For more than 15 years, developers have used Google Maps Platform to deliver location-based experiences to their end users and used location intelligence to optimize their businesses. Along this journey, we’ve made a variety of changes to better support our community as needs have changed and new industries and technologies have emerged. We started rolling out a new website experience, at https://mapsplatform.google.com, to help you better understand the products and solutions best suited to address your objectives. Plus, now you can directly connect to the developer documentation for each product to get started quickly, and you can visualize usage and associated costs to have a better idea of what to expect before getting started. 

Getting to your solution faster 

Maps, Routes, Places are building blocks that let you develop implementations for any use case. Building for specific use cases, however, typically requires using a combination of APIs and SDKs. To help you quickly understand what’s possible and what you need to build for your use case, you can now visit the solutions tab to select from a list of popular use cases or industries. Once you’ve selected a use case or industry, you’re taken to a page where you can explore relevant products, read helpful blog posts, see how other customers have deployed for similar use cases, and more.  

Direct access to developer documentation

Did you know there are more than a thousand pages of developer documentation created to help you get started, unblock you when you’re stuck, and share best practices? Now when you explore a product or solution from the Google Maps Platform website, you can easily navigate back and forth between our website and documentation. Just tap on JS, iOS, Android or API under the product name to get to the documentation you need. 

Budgeting for your project

To help you calculate pricing for your project, we’ve introduced a new pricing calculator. Once you find the product and API or SDK you plan to use, pull the slider to reflect your estimated number of monthly requests. This will automatically update the “monthly cost” column for each product and API or SDK you plan to use. If your estimated monthly requests exceed the slider limit, contact our sales team to ​​learn about volume discounts that start at 20% off. 

We hope our new website makes it easier to discover our products and solutions, estimate your budget, and start building with our documentation so you can deliver helpful experiences to your users and optimize your business. 

For more information on Google Maps Platform, visit https://mapsplatform.google.com.

With more than 6 years of experience in building open source and Generative AI in education at scale, ibleducation.com continues to evolve its approach and services. More recently, the company became an education-focused Vertex AI integrator. They provide enterprises and academic institutions with a platform to build, train and securely customize large language models (LLMs) for interactive mentors using Google’s Vertex AI.

“The education industry only recently began understanding the value of Generative AI with open source and the opportunities it presents,” says Miguel Amigot II, Chief Technology Officer at ibleducation.com. “We’re providing the chance to build a new type of learning, mentoring, and analytics platform that gives organizations total control over their training methods, data, and more, without locking into one vendor.”

Organizations large and small, including Fortune 500 companies and leading universities institutions, use ibleducation.com to support their learners, allow educators to develop coursework, and provide engineers with a solid platform to build on.rely on it to power their learning strategies.

Recently, ibleducation.com chose to partner with Google Cloud to improve its platform and scale beyond its base of millions of users.

Harnessing the power of GenAI in education and training

ibleducation.com initially began working with Google Cloud to unify its education data strategy to drive real-time and predictive analytics. 

“Google Cloud outperforms others when it comes to maintaining control over algorithms and general data governance,” says Amigot.  “We’re fortunate to be able to maintain ownership of our models while maintaining a pay-per-use pricing model. This is critical for our business. It allows our clients to avoid lower-value maintenance tasks, focus on innovation and user experience, and not worry about excessive costs.”

In the past, because of the high costs and need for AI and engineering talent to produce large language models, schools have been unable to take full advantage of open-source education technologies. Together, ibleducation.com and Google Cloud democratize access to AI-enabled tools for educators worldwide.

With a strong analytics and AI foundation, ibleducation.com has been able to personalize, translate, and create education content faster than before. This has been especially effective in improving accessibility, as automated translation and repurposing ensures those with visual and hearing impairments can interact with the content. 

“Google Cloud enables us to reduce the total cost of building, running, and maintaining large language models by more than 70%,” says Amigot. “We’ve seen language model hosting costs decrease before, but nothing like this.”

Bringing AI-powered content creation to educators

Vertex AI has been especially useful in creating content, helping ibleducation.com to dramatically scale out its learning materials. Usage-based pricing mixed with an open-source model gives ibleducation.com the resources to support its users in developing evolving, valuable learning experiences to serve people worldwide.

