Valve hat nach rund einem Monat in der Beta das Remote Play Together über Steam fertiggestellt. Spieler können am Sofamodus aus der Distanz teilnehmen – auch mit dem Smartphone. (Steam, Valve)
Quelle: Golem
Richtig ernst meint es Google mit seinem Spielestreamingdienst Stadia nicht – noch nicht. Das Unternehmen plant langfristig und wartet auf 5G und Titel wie Baldur’s Gate 3. Von Peter Steinlechner (Stadia, Google)
Quelle: Golem
Die Versorgung der Partner mit in 14 nm gefertigten Chips sei “extrem knapp”, sagte Intel: Der Hersteller entschuldigt sich zwar dafür, hat aber vorerst keine Lösung parat. Und auch bei AMD gibt es Probleme. (Intel, Prozessor)
Quelle: Golem
Politische Anzeigen sollen bei Google künftig nicht mehr maßgeschneidert für bestimmte Wählergruppen geschaltet werden können. Das erhöht den Druck auf Facebook-Chef Mark Zuckerberg, ebenfalls Wahlwerbung einzuschränken. (Google, Soziales Netz)
Quelle: Golem
Nutzer von Paypals Geschäftskonto können künftig auch in Geschäften darüber bezahlen: Der Finanzdienstleister führt eine kostenlose Debit-Mastercard ein. Gezahlt wird entweder über das Paypal-Guthaben oder direkt über ein verknüpftes Konto, es gibt eine monatliche Cashback-Aktion. (Paypal, Wirtschaft)
Quelle: Golem
Nicht ohne Grund nennt Ubiquiti seinen neuen WLAN-Router Alien. Er sieht recht ungewöhnlich aus und hat einen Touchscreen integriert, der diverse Informationen anzeigt. Ebenfalls verbaut: ein noch wenig verbreitetes Wi-Fi-6-Modem. (Ubiquiti, Mesh)
Quelle: Golem
Nach mehreren Jahren Arbeit kommt die Oberfläche des Firefox-Browsers von Mozilla nun ohne XBL aus. Stattdessen nutzt er Webcomponents und damit standardisierte Technik. (Firefox, Browser)
Quelle: Golem
June marked the first anniversary of Google’s AI Principles, which formally outline our pledge to explore the potential of AI in a respectful, ethical and socially beneficial way. For Google Cloud, they also serve as an ongoing commitment to our customers—the tens of thousands of businesses worldwide who rely on Google Cloud AI every day—to deliver the transformative capabilities they need to thrive while aiming to help improve privacy, security, fairness, and the trust of their users.We strive to build AI aligned with our AI Principles and we’re excited to introduce Explainable AI, which helps humans understand how a machine learning model reaches its conclusions.Increasing interpretability of AI with Explainable AIAI can unlock new ways to make businesses more efficient and create new opportunities to delight customers. That said, as with any new data-driven decision making tool, it can be a challenge to bring machine learning models into a business.Machine learning models can identify intricate correlations between enormous numbers of data points. While this capability allows AI models to reach incredible accuracy, inspecting the structure or weights of a model often tells you little about a model’s behavior. This means that for some decision makers, particularly those in industries where confidence is critical, the benefits of AI can be out of reach without interpretability. This is why we are excited to announce our latest step in improving the interpretability of AI with Google Cloud AI Explanations. Explanations quantifies each data factor’s contribution to the output of a machine learning model. These summaries help enterprises understand why the model made the decisions it did. You can use this information to further improve your models or share useful insights with the model’s consumers.Of course, any explanation method has limitations. For one, AI Explanations reflect the patterns the model found in the data, but they don’t reveal any fundamental relationships in your data sample, population, or application. We’re striving to make the most straightforward, useful explanation methods available to our customers, while being transparent about its limitations. We have received positive feedback from customers who are looking forward to applying AI Explanations:Sky“Understanding how models arrive at their decisions is critical for the use of AI in our industry. We are excited to see the progress made by Google Cloud to solve this industry challenge. With tools like What-If Tool, and feature attributions in AI Platform, our data scientists can build models with confidence, and provide human-understandable explanations.” —Stefan Hoejmose, Head of Data Journeys, SkyVivint Solar”Model interpretability is critical to our ability to optimize AI and solve the problem in the best possible way. Google is pushing the envelope in Explainable AI through research and development. And with Google Cloud, we’re getting tried and tested technologies to solve the challenge of model interpretability and uplevel our data science capabilities.” —Aaron Davis, Chief Data Scientist, Vivint SolarWellio“Introspection of models is essential for both model development and deployment. Oftentimes we tend to focus too much on predictive skill when in reality it’s the more explainable model that is usually the most useful, and more importantly, the most trusted. We are excited to see these new tools made by Google Cloud, supporting both our data scientists and also our models’ customers.” —Erik Andrejko, CTO, Wellio iRobot“We are leveraging neural networks to develop capabilities for future products. Easy-to-use, high-quality solutions that improve the training of our deep learning models are a prerogative for our efforts. We are excited to see the progress made by Google Cloud to solve the problem of feature attributions and provide human-understandable explanations to what our models are doing.