Today, Yandex released a new version of its search platform, incorporating two important elements:
· An upgraded version of a deep neural network based search algorithm called “Korolyov,” named after a town northeast of Moscow that has long served as the center of Russia’s space exploration program.
· Incorporating Yandex.Toloka, a mass-scale crowd-sourced platform for search assessors into Yandex MatrixNet.
“Korolyov” builds on “Palekh,” Yandex’s first neural network based search algorithm released in late 2016. The update improves how Yandex handles infrequent and complex queries, known as long-tail queries, in two distinct ways.
First, “Korolyov” is better at understanding user intent than its predecessor because it examines the entirety of web pages rather than just their headlines. Second, “Korolyov” can scale to analyze a thousand times more documents in real time than “Palekh.”
Like all modern AI-based systems, “Korolyov” improves itself with each incremental data point. Yandex’s position as the largest search engine in Russia creates a positive feedback loop for our deep neural network algorithm, which leads to superior search results for our users.
"Korolyov" results feed into MatrixNet, Yandex’s proprietary machine learning ranking algorithm, where a number of other ranking factors are considered before results are returned to a user.
Recently, MatrixNet started incorporating data from Yandex.Toloka, Yandex's crowdsourcing platform, in addition to anonymized user data to train the machine learning algorithms.
These updates help us to enhance the quality of our search services for our users. To learn more about “Korolyov,” you can watch the event broadcast in Russian only.
Starting this month, Android users in Russia are presented with a choice screen in Chrome Mobile browser allowing them to select their preferred search engine. Previously, Google search was the default search on Chrome on all Android devices; a different search engine could only be selected by accessing the application settings. Now, with the most recent version of Chrome Mobile (v.60), users are prompted to select their default search engine when the Chrome app launches. This is a huge milestone for Russian users and something we have been working towards for a long time.
Users are prompted via a new choice screen to select one of three search engines: Yandex, Google or Mail.ru when the updated Chrome Mobile app first launches:
Choice screen here has been translated into English for convenience
As one of the largest internet companies in Europe, and the leading search and mobile applications provider in Russia, access to platforms is critically important to Yandex. We are excited that Russian consumers can now easily choose their preferred search engine on their Android devices.
Yandex has been the leading search provider in Russia for over 20 years. We help mobile users when they are on the go by providing them with the highest quality and most relevant information. We have built our search and other services with a total commitment to serving Russian users’ distinct needs. As consumers are ever more dependent on their mobile devices for finding information about the world around them, we are excited that Russian users now can now easily choose the search provider best suited to their needs.
Thanks for choosing Yandex over the years! We look forward to continuing to help users better navigate the online and offline world!
Yandex is proud to announce Yandex.Radar, a new analytics tool that provides the most accurate search engine and browser usage data available for the internet market in Russia, Belarus, Kazakhstan and Turkey. Yandex.Radar allows webmasters to quickly and easily segment search engine and browser usage data by device type and operating system. For example, users can analyze Yandex Browser on different platforms.
Yandex.Radar originated out of Yandex.Metrica, the leading web analytics platform in Russia and the second largest web analytics platform in the world. Yandex.Radar uses anonymised aggregated data from all sites with a Yandex.Metrica tag. Approximately 60% of websites with Yandex.Metrica installed do not have other analytics tools that provide publicly reported data. Yandex.Metrica’s exclusive coverage creates a rich and broad dataset that allows Yandex.Radar to provide unparalleled accuracy of the provided data.
With Yandex.Metrica installed on 67% of websites in Russia, Yandex.Radar can measure 78% of the Russian internet traffic. This additional data provided by Yandex.Metrica enables significant improvement in the accuracy of our measurements and reporting compared to the other providers. The next leading free web analytics platform can only measure 35% of the traffic of the Russian internet traffic.
In order to further increase the accuracy of measurements, we carefully monitor traffic data nuances. Data is adjusted for inaccuracies that appear as a result of various technological changes. For example, during the global transition to HTTPS encryption, we introduced a correction to compensate for some older browsers losing referrers of the traffic from HTTPS to HTTP.
As an additional measure to ensure the highest possible accuracy and independence, we also exclude all traffic to Yandex sites, such as Yandex.News, Yandex.Market and Yandex.Maps. Yandex.Radar also uses sessions for its metrics rather than visitors because it results in a more accurate depiction of web usage.