Additionally, Google Cloud powers ibleducation.com’s Generative AI-based mentors, which provides a one-to-one learning experience to students and professionals looking for tailored skills development.

“The mission is to provide educators with a centralized system wherein they can manage everything from indexing data about customer courses to designing and facilitating UI/UX for users’ digital mentors,” says Amigot. “Google Cloud has helped us cover all of these bases.”

ibleducation.com allows users to create virtual mentors that provide personalized teaching, assess student knowledge, guide students through skills and learning paths, and offer robust learning analytics. Text-to-text and speech-to-speech interfaces can be offered over Slack, Discord, text message-based bots, web scripts, and LTI integrations. 

“AI Mentor has been a very effective tool for our users because it adapts to the needs of educators and organizations very quickly,” says Amigot. “We’re seeing people use it for teaching assistance, marketing and administration, and a lot more. Vertex AI allows us to have many models for any variety of purposes.”

Looking forward, ibleducation.com is looking to continue taking personalized learning to new heights.

“From a mission standpoint, we want to expand to reach all the organizations not currently served by online education through our platform, analytics, and other AI-powered services,” says Amigot. “Our relationship with Google Cloud is instrumental in achieving our goal to democratize access to next-generation educational capabilities to more communities and organizations around the globe.”

Ready to take the next step? Join our upcoming GenAI workshop for higher education to learn how Google’s AI tools can help education institutions or explore our EdTech solutions to see how you can make education more personal, safe, and accessible with 100+ cutting-edge products.

Communication service providers (CSPs) are at an inflection point. From stagnating revenues, to network strain in meeting the demands of 5G, to challenges in delivering innovative customer experiences, there’s enormous pressure on the telecommunications industry to transform.

Over the past few years, CSPs around the globe have turned to artificial intelligence (AI) to address some of these challenges, but the lion’s share of an operator’s operational expenses are still spent on infrastructure and data management. This has limited their ability to capitalize on core data assets and develop differentiated customer experiences that meet individual needs. 

Enter generative AI, a type of machine intelligence that’s received enormous attention as of late. We’ve all marveled over its ability to generate text that reads like it was written by a person, to create new images, and to build even musical scores. It’s a riveting addition to the AI toolset — and one that complements machine learning (ML) and its ability to identify patterns to make predictions, spot efficiencies, or interpret large data sets. 

But while there’s a lot of hype around generative AI, at Google Cloud we view it through a much more practical lens for the telecommunications industry. Generative AI can accelerate the transformation already underway, with its potential to streamline many of the tools and processes that CSPs engage with daily, bringing a new level of natural interaction between people and computers, and enabling machines to be programmed to carry out an action with a spoken request, and respond in natural, interactive ways. 

Generative AI builds on existing Google Cloud data, AI, and ML services. For example, Contact Center AI, with human-like interactions between callers and computers, has been successfully adopted by CSPs for many years, increasing the satisfaction of both customers and call center workers. As we add generative AI to this technology, CSPs and their customers will see even greater capabilities and impact, for example, virtual agents that not only provide helpful information, but also let customers make payments and execute other transactions. With generative AI, CSPs will be able to harness customer call summaries to better understand customer sentiment and identify cross-sell and up-sell opportunities. CSPs could also easily and quickly build and deploy virtual agents informed by customer conversations that enable more innovative and personalized customer interactions. And that’s just the start.

Three key areas

The contact center is just one of the areas where practical and generative AI will help drive new value. When reflecting on the key challenges facing CSPs today, three areas in particular stand out where generative AI may be transformative:

