Over the decades, technology has helped us organize large amounts of physical information in ways that are streamlined, efficient, and easily accessible. Rows upon rows of encyclopedias are no longer needed; simply punch in or speak a query into Google Search and find numerous results at your fingertips. There’s no need to haul around cases of CDs or cassettes either, when you can access hundreds of thousands of songs on music streaming services. 

We wanted to bring that same level of accessibility to one specific type of publication: the printed car manual. Here’s how we shifted the paper manual to become an easy-to-access, voice-activated digital experience for owners of the all-new Sienna. It’s helped this resource become just as modern and useful as Toyotas themselves.

Putting cloud technology in the driver’s seat

Far too often, the printed car manual remains unused, collecting dust in the glove compartment—that is, until it’s needed during a roadside emergency or to solve the meaning of a mysterious light popping up on the dashboard. Even then, thumbing through hundreds of pages during high-stakes moments can be stressful. On top of that, these manuals can be expensive to print and update.

Digital versions, such as a PDF file, are a nice start. But, they’re often little more than reformatted flat documents. In poor visual conditions on the side of a road, the last thing a driver wants to do is squint at a phone or scroll through pages of tiny text. And similar to printed manuals, digital versions don’t facilitate ongoing, real-time conversations between drivers and automakers when it matters the most; they’re often only text and pictures, and at best, schematic drawings.

With that in mind, we set out to elevate and personalize the car manual by creating a voice-activated digital owner’s manual experience, all powered by Google Cloud. A voice-based assistant was the clear choice because, as it felt like the most intuitive, natural option, especially while driving. In fact, 51% of U.S. adults have used a voice assistant while driving, and 95% of all drivers expect to use a voice assistant in the next three years. 

We’re now putting this technology on the road by powering the Toyota Driver’s Companion for the all-new 2021 Toyota Sienna model. Accessible through the existing Toyota app, this companion provides real-time assistance, any time of the day. Drivers can ask questions and the companion will efficiently provide helpful answers in convenient ways, from voice to 3D walkthroughs and explorable environments. 

What a modern car manual should do

The Toyota Driver’s Companion has interactive features to help drivers discover the Sienna’s dashboard, set up the car’s interior and exterior appearance, better understand vehicle maintenance, and explore Dynamic Radar Cruise Control, Lane Departure Alert and other features. 

Here are a few other additional key features to call out within the Toyota Driver’s Companion: 

• An easily accessible virtual voice through the Toyota Driver’s Companion lets app users ask personal questions about their 2021 Sienna such as “what’s the height of my car?” and receive immediate answers either by voice, display or interactive input. 
• The manual automatically connects with the purchased vehicle’s VIN number to create a completely personalized experience, curated specifically for the driver. For example, if an unfamiliar light on the dashboard pops up, the Toyota Driver’s Companion can help identify the light’s meaning.
• Interactive hotspots throughout the vehicle’s interior let drivers explore the cabin virtually. Drivers can discover button functionalities, find specific dials, and learn more about car functions, such as how to slide seats or open doors, to become acclimated with their new vehicle.

To bring this experience to life, we tapped into some of our key Google Cloud solutions:

• APIs powered by Google Cloud artificial intelligence technology make accessing specific vehicle information easy and effortless, by leveraging Google’s natural language processing:
  • Google Cloud DialogFlow API serves as the decision tree that gives intelligence for both finding an answer for a question, i.e., how the Companion responds to the end user’s questions. 
  • One of our Text-to-Speech APIs—called Wavenet—creates the Companion’s realistic voice. 
  • And finally, our Speech-to-Text API “listens” to the user’s voice and finds the correct information to craft responses. That means a driver can ask a question multiple ways, and the Companion will still respond with the right answer. 

Our Firebase mobile app dev platform gleans analytic insights that help improve the overall experience and services for OEMs and drivers alike.https://www.youtube.com/embed/66QxWS-PzIM?enablejsapi=1&

We’re encouraging better consumer experiences by providing faster access to fresh information, in a natural, accessible format—voice. But these new voice-activated experiences aren’t only an opportunity to help out drivers; it’s also about strengthening connections between drivers, their vehicles, and automakers, too. 

Our hope is that through this information exchange, drivers can provide feedback on the most frequently misunderstood features, enabling OEMs to address questions early on. By understanding the most requested features, OEMs can also predict and inform driver questions about features. We’re incredibly excited to help make the driver’s experience more connected and helpful.