This approach provides more accurate data for search and browser shares compared to other existing providers. For example, when comparing search engine market shares in Russia, there is a slight shift in share between search engines because of Yandex.Metrica’s larger and more accurate dataset.
Yandex.Metrica is very excited about the launch of this new tool and looks forward to adding new and additional features and functionality in the future. Any questions about the data or tool can be sent to firstname.lastname@example.org
By Misha Bilenko, Head of Machine Intelligence and Research
Recent developments in machine learning have accelerated its transition from a computer science research area to a technology that drives numerous customer applications. One of the most buzzed about methods leading this transition is deep learning. At Yandex, our homegrown deep neural networks are an important part of the machine learning portfolio that helps sustain our market-leading performance in search, speech recognition and synthesis, vision applications and machine translation. At the same time, we’ve also integrated many other forms of machine learning across our products and services.
One thing to remember about machine learning is that there is no singular best approach – it is a rich collection of algorithms that each have their own strengths and weaknesses for specific types of data and certain types of customer problems. Deep learning has unlocked amazing capabilities in the advancement of artificial intelligence, but, at the end of the day, it’s just one part of a much broader machine learning tech stack that also includes linear and tree-based models, factorization methods, and numerous other techniques that leverage statistics and optimization.
Gradient boosting is a machine learning algorithm that is widely applied to the kinds of problems businesses encounter every day like detecting fraud, predicting customer engagement and ranking recommended items like top web pages or most relevant ads. It delivers highly accurate results even in situations where there is relatively little data, unlike deep learning frameworks that need to learn from a massive amount of data. Gradient boosting is ideal for predictive models that analyze many different forms of data, including descriptive data formats with categorical features. In most applications, it is the most powerful “ultimate” model that integrates inputs from many different machine learning techniques, including those from deep learning models. Thus, it is the most important method in a practitioner’s tool case, one that can be used to leverage a wide range of data formats and combine a variety of more specialized models.
Today, we are thrilled to announce that we are open-sourcing CatBoost, a gradient boosting library. It is especially powerful in two ways: it yields state-of-the-art results without extensive data training typically required by other machine learning methods, and it provides powerful out-of-the-box support for the more descriptive data formats that accompany many business problems. Developed by Yandex researchers and engineers, it is the successor of the MatrixNet algorithm that is widely used within our services for ranking tasks, weather forecasting, fraud detection and making recommendations. We believe that it can be applied across a wide range of industrial machine learning tasks, in domains ranging from finance to scientific research.
CatBoost can be integrated with deep learning tools like Google’s TensorFlow, as demonstrated in the accompanying tutorials, where TensorFlow-trained models for text provide inputs to CatBoost. Models trained by CatBoost can be used in production via Apple’s Core ML framework. Apps can be built with CatBoost-trained models, bringing intelligent features directly to customers’ devices.
CatBoost delivers best-in-class accuracy unmatched by other gradient boosting algorithms today. It is an out-of-the-box solution that significantly improves data scientists’ ability to create predictive models using a variety of data sources, such as sensory, historical and transactional data. While most competing gradient boosting algorithms need to convert data descriptors to numerical form, CatBoost’s ability to support categorical data directly saves businesses time while increasing accuracy and efficiency.
Over the coming months, we will be rolling out CatBoost to benefit the majority of our Yandex services as we strive to deliver the best customer experience we possibly can. For example, today our weather forecasting tool Yandex.Weather uses MatrixNet to deliver minute-to-minute hyper-local forecasts, while in the near future, CatBoost will help provide our users with even more precise weather forecasting so people can better plan for quick weather changes.
Outside of Yandex, CatBoost is already being used by data scientists at the European Organization for Nuclear Research (CERN) in order to reduce the amount of particle identification errors in data produced by the Large Hadron Collider beauty experiment.
For 20 years now, Yandex has been pioneering innovation in machine learning and artificial intelligence to build intelligent products and services that help consumers and businesses better navigate the online and offline world. We feel it’s our duty to share our expertise in machine learning with the open-source community. By making CatBoost available as an open-source library, we hope to enable data scientists and engineers to obtain top-accuracy models with no effort, and ultimately define a new standard of excellence in machine learning. Learn more at http://catboost.yandex.