1. Personalized experiences: In addition to further improvements in customer call center interactions, generative AI can deliver improved personalization in ecommerce interactions — a big factor in helping customers sort through their choices of phones and calling plans. Personalization is also important for lowering churn, offering relevant new services, and managing the customer lifecycle. For example, generative AI could enable CSPs to produce marketing campaign content customized for select themes, and target individual customers with customized text and images. 
2. Autonomous networks: Generative AI will also help to pave the way for autonomous networks by connecting multiple complex AI/ML models used across network planning and operations with large language models (LLMs) that can understand network behaviors and create action plans in areas including network capacity planning and performance. For example, generative AI will enable CSPs to train models with customer experience and sentiment data to build better prediction capabilities. Importantly, the customer data sets used to tune these models are not public, but curated internal customer data — significantly enhancing privacy, factuality, and relevance, while protecting intellectual property. In addition, generative AI will be able to help network planning and design, which requires high levels of reporting and analysis.
3. Streamlined operations: Both operations center uptime and field service efficiency are crucial for managing costs and improving customer satisfaction. In particular, applying generative AI to field service devices can speed up diagnostics and analysis, and can even help with installation, parts, and troubleshooting, and minimizing the number of times companies have to send out trucks and improve field-service training. Generative AI will also provide a productivity boost to IT development processes, enabling code generation and troubleshooting to deliver reliable software products and services.

Data security and reliability

An under-discussed area in generative AI is the importance of data quality and data security in building and training the LLMs that power the technology. Many CSPs are rightly concerned about intellectual property leaking both into and out of LLMs, risking the security of their systems and their intellectual property. We’ve long offered industry-leading data security and privacy technologies, and with generative AI integrated with Vertex AI, we can ensure all data is secured within the CSP’s environment.

Meanwhile, to ensure that their LLMs generate accurate information, CSPs are building out scenarios and use cases for training on smaller, controlled amounts of their own data, sometimes accompanied by highly trusted sources from partners and others. Google Cloud also provides tools including Prompt Engineering, Tuning, and Reinforcement Learning from Human Feedback to further ensure data factuality and reliability. This will likely result in the first generative AI applications targeted to smaller, high-impact problems, like optimizing network topologies. 

The human element

Of course, people are critical to the success of generative AI, whether that means solving problems in call centers, field service workers combining AI information with their own knowhow, marketing and creative teams brainstorming with generative AI to make new presentations and marketing materials, or operations engineers augmenting and approving an AI suggestion. We’ve built a lot of amazing technology to help augment what people cannot do: synthesize millions, if not billions, of records and sources to help energize new workflows and productivity.

Telecommunications is a fast-changing industry that is tech savvy and hungry to learn and deploy the best possible new technologies, and that includes generative AI. Each meeting with a CSP inspires new ideas, sparks more use cases, and leads to more industry-changing initiatives. It’s exciting to see this pace of change, and we are only just getting started.

We look forward to showing you more soon as we deep dive into some of the exciting CSP industry use cases in upcoming blog posts. We also invite you to find out more about how Google Cloud is partnering with CSPs around the world to deliver a holistic cloud transformation.

Local governments are embracing more modern and scalable ways to support their communities. In an effort to save both time and money, Costa Mesa Sanitary District  (“CMSD”) used machine learning to automate and streamline manhole maintenance. Manhole maintenance is an essential part of the upkeep of cities. Manholes provide critical access points for underground public utilities, allowing inspection, maintenance, and system upgrades. But failure to upkeep manholes can cause a multitude of problems, from road hazards to sewer blockages, and can make it difficult for workers to access underground public utilities, which can lead to other safety issues. Manhole maintenance is an essential part of Costa Mesa Sanitary District maintenance, but this process requires the work of an outside consultant and costs the CMSD over $100,000.

ML to the rescue 

CMSD, in collaboration with SpringML and Google Cloud, developed a project to streamline manhole maintenance by leveraging the power of machine learning (ML) to detect sewer manholes and rate their conditions. This solution saves CMSD $40,000 every year, freeing up funds for other public service projects.

Every quarter, one member of the CMSD drives a car outfitted with a GoPro camera. This car travels through the entire District area, which includes the city of Costa Mesa and small portions of Newport Beach, CA, which is about 218 street miles, and records the roads to detect approximately 5,000 manholes. At the end of the recording day, CMSD members transfer images and videos from the GoPro SD card into a local server. Then these files are automatically ingested into Google Cloud Storage for processing. From here, the machine learning algorithms detect which manholes need repair.

Google Cloud products are used throughout this project. Once the images and videos are in Google Cloud Storage, a workflow with Cloud Scheduler spins up the VM every night to detect if there are new videos on Cloud Storage. If there are, this triggers cloud machine learning, which reviews the numerous images and videos, rates each manhole, and determines if any require maintenance. 

