potential to use Agent Assist to provide recommendations to their customer support representatives. They expect Agent Assist to help minimize repetitive tasks by providing response and typeahead suggestions, helping improve the efficiency of their agents and the quality and consistency of service they provide.
Another customer, LoveHolidays, is using Agent Assist to support their agents and customers in the travel industry.
“Agent Assist has been a beneficial aid to agents and our customers alike… It gives us the power to flex our contact center staff levels in hours not weeks,” said Eugene Neale, Director of CX Engineering & Business IT at LoveHolidays
Analysts say online chat is becoming one of the most popular ways to reach out to businesses for customer support. IDC research finds that single-function contact centers worldwide are increasingly rare — in 2020, although phone/voice is still responsible for most interactions (at around 18%); email is responsible for around 13% of interactions, and live chat (without automation) is responsible for around 8% of interactions, according to IDC, Toward the AI-Powered Contact Center, Doc # EUR147017320, December 2020.
Deploying CCAI with Agent Assist for Chat
As part of Google’s Contact Center AI suite, Agent Assist provides a seamless handoff from chats managed by your Dialogflow CX virtual agents. If a conversation or customer requires a live agent, Agent Assist will help your team pick it up quickly and drive it to a satisfying resolution.
Historically, when managers saw contact center volumes increase they had two choices: allow customers to wait longer to speak to someone (lowering customer satisfaction) or bring on more agents (increasing cost to serve). Deploying CCAI provides contact center leaders with a third choice: equip agents with tools like, Agent Assist for Chat, to efficiently manage customer interactions while maintaining high quality service.
Global CCAI partners support Agent Assist for Chat
Agent Assist for Chat is a set of public APIs that your engineering team can integrate directly into an agent desktop to control the agent experience from end-to-end. For a more out-of-the-box solution, we have partnered with LivePerson and 247.ai to build Agent Assist directly into their agent desktops.
“Integrating our Conversational Cloud directly with Agent Assist means agents can leverage cutting-edge productivity AI to build even further on the massive ROI of conversational commerce, from reduced agent effort and time-to-respond to increased customer satisfaction and revenue,” said Alex Spinelli, CTO of LivePerson.
More Agent Assist resources
To learn more, check out the Agent Assist webpage. Give Agent Assist a try by training a model and then testing it using the Agent Assist simulator.

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To design and implement a successful machine learning (ML) project, you often need to collaborate with multiple teams, including those in business, sales, research, and engineering. Understanding the essentials of gathering and preparing your data is crucial to align teams, and getting a project off the ground.
Building an ML model that achieves your business’ goal, requires the kind of data that a model can learn from. If the goal is to predict a target output based on some input, you’ll need some data consisting of past input-output examples. For example, to detect (that is, to predict) a fraudulent credit card transaction (the target of the prediction), each example must contain information on a past credit card transaction and whether that transaction was fraudulent or not.
So what are the best practices in preparing and curating data for a machine learning initiative? The answer to this question lies within the guide. You’ll learn:
- To evaluate whether your data is suitable for ML
- What types of data you need–and which ones to eliminate
- How to prevent data leakage
- Tricks to speed up an ML project such as Google’s human labeling service, and Cloud Dataprep
- The importance of the granularity of data
Digital Maturity in Higher Ed Tied to Improvements in Students’ Journey: Study

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Why Higher Ed Needs to Go All-in on Digital
In the wake of the COVID-19 pandemic, the majority of students within the 18-24-year-old demographic now expect hybrid learning environments–even once we are beyond the pandemic. And a vast number of adult learners are seeking options that accommodate their work and family lives now that it’s clear that effective learning can indeed occur virtually. Implementing cloud technologies and achieving digital maturity within higher education will enable institutions to be innovative and responsive to evolving student preferences, while being prepared for future disruptions.

The state of digital maturity
In February and March 2021, Boston Consulting Group (BCG), in partnership with Google, surveyed U.S. higher education leaders on their views of the state of digital maturity in the higher education sector. This survey found that institutional and technology leaders strongly agreed that moving legacy IT systems to the cloud, centralizing and integrating data, and increasing the use of advanced analytics is necessary to make a successful digital transformation, and ultimately achieve digital maturity.
But what is digital maturity? Digital maturity—a measure of an organization’s ability to create value through digital delivery—focuses on three areas of technological advancement that drive large-scale innovation:
- Using cloud infrastructure
- Expanding access to data
- Using that data to improve processes through advanced analytics, such as Artificial Intelligence and Machine Learning (AI/ML)
Although university leaders agree on prioritizing digital maturity, more than 55% said they considered their schools to be “digital performers” or “digital leaders.” However, only 25% of tech leaders at these universities stated that their schools regularly use data analytics. As with corporations and governments, higher education institutions face barriers to technological innovation, such as:
- Competing priorities to meet step-change goals and decentralized decision making
- Budget constraints
- Cultural resistance to change
- Tech staff skillset gaps
Still, leaders understand that the way to overcome institutional inertia is with a strong, goal-oriented vision of what is best for the institution overall. Although only a handful of schools have reached digital maturity as we define it, others can learn a great deal from their examples. Here are the top takeaways from higher education leaders who successfully transformed their institutions:
Digital solutions can improve the student journey in many ways