This is the letter Tigran Khudaverdyan, CEO of Yandex.Taxi, sent to the team earlier today:
I’m excited to share some important news. Yandex.Taxi and Uber have agreed to combine their businesses in Russia, Azerbaijan, Armenia, Belarus, Georgia and Kazakhstan. Together, we will continue to build a ride-sharing service that offers a viable alternative to automobile ownership or public transportation.
Here are a few stats for the combined company:
– 127 cities and 6 countries
– 35MM rides in the month of June
– $130MM of gross bookings in the month of June
Many of us who work inside Yandex feel that everyone has already switched to ride-sharing, but in reality, we are just at the beginning of this journey. Our goal is to create a platform that rivals car ownership or public transportation in accessibility and convenience.
Analysts estimate that the size of the official Russian taxi sector is approximately $8.4Bn (2016, VTB Capital). Unofficial or “gypsy cab” sector is estimated at another $1.9Bn (2015, Analytical Center of the Government of Russian Federation). That means that the combined company’s share of the taxi sector would have been only about 5-6%.
Now turning to how this will work from an integration perspective. Following regulatory approval and completion of the deal, for riders, both the Yandex.Taxi and the Uber app will operate as before. Driver apps, on the other hand, will be transitioned to a unified platform, allowing drivers to receive orders both from Yandex.Taxi and from Uber rider apps. This combined driver platform will significantly increase the number of available cars, reduce passenger wait time, and boost vehicle utilization. Drivers will be able to perform more trips per hour while passengers will continue to enjoy affordable prices.
The combined company will benefit from the robust technology stack that we have developed and the world-class navigation and mapping technologies of Yandex. Over the past year we have made several significant leaps in our routing and ride-assignment algorithms that have greatly increased vehicle utilization. For example, now, during rush hour, drivers can perform 30% more trips than before. Unifying our drivers platforms will create an even greater opportunity to improve the quality of our services and further boost vehicle utilization.
In addition, our users will have seamless global roaming across the Uber and Yandex.Taxi platforms. For example, a user of Yandex.Taxi could order an UberX directly from their Yandex.Taxi app upon arriving in London or Bangkok. An Uber user arriving in Moscow from Paris will be able to order a Yandex.Taxi straight from their Uber app. This creates one of the most convenient ride-sharing roaming agreements in the world!
In late May, as many of you have seen we demonstrated an early prototype of our driverless car technology. We will continue developing this project, drawing on the expertise of our engineers in computer vision, image recognition and machine learning. I hope we will have more to share with you in the coming months :)
The new company will also operate the UberEATS service in the region. We will bring together the great international expertise of the UberEATS team and Yandex’s expertise in mapping and pedestrian navigation to improve on the logistical complexities of online food delivery.
As part of the transaction, Uber will invest $225MM and Yandex will invest $100MM into the combined company, valuing it at $3.725Bn on a post-money basis. On a pro forma basis, Yandex will own 59.3% of the combined company, Uber will own 36.6%, and employees will own 4.1%. The two teams will be integrated together and I will serve as the CEO.
As we take this next step and I reflect on everything that we have accomplished, I want to take the opportunity to thank the Yandex.Taxi team that has built an incredible business in a short period of time. The Yandex.Taxi team is one of the strongest teams with whom I have had the privilege to work. I am very excited that we will now be joined by the strong, talented, and successful Uber team. One of our primary goals following completion will be to integrate our two teams and combine our talents.
The transaction is still subject to regulatory approvals and is scheduled to close in Q4’17.
I look forward to working together to build a new company! :)
Yandex is excited to announce it is ramping up its cloud platform initiative, Yandex.Cloud. Over the last year we have been exploring and experimenting with a cloud initiative that would combine our network of state-of-the-art data centers across Western Europe and Russia, our developer talent, and our deep expertise in machine learning and data mining to provide businesses a high-performance cloud computing platform. Today we are excited to take the next step and further expand on the Yandex.Cloud vision.
As more and more businesses migrate their services to the cloud, cloud-computing technologies are becoming an indispensable part of the enterprise. In the Russian market, the demand for cloud services is growing but there are few high-quality cloud providers serving local businesses. Yandex sees an opportunity to build a cloud solution powered by machine learning that will address this issue locally before we expand our offering globally.