While the promise of artificial intelligence transforming the manufacturing industry is not new, long-ongoing experimentation hasn’t yet led to widespread business benefits. Manufacturers remain in “pilot purgatory,” as Gartner reports that only 21% of companies in the industry have active AI initiatives in production. 

However, new research from Google Cloud reveals that the COVID-19 pandemic may have spurred a significant increase in the use of AI and other digital enablers among manufacturers. According to our data—which polled more than 1,000 senior manufacturing executives across seven countries—76% have turned to digital enablers and disruptive technologies due to the pandemic such as data and analytics, cloud, and artificial intelligence (AI). And 66% of manufacturers who use AI in their day-to-day operations report that their reliance on AI is increasing.

The top three sub-sectors deploying AI to assist in day-to-day operations are automotive/OEMs (76%), automotive suppliers (68%), and heavy machinery (67%).

In fact, Bryan Goodman, Director of Artificial Intelligence and Cloud, Ford Global Data & Insight and Analytics shares, “Our new relationship with Google will supercharge our efforts to democratize AI across our business, from the plant floor to vehicles to dealerships. We used to count the number of AI and machine learning projects at Ford. Now it’s so commonplace that it’s like asking how many people are using math. This includes an AI ecosystem that is fueled by data, and that powers a ‘digital network flywheel.’”

Moving from edge cases to mainstream business needs

Why are manufacturers now turning to AI in increasing numbers? Our research shows that companies who currently use AI in day-to-day operations are looking for assistance with business continuity (38%), helping make employees more efficient (38%), and to be helpful for employees overall (34%). It’s clear that AI/ML technology can augment manufacturing employees’ efforts, whether by providing prescriptive analytics like real-time guidance and training, flagging safety hazards, or detecting potential defects on the assembly line.

In terms of specific AI use cases called out by the research, two main areas emerged: quality control and supply chain optimization. In the quality control category, 39% of surveyed manufacturers who use AI in their day-to-day operations use it for quality inspection and 35% for product and/or production line quality checks. At Google Cloud, we often speak with manufacturers about AI for visual inspection of finished products. Using AI vision, production line workers can spend less time on repetitive product inspections and can instead focus on more complex tasks, such as root cause analysis. 

In the supply chain optimization category, manufacturers said they tapped AI for supply chain management (36%), risk management (36%), and inventory management (34%).

In our day-to-day work, we’re seeing many manufacturers rethink their supply chains and operating models to better accommodate for the increased volatility that has been brought about by the pandemic and support the secular trend of consumers asking for increasingly individualized products. We’ll share more on deglobalization in the third installment of our manufacturing insights series.

AI use differs by geography, but not for the reasons you may think

The extent to which AI is already being used today varies quite strongly between geographies, according to our research. While 80% and 79% of manufacturers in Italy and Germany respectively report using AI in day-to-day operations, that percentage plummets in the United States (64%), Japan (50%) and Korea (39%).

It’s tempting to state this disparity is due to an “AI talent gap.” Although the most common barrier, just a quarter (23%) of manufacturers surveyed believe they don’t have the talent to properly leverage AI. Cost, too, does not appear to be a roadblock (21% of those surveyed). Rather, from our observations, the missing link appears to be having the right technology platform and tools to manage a production-grade AI pipeline. This is obviously the focus of our efforts and others in the space, as we believe the cloud can truly help the industry make a step change.

Looking ahead: The Golden Age of AI for manufacturing

The key to widespread adoption of AI lies in its ease of deployment and use. As AI becomes more pervasive in solving real-world problems for manufacturers, we see the industry moving away from “pilot purgatory” to the “golden age of AI.” The manufacturing industry is no stranger to innovation, from the days of mass production, to lean manufacturing, six sigma and, more recently, enterprise resource planning. AI promises to bring even more innovation to the forefront. 

To learn more about these findings and more, download our infographic here and our full report here. 