As digital capabilities hold the key to dealing effectively with declining enrollment and rising costs, higher ed leaders identified four goals that are critical to improving performance:
- Improve the student journey
- Increase operational efficiency
- Scale computing power in advanced research
- Innovate education delivery
The research found that technology investments can help enhance the student journey in the recruiting and retention of students, improving digital education delivery, government funding, and donations from alumni. Digital maturity can make institutions more agile and efficient in delivering education that aligns with the changing societal norms, evolving student preferences, and future disruptions. Survey participants shared that they plan to increase the use of the cloud by more than 50% over the next three years. By shifting legacy IT systems to the cloud, institutions can increase scalability, lower the cost of ownership, and improve operational agility, while offering a more secure, long-term data storage solution.
Cloud-native software-as-a-service (SaaS) solutions provide an excellent platform for centralizing data. However, institutions that attempt to “lift and shift” their legacy systems to the cloud may encounter challenges to achieving measurable improvements in data integration and cost reduction. Higher ed leaders must realize that centralizing data and transitioning to the cloud do not happen simultaneously.
Leaders who are able to articulate a strong vision and commitment will experience a more successful technology transformation. By linking their vision to specific needs, such as more effective recruiting, leaders will find their technology investments will have a more substantial return. University presidents should base their decisions about which systems to move, when, and how on desired performance outcomes.
Big visions become a reality with small steps. Small pilot projects are an excellent way to start the journey toward digital maturity. Small steps toward a significant transformation can reduce resistance to change, build positive momentum, and produce better student outcomes. Read the full report here. If you’d like to talk to a Google Cloud expert, get in touch.
Google and Fervo Agreement to Shape-up Plans for 24/7 Carbon-free Energy by 2030

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When Google announced our plan to go beyond purchasing renewable power for 100% of our energy usage and operate on 24/7 carbon-free energy by 2030, we noted that achieving this goal will require new transaction structures, advancements in clean energy policy, and innovative new technologies. Today, we’re pleased to announce that one of these new technologies—a first-of-its-kind, next-generation geothermal project—will soon begin adding carbon-free energy to the electric grid that serves our data centers and infrastructure throughout Nevada, including our Cloud region in Las Vegas.
Google and clean-energy startup Fervo have just signed the world’s first corporate agreement to develop a next-generation geothermal power project, which will provide an “always-on” carbon-free resource that can reduce our hourly reliance on fossil fuels. In 2022, Fervo will begin adding “firm” geothermal energy to the state’s electric grid system, where Google’s commitments already include one of the world’s largest corporate solar-plus-storage power purchase agreements.
Importantly, this collaboration also sets the stage for next-generation geothermal to play a role as a firm and flexible carbon-free energy source that can increasingly replace carbon-emitting fossil fuels—especially when aided by policies that expand and improve electricity markets; incentivize deployment of innovative technologies; and increase investments in clean energy research, development, and demonstration (RD&D).
Next-generation geothermal technology
Traditional geothermal already provides carbon-free baseload energy to a number of power grids. But because of cost and location constraints, it accounts for a very small percentage of global clean energy production.
That’s one reason this new approach is so exciting; by using advanced drilling, fiber-optic sensing, and analytics techniques, next-generation geothermal can unlock an entirely new class of resource. And the US Department of Energy has found that with advancements in policy, technology, and procurement, geothermal energy could provide up to 120 GW of firm, flexible generation capacity in the US by 2050.
As part of our agreement, Google is partnering with Fervo to develop AI and machine learning that could boost the productivity of next-generation geothermal and make it more effective at responding to demand, while also filling in the gaps left by variable renewable energy sources. Although this project is still in the early stages, it shows promise.

Using fiber-optic cables inside wells, Fervo can gather real-time data on flow, temperature, and performance of the geothermal resource. This data allows Fervo to identify precisely where the best resources exist, making it possible to control flow at various depths. Coupled with the AI and machine learning development outlined above, these capabilities can increase productivity and unlock flexible geothermal power in a range of new places.
This won’t be the first time that Google is applying software solutions to clean energy applications: we’ve just announced an update to our carbon-intelligent computing program that helps us reduce emissions associated with running applications at Google data centers. And other forms of AI and machine learning are currently being used to increase the value of wind energy.
Already this year, Google has taken significant strides toward sourcing 24/7 carbon-free energy for all our data centers, office campuses, and Cloud regions. On Earth Day, our CEO Sundar Pichai announced that for the first time, five of our global data center sites operated near or at 90% carbon-free energy in 2020.
Not only does this Fervo project bring our data centers in Nevada closer to round-the-clock clean energy, but it also acts as a proof-of-concept to show how firm clean energy sources such as next-generation geothermal could eventually help replace carbon-emitting power sources around the world.
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