“Yandex is uniquely positioned to develop a best-in-class cloud computing solution that meets the technological and business needs of enterprises today,” said Yandex CTO Mikhail Parakhin. “We have taken the time to understand the needs of enterprise customers and now we are putting the right infrastructure in place to build a cloud computing solution that delivers against these needs.”
Heading the new division is Yan Leshinsky a seasoned engineering leader who brings a wealth of world-class cloud computing expertise and specialization to Yandex. Prior to joining Yandex, Yan managed development teams at Salesforce, Amazon Web Services and Microsoft. Yan’s leadership will accelerate Yandex’s initial cloud experiments towards a highly developed solution on the cloud market.
“There are a number of things that attracted me to Yandex and this role. Yandex has access to an amazing pool of math and engineering talent in Russia, which feeds the high amount of intellectual capital at the company,” said Yan. “Yandex also has an organizational and technological framework centered on the customer. That combination of developer talent and customer focus sets Yandex apart and creates a great atmosphere to build out the Yandex.Cloud initiative.”
Yan’s appointment as the head of Yandex.Cloud is another example of Yandex’s commitment to best-in-class excellence, following Misha Bilenko’s hiring in February to head our Machine Intelligence and Research division. Over the next year, Yan will be working to further develop the Yandex.Cloud vision as well as build out the Yandex.Cloud team. While Yandex initially plans to focus Yandex.Cloud for the local market, we hope to broaden our reach to businesses across globe in the future.
Jane Zavalishina is the CEO of Yandex Data Factory, a spin-off of Yandex founded in 2014 to provide machine learning solutions to enterprises. In this post, Jane explains how YDF’s business has evolved since its launch, and why industrial AI is now in focus of its strategy.
Since its inception, Yandex Data Factory (YDF) has pioneered an innovative way to create value for companies by applying our expertise in machine learning and artificial intelligence (AI) to help solve their business needs. YDF arose as a solution to the problem many businesses faced at the peak of the big data craze. Essentially, businesses had begun amassing huge amounts of information, but were struggling to extract tangible value from this data.
The solution, of course, is in machine learning. Our parent company, Yandex, was an early leader in machine learning technology and today, machine learning powers 70 percent of Yandex’s products and services. We realised that wherever large stores of data exist, so does the opportunity to use that data to reach measurable business improvements. The same algorithms that power Yandex’s services, can be used to help other businesses improve their operations, revenues and profitability.
Over the past two years, we have worked with a number of companies across multiple industries on various successful projects. Together with our clients, we discovered the best use cases where machine learning can be applied to increase the efficiency of existing processes in a measurable way – be it predicting demand for a retail chain, or using computer vision to cut moderation costs for online service. Along the way, we accumulated a huge amount of expertise on merging data science with business.
One such case included our work with Magnitogorsk Iron & Steel Works (MMK), that marked one of the first ever collaborations of its kind between a technology company and steel company. MMK, one of the world’s largest steel producers, wanted to reduce its production costs while maintaining the same high-quality product. YDF developed a machine learning-based service that recommends the optimal amount of ferroalloys—the ingredients needed to produce specific steel grades. Our predictive system demonstrated the reduction of ferroalloy use by an average of five percent, equating to annual savings of more than $4 million in production costs, while consistently maintaining the same high quality of steel.
Similarly, we are now optimising the operations of a gas fractionation unit for a petrochemical company. Our solution recommends the fractionation unit parameters for maintaining the best performance and energy savings, decreasing costs in the process. Last week, we also signed a collaboration agreement with Gazprom Neft, an integrated oil company. We plan to apply our technologies to well drilling and completion, and other production processes. These successful efforts demonstrate the high potential for collaboration between artificial intelligence and industrial manufacturing.
The industrial sector – responsible for one-third of global GDP – has proven to be the ideal vertical, perfectly positioned for the effective application of our technologies. The industrial sector has become YDF’s focal point through the combination of our own successful application of predictive analytics with industrial data and the fit of the industry. Put simply, manufacturers know the value of optimisation at their hearts. Industrial manufacturing is also a unique cultural fit. They value measurements above opinions, they have perfected integrating new technologies in the existing processes, and they know how to estimate their effect through properly designed experiments.