“ —Chris Jones, Chief Technology Officer, iRobotExplainable AI consists of tools and frameworks to deploy interpretable and inclusive machine learning models. AI Explanations for models hosted on AutoML Tables and Cloud AI Platform Predictions are available now. You can even pair AI Explanations with our popular What-If Tool to get a complete picture of your model’s behavior–check out this blog post for more information. To start making your own AI deployments more understandable with Explainable AI, please visit: https://cloud.google.com/explainable-ai. Expanding our Responsible AI effortsAlongside tools and frameworks like AI Explanations, we continue to seek new ways to align our work with the AI Principles. This includes efforts focused on increasing transparency, and today we’re introducing model cards, starting with examples for two features of our Cloud Vision API, Face Detection and Object Detection. Inspired by the January 2019 academic paper, Model Cards for Model Reporting, these “cards” are “short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions.” Our aim with these first model card examples is to provide practical information about models’ performance and limitations in order to help developers make better decisions about what models to use for what purpose and how to deploy them responsibly. For more information and to share your feedback, we encourage you to visit modelcards.withgoogle.com.Additionally, we believe deeply in building our products with responsible use of AI as a core part of our development process. As we’ve shared previously, we’ve developed a process to support aligning our work with the AI Principles and we’ve now begun working with customers as they seek to create and support such processes for their own organizations. Our collective support of each other’s efforts will lead to more successful deployed AI, and we’ve been thrilled to work closely with HSBC–one of the world’s largest banks and a continual innovator in financial services–in this effort. By engaging in this joint process, we were able to share relevant expertise across our organizations. HSBC was impressed by the rigor and analysis we brought to the review process, as well as our commitment to ensuring safe, ethical and fair outcomes, and quickly recognized the value of such an approach. In parallel, HSBC has been developing their own responsible AI review process to ensure their future AI initiatives benefit from the same guidance and reliability as their Google Cloud deployments.Ongoing commitmentIt’s been an exciting year for so many aspects of AI, but the most inspiring breakthroughs aren’t always about technology. Sometimes, it’s the strides we take toward increased fairness and inclusion that make the biggest difference. That’s why, as the power and scope of AI increases, we remain committed to ensuring it serves all of us. Responsible AI isn’t a destination, but a journey we share with our customers and users. We hope you’ll join us. For more information about our Responsible AI Practices, please visit: https://ai.google/responsibilities/responsible-ai-practices/
Quelle: Google Cloud Platform
The app economy has enabled a huge range of unique business models to flourish. One such model is online food ordering and delivery services, in which apps leverage geo-location data to aggregate local food choices and offer personalized options to consumers.A leading company in this space is Just Eat. Launched in the UK in 2001 with a vision of ‘serving the world’s greatest menu. Brilliantly.’ The company has capitalized on the popularity of online food delivery and grown its presence across 12 markets. Just Eat acts as an intermediary between take-out food outlets and hungry customers, giving local restaurants access to a broader base of potential diners, while providing consumers with an easy and secure way to order and pay for food from their favourite restaurants. Today the company helps 27 million customers find food from more than 112,000 restaurants—everything from homemade Italian pasta, to Chinese noodle bowls, to fish-and-chips. Data is the fuel of Just Eat’s rapid growth, but it wasn’t always looked at that way. In its early days, Just Eat struggled with the deluge of information and faced fragmentation across its systems. In fact, the company realized its legacy data vendor wasn’t capable of ingesting 90 percent of the data produced by its food platform. This was incredibly frustrating for Just Eat’s analysts and data scientists, who had to waste