^{Research methodology
The survey was conducted online by The Harris Poll on behalf of Google Cloud, from October 15 – November 4, 2020, among 1,154 senior manufacturing executives in France (n=150), Germany (n=200), Italy (n=154), Japan (n=150), South Korea (n=150), the UK (n=150), and the U.S. (n=200) who are employed full-time at a company with more than 500 employees, and who work in the manufacturing industry with a title of director level or higher. The data in each country were weighted by number of employees to bring them into line with actual company size proportions in the population. A global post-weight was applied to ensure equal weight of each country in the global total.}

Powered by Vertex AI (Google Cloud’s platform for accelerating development and deployment of machine learning models into production), SAVI (Semi Automated Vision Inspection)1 is transforming surgical instrument identification and cataloging, leading to fewer canceled surgeries and easing pressure on surgery waitlists.

Max Kelsen, an analytics and software agency that specializes in machine learning, has worked closely with Google Cloud and Johnson & Johnson MedTech to create a system that can manage tens of thousands of individual devices, their characteristics, and how they apply to each set or tray used by a surgeon. SAVI does this while delivering a one in 10,000 real-world error rate, much faster and more accurately than manual processes currently in use across the industry. Implementing SAVI can also unlock end-to-end visibility and traceability across the surgical set supply chain and provide advanced analytics and insights.

Eliminating time-consuming manual processes

Surgeons need a large number of specialist instruments and devices to complete complex, delicate procedures. Because each tray of these instruments can typically cost more than $350,000, and having every type of set on shelf at every surgical facility is not feasible, manufacturers generally loan them to hospitals for procedures, such as inserting one of the manufacturers’ implants into a patient’s knee. Once a procedure is complete, the hospital returns the instrument tray to the manufacturer for storage and re-distribution to other hospitals as needed.

Each time a hospital returns a tray, the manufacturer needs to check that each instrument is there, correctly placed, cleaned, and fit for the purpose of the next procedure. As each set may hold more than 400 instruments, completing this process manually is complex and time-consuming. While each tray is checked before and after surgery at the hospital, and again when it arrives and leaves the manufacturer’s facility, Max Kelsen finds that 5% of surgeries can still be affected by missing, broken or bent instruments. This has a severe downstream impact on private hospitals in particular, directly affecting patient safety and outcomes; in Australia, for example, around 60% of surgeries are performed in private hospitals.

Johnson & Johnson MedTech has 60,000 surgical trays across the Asia-Pacific, and loans these trays out about 100,000 times per month. The manufacturer approached Max Kelsen to help design and develop a solution to make the supply chain more efficient, and to give more visibility into asset movement. As a Google Cloud Partner specializing in applying machine learning at scale in healthcare contexts, Max Kelsen had the expertise and track record to meet Johnson & Johnson MedTech’s need for a globally scalable solution that was engineered for quality and performance.

The first step was to establish a baseline for the project by determining how long the manufacturer’s team took to process each tray, and to set an efficiency number. We then spent six months determining and evaluating how to deliver a robust, accurate solution that outperformed current manual and labor-intensive methods in processing instruments and trays, globally. Our work included extensive technical feasibility research involving a representative sample for the variety and complexity of sets, trays, and devices needed for different types of surgery, including orthopedics, spinal trauma, and maxillofacial groups.

Working with Google Cloud to accelerate and de-risk the project

This is a familiar problem that is industry-wide. The issue has been widely explored and tried with a number of technologies over several years without producing the scalability and performance results required to make this an appropriate and feasible solution. Google Cloud partnered with Max Kelsen to accelerate and de-risk this large and strategic project for a mutual customer.

Technical feasibility took four months, prior to a year-long production pilot of SAVl in a distribution center in Queensland that services over 100 hospitals. After obtaining enough real-world data and experience to validate that the solution was as scalable and as accurate as needed, an Asia-Pacific rollout of the system commenced. SAVI is now live across Johnson & Johnson MedTech’s operations in Australia, New Zealand, and Japan, garnering recognition with a JAISA excellence award.

Google Cloud machine learning is integral to SAVI. Google Cloud’s technologies were a big differentiator for Max Kelsen’s engineering team in delivering the breakthroughs needed at scale, and in production, to meet Johnson & Johnson MedTech’s needs.

Reducing checking and documentation time

Running SAVI in Google Cloud has reduced the time Johnson & Johnson MedTech needs to check and document inspections of these surgical instrument sets by over 40%. The application also delivers consistent measurable quality that is often hard to measure at scale when using manual processes. During the pandemic, the application enabled Johnson & Johnson MedTech to operate with a lower headcount for the same volume output, enabling the organization to quickly service a backlog of waiting list surgeries.