For decades, the cornerstone of competitiveness in manufacturing has been centered on the optimization of existing processes, reaching for each tenth of a percent of efficiency in each step. And when all traditional optimisation means have been applied, the next efficiency leap of five to ten percent is often prohibitively expensive and equally time-consuming. These improvements typically consist of equipment upgrades with multi-million dollar investments, years spent on construction, rigorous training and implementation, and a lengthy delay before seeing any tangible financial return. Compared to this, receiving the same level of optimization via machine learning in a matter of months with minimal upfront investment is nothing short of revolutionary.
These long-term benefits extend far beyond a simple profit and loss sheet, and can help conserve both human capital and natural resources. By training machines to focus on the mundane, routine decisions that keep a factory running, artificial intelligence and machine learning allow human employees time to tackle more important tasks. By applying these technologies to oil and gas, companies not only achieve time and material savings, they can also reduce their energy consumption by up to 25 percent.
Our AI-enhanced models create endless opportunities to add value to the manufacturing industry. These benefits are especially noticeable in process manufacturing, where materials and mixtures – metals, chemicals, etc. – are produced. Essentially, these are also the industries responsible for the highest resource consumption.
The AI revolution in manufacturing is happening right now, and we are thrilled to be leading the charge. As this future becomes a reality, we’ll be there – at the forefront – blazing new trails in the industrial sector and delivering far-reaching effects for both the companies we work with and the larger communities they serve.
Yandex’s on-demand transportation service Yandex.Taxi unveils its autonomous car project. The prototype of a self-driving car the company has developed is a step towards a comprehensive set of driverless technologies for application across a wide range of industries.
The driverless car incorporates Yandex’s own technologies some of which, such as mapping, real-time navigation, computer vision and object recognition, have been functioning in a range of the company’s services for years. The self-driving vehicle’s ability to ‘make decisions’ in complex environments, such as busy city traffic, is ensured by Yandex’s proprietary computing algorithms, artificial intelligence and machine learning.
“Self-driving cars are set to revolutionalise the way we commute within a matter of a decade,” says Dmitry Polishchuk, head of Yandex.Taxi Self-Driving Project. “At this point in time, there are dozens of companies around the world building their own driverless cars, but only a few of them have components crucial for turning this project into reality. These components include a stack of reliable technologies and algorithms, engineering expertise and resources, and access to the market for self-driving vehicles. Yandex.Taxi, with the backing of Yandex, is one of the few players who can boast of possessing all of the above.”
Yandex.Taxi’s effort in developing the self-driving car technology aims at creating a fully-fledged autopilot functionality, which is described as Level 5, according to the currently universally accepted classification system for automated vehicles. This system classes all self-driving cars into levels from 0 to 5, where Level 0 means a person has full control over the vehicle, and Level 5 involves no human intervention.
Yandex.Taxi will push on with experimenting and honing the self-driving technology, together with improving maps, navigation and route planning implemented in this project. Tests on public roads are expected to kick off next year.
With Yandex.Taxi test-driving the self-driving service, Yandex looks forward to partnering with car manufacturers and other companies interested in taking the autonomous car technology to the road.
Yandex.Zen, an AI-powered personally targeted content feed based on the interests of each individual end user, was built to help mobile users on the go and in the moment with contextually relevant information. Through partnerships with Yandex.Zen, smartphone manufacturers can provide this personalized experience for their users to set their devices apart. Today we are glad to announce we are enhancing device differentiation for another global partner, Micromax Informatics, the world’s 10th largest mobile brand.
The AI and machine learning that powers Yandex.Zen provides smartphone users with suggested stories, articles and videos in their local language based on their personal tastes and choices through a user-friendly interface. The result is a superior end-user experience gained from a device that stands out from other Android devices.
Yandex.Zen will be incorporated into Micromax's AROUND experience, which integrates shopping, travel and food services in one window. Micromax, well-known as India’s largest mobile brand, helps users navigate a number of shopping and dining tasks on their devices. The Yandex.Zen integration brings an additional layer of personalization to the AROUND experience, delivering users contextually relevant information and entertainment content in Hindi, Telugu, Tamil, and English.
By complementing the AROUND experience with Yandex.Zen, Micromax offers users with a truly comprehensive and empowering mobile experience. Yandex.Zen’s feed will run as a news category alongside the leisure category (where users can order food and comparison shop), and the travel categories (used for hailing taxis, checking transportation schedule, or booking accommodations.