Machine learning to detect and grade manholes

SpringML applied a very systematic approach to detecting and grading the manholes. First, image processing ensures that the region of interest is only the section of the road in front of the vehicle to avoid any privacy concerns. Then SpringML applied a 5-step process using machine learning to detect and grade the manholes.

1. SpringML developed two separate custom TensorFlow-based Mask R-CNN models. Mask-R-CNN is a deep neural network that is used for image segmentation tasks, which means it can separate different objects in an image or a video. 
   
   The first Mask-R-CNN model was created to accurately detect if an image showed a manhole cover. This was a critical step because sewer manhole covers look similar to water main covers. To make the model accurate, it was important that there be no false positives. SpringML used around 50 images of sewer manholes and other images to train and validate that the algorithm could successfully detect sewer manhole covers. 
2. Once a manhole is accurately detected, the surrounding area is masked using image processing to focus on the manhole and then the cropped images are sent to the second model which is used to detect damage in the area around the manhole, called the apron. To reduce the processing time, the second model only does the inference if a manhole cover is detected.
3. The second Mask-R-CNN model uses CMSD Guidelines to decide what types of damage contributed to the rating system. For training purposes, SpringML uses a TensorFlow-Keras inside a Virtual Machine on Google Cloud. The initial model was trained and the Keras weights were saved on Google Cloud Storage. This helped create a versioned system of the models as the model gets refined over time.
4. Then, duplicated detections are cleaned up to have a unique detection for each manhole cover.
5. Finally, Manholes are graded from a 1-5 rating, with 5 representing high damage and 1 representing low damage. 

Once cloud machine learning has analyzed the new videos and images, the final scores are stored in BigQuery. Results are then served to members of CMSD via a simple web application where they can see which manholes need to be maintained. Two staff members review the ML results and determine priorities for repairs. One of the most interesting features of this project is that the model gets continually retrained based on feedback submitted in the web application. For example, if the model inaccurately detects a manhole, a member of CMSD can mark that in the web application and their feedback is immediately used to refine the model.

This solution shows how leveraging machine learning can streamline a necessary local government project and save money and labor while being highly scalable! Not only does this system streamline the manhole maintenance process, but it also allows for more frequent review of manhole conditions and provides a historical view of how the District’s manholes change over time.

Want to learn more about Google Cloud machine learning? Check out this tutorial to  learn TensorFlow and Keras and check out more machine learning tools in our AI Platform.

Video AI is a powerful way to enable content discovery and engaging video experiences.

Here, try it out right now!

Google Cloud’s easy-to-access video AI solutions can accomplish a bunch of things. Here are a few:

Precise video analysis: Video Intelligence API automatically recognizes more than 20,000 objects, places, and actions in stored and streaming video. It also distinguishes scene changes and extracts rich metadata at the video, shot, or frame level. Use in combination with AutoML Video Intelligence to create your own custom entity labels to categorize content. Imagine being able to categorise hundreds of videos of customer interactions quickly to improve service training!

Recommended content: Build a content recommendation engine with labels generated by Video Intelligence API and a user’s viewing history and preferences. This will simplify content discovery for your users and guide them to the most relevant content that they want.

Simplify media management: Find value in vast archives by making media easily searchable and discoverable. Easily search your video catalog the same way you search text documents. Extract metadata that can be used to index, organize, and search your video content, as well as control and filter content for what’s most relevant.Imagine being able to locate insight in hundreds of enterprise videos to improve productivity and customer experience!

Easily create intelligent video apps: Gain insights from video in near real time using the Video Intelligence Streaming Video APIs, and trigger events based on objects detected. Build engaging customer experiences with highlight reels, recommendations, interactive videos, and more. Marketers, imagine being able to trigger a customer workflow, in real time, based on a live customer interactions.

Automate expensive workflows: Reduce time and costs associated with transcribing videos and generating closed captions, as well as flagging and filtering inappropriate content.

Content moderation: Identify when inappropriate content is being shown in a given video. You can instantly conduct content moderation across petabytes of data and more quickly and efficiently filter your content or user-generated content.

What can your organisation do with video AI?