In addition, the automation delivered with SAVI has reduced the time required to bring technicians up to speed on quality control processes, from eight to 12 months down to just three months, enhancing productivity and performance while delivering a more robust workforce.

So how does SAVI work in a real-world context? SAVI is deployed via a tablet and a web-based application incorporates an API to photograph the medical device trays, as shown below. Max Kelsen captures the photograph and sends it to a range of different services, via an API endpoint hosted on Google Cloud:

• Image information is stored in Cloud Storage
• Data relating to the trays is stored in Cloud SQL for PostgreSQL
• APIs and web UI components run in CloudRun
• Analytics data is stored within BigQuery

Once this tray and device onboarding stage is completed, the next step is to perform inferences from the images and data. By hosting online models with Kubeflow model serving on GKE, we enable a model to identify all the instruments in a tray at low latency.

Vertex AI Workbench notebooks are used for data exploration and modeling. Kubeflow training pipelines hosted on GKE are executed to produce machine learning models for specific surgical instrument sets. Several hundred machine learning models are then hosted with Kubeflow model serving on GKE, with state and analytics managed using Firebase. Using machine learning to infer from images whether any devices are incorrectly placed, dirty, or otherwise not fit for purpose, the data is then returned to the tablet for the user to respond accordingly.

Based on our success to date with SAVI, it is now available on Google Cloud Marketplace to help healthcare organizations achieve machine learning-powered efficiencies across a range of use cases, and ultimately improve patient safety and outcomes.

Not to be confused with the usage of Visual Inspection Model (Assembly) available in Vertex AI Vision

Last week we announced the private preview of Duet AI for Google Cloud, an always-on AI collaborator that uses generative AI to provide help to developers and cloud users. This article gives you a detailed look at Duet AI for developers, showing how Duet AI can help provide developers with real-time code suggestions, chat assistance, and enterprise-focused customization. You can sign-up here to join our waitlist for Google Cloud’s AI trusted tester program.  

We believe that addressing these use case​​s with large language models (LLMs) will usher in a major leap in productivity in enterprise development. Duet AI uses Codey, a family of code models built on PaLM 2. 

Developers continuously seek ways to improve their productivity and, over the past few decades, these efforts have resulted in massive productivity leaps due to technological changes. From advanced debuggers and online developer communities, to modern IDEs/notebooks and cloud computing, each advance brought massive changes in productivity. Despite these improvements, developers still face numerous challenges, some of which are unique to cloud development:

• Disruptive context switching and friction when integrating a new tool or service
• Excessive time spent on repetitive tasks
• Time required to understand a new code base or project
• Large cognitive workload when working on large code bases or complex APIs

Duet AI for developers focuses on challenges and tasks across the development lifecycle:

Code/Boilerplate Generation — Developers can describe the tasks they have in mind as a comment or function name, such as creating a Cloud Pub/Sub topic. Duet AI will generate a reference implementation that can be reviewed and modified, so developers don’t need to spend time reading through multiple documentation pages.

Inline Code Completion — To reduce the time spent on repetitive tasks and minimize the cognitive workload of tasks such as writing repetitive code or retrieving variable names, Duet AI provides intelligent, context-aware code completion, helping reduce the time spent on coding and enhancing the quality of the written code.

Enterprise Customization — Organizations frequently have massive code bases and specific recommended frameworks and best practices, which generic code assistance solutions may not be best positioned to support. With Vertex AI, developers will be able to tune and customize the underlying models and connect them to the Duet AI experience, allowing for assistance optimized to the needs of the organization.

Code Explanation — Developers spend significant time and effort reading and understanding code written by their peers or external contributors. To help assist in this process, Duet AI for code assistance provides an “Explain this code” option available whenever a developer selects their code, allowing them to more quickly understand, map, and navigate unfamiliar code bases.

Code security guardrails — Code generated by Duet AI can also be scanned for vulnerable dependencies via Source Protect, helping surface known public vulnerabilities impacting code, along with suggested fixes when available, bringing additional security.

By harnessing the power of AI-driven developer assistance such as the one provided by Duet AI for developers, businesses can unlock unprecedented levels of productivity and efficiency in software development, paving the way for a new era of innovation and growth.

These early features of Duet AI for Google Cloud will be available for limited users and we will be expanding access very soon. Sign up here to join Google Cloud’s AI Trusted Tester Program.