Artem Fokin, Yandex VP for Business Development, says of the partnership: “We are proud to be working with Micromax to help enhance the user interface of its devices through AI, for improved user experience and ultimately to achieve device differentiation within a crowded marketplace. Yandex’s software allows cult brands in the market to truly understand and engage with their consumers as the digital landscape continues to evolve.”
Mr. Rahul Sharma, Co-founder, Micromax Informatics echoed Fokin’s comments: “At Micromax, our emphasis is to drive innovations through software and services that simplify the user experience and create much values for them. A large chunk of our efforts are now concentrated on introducing products and services which act as solutions to the needs of our customers, empowering them with the latest technological innovations and eventually becoming an extension of their lifestyle.” He continued, “Given the fact that, personalisation, flexibility and simplicity are key for consumer engagement, the partnership with Yandex will help our users stay updated with news and articles as a personal news feed and have an enriched device experience.”
Yandex’s expertise in machine learning, neural networks and artificial intelligence are key components for Yandex.Zen’s simplified end user experience that help partners like Micromax achieve its goals for customers. Yandex.Zen supports multiple integration options as a part of Yandex Browser or Yandex Launcher and as a separate SDK for third party deployment.
At Yandex we believe in delivering compelling customer experiences directly to our users through our suite of intelligent products or services as well as to our partner’s customers via mobile applications such as Yandex.Zen. We are proud to say the daily Yandex.Zen usage currently stands at 20 minutes per day, on par with the average time users spend on social networks. Over the past year, Yandex has expanded our reach, keeping users informed and entertained with personalized content on their devices and delivering high-quality software for our partners. Micromax is among a respected group of smartphone manufacturers benefiting from our global partnership program, including Wileyfox, ZTE, Posh mobile and others. We look forward to the positive impact of Yandex.Zen on Micromax devices and welcome new opportunities with global partners.
To learn more about Yandex.Zen, visit https://zen.yandex.com/.
In this blog post, I would like to share my reflections on the landmark settlement between Google and the Russian Federal Antimonopoly Service (FAS) (you can read the FAS press release here).
Today is an important day for Russian consumers as Google has agreed to take significant steps that open up its Android platform in Russia. Under the terms of the settlement, 55 million Russian Android users will be offered a choice of search engines on their mobile devices. Smartphone manufacturers will also have more freedom to select the apps that they preinstall on devices.
Several years ago, it became clear that the closed nature of Google’s Android inhibited our ability to provide a search option for Russian users on the most popular mobile platform. Google required Android smartphone manufacturers to ship devices with Google search as the default search engine and to place the Google search widget on the default home screen. Google also limited the placement of competing applications on Android devices. These factors created limits for how smartphone manufacturers could access the essential Android App Store - Google Play. These requirements made it challenging for search providers and other competing applications providers to pre-install their services on Android phones. Android was limiting options for users, smartphone manufacturers, and competitors – and all together restricting innovation. Yandex requested that FAS initiate an investigation into Google’s business practices. In 2015, FAS found Google’s practices to be anti-competitive and in violation of Russian antitrust laws.
As one of the largest internet companies in Europe, and the leading search and mobile applications provider in Russia, access to platforms is critically important for Yandex. Technology platforms make it possible for us (as well as other companies) to continue a rapid pace of innovation. But this is only possible if those platforms are sufficiently open to foster competition by allowing access to third-party developers. We are excited to have reached a solution that restores these necessary elements to ensure a more dynamic and competitive ecosystem.
I am thankful to the Federal Antimonopoly Service for applying the law in a manner that effectively and efficiently restores competition to the market for the benefit of Russian users.
I also want to thank Google, not only for their cooperation, but also for recognizing the value of openness. We have always thought Google plays a constructive role in the Russian market. Competition breeds innovation. It’s our desire to participate in a market where users can choose the best services available.
For the past 20 years, it has been our mission to help users better navigate the online and offline world. When I founded Yandex in 1997 with Ilya Segalovich, Elena Kolmanovskaya, and others, we shared a vision for the way search technologies would help people find information on the Internet. Over the years, our machine learning capabilities have grown, and with it our aspirations. Our mobile services, maps, eCommerce, classifieds, and on-demand transportation services have expanded our ability to help users on the go and in the moment with contextually relevant information.
I’m excited, together with the entire team at Yandex, to continue building products and services that deliver exceptional customer experiences. With open platforms, our future is bright. With choice, the possibilities are endless.