time cleaning up sources instead of leveraging the data to create a better user experience. Just Eat turned to Google Cloud, and now uses machine learning (ML) to power sophisticated consumer recommendations on both its app and website. It also makes heavy use of features offered by Google Cloud Platform, including BigQuery for running analytics on its customer data set and Cloud Pub/Sub for messaging app users with relevant offers in real-time. Having all of Just Eat’s data in one platform has translated into real value for its customers. With Google Cloud tools, Just Eat has created its own proprietary Customer Ontology framework, which today contains 5.5 billion features that better understand consumers’ behavior and food habits, and provides insights into previous visits. Just Eat recently created an “Adventurous Index” to map its customers according to their ordering habits, enabling them to tailor their marketing and user experiences. For example, mid-adventurous customers are shown a choice of restaurants that serve their most ordered cuisine, while adventurous customers can choose from restaurants that serve a wider variety. This not only has prompted consumers to be more adventurous with their choices, but also has led to more business at a more diverse set of restaurants.Matt Cresswell, Director of Customer Platforms at Just Eat said that Google Cloud has become integral to its product delivery: “Consumer food choice is a hugely nuanced topic. We know that individuals have their own unique journeys when they use Just Eat. We’ve sought to create a truly one-to-one relationship with every customer. The changes we’ve made to the platform mean they can access the dishes they enjoy at the touch of a fingertip, and find inspiration to discover new dishes they’ll love. We’re grateful to Google Cloud for helping us support our customers on their culinary explorations.”
Quelle: Google Cloud Platform
Delivering smarter retail journeys that marry the personal service of a store visit with the wealth of choice available online has become a key ambition in the consumer goods industry. While we’ve come to expect digitally integrated shopping experiences when buying electronics or groceries, even shoppers of made-to-order products can benefit from the use of public cloud to enhance customer touchpoints across digital and physical channels.This is what DFS, the UK’s leading upholstery retailer, is now doing with Google Cloud’s support. A household name, with a 50-year pedigree and more than 5,500 employees, DFS is highly regarded for the quality of its handmade-to-order sofas and soft furnishings, and for the service customers receive when visiting its showrooms. The company operates its own distribution network, with the support of 20 distribution centers, nearly 300 delivery vehicles and over 600 delivery specialists.We’re working with DFS across its sales and distribution network, helping it to prepare for the future of retail via an unparalleled combination of cloud services, collaboration tools and digital devices.Our relationship came about due to DFS’ acquisition of Sofology in 2017. The company had some decisions to make around how it should integrate IT infrastructure and applications as a group, DFS looked at what it could learn from its new brand’s technology approach. As part of this exploration, conducted with Google Premier partner NetPremacy, it also reviewed its existing environment in light of the traffic demands during the Christmas holiday.DFS first followed Sofology’s example by trialing G Suite for employee communications, looking to effect broader culture change towards more seamless collaboration. The feedback was overwhelmingly positive, prompting adoption and roll out across its network over a six-month period in 2019. The success of G Suite led DFS to investigate further Google products, resulting in the decision to adopt two additional solutions.Firstly, DFS embraced Chrome devices to improve in-store systems and augment the customer experience. The company rolled out 1,200 Chromebooks to its retail stores in late 2019, enabling salespeople to access key information in a convenient and secure way, while interacting with customers. Technical colleagues found that the tablets could be set up in a matter of seconds, with secure login and individual accounts for each member of staff on the showroom floor.Secondly, DFS has made the decision to transition its web platform from a private cloud environment to Google Cloud Platform (GCP). Once implemented, this will allow the technology team to deploy a number of new applications, and will result in a series of benefits, including:Improved functionality and performance of DFS’ e-commerce platform. The site will have far greater capacity, providing a more responsive user experience and scalable resources to easily handle seasonal spikes in traffic.E-commerce back-end improvements, including greater flexibility and the ability to deploy major upgrades seamlessly.A commercial benefit in lowering hosting costs, compared with the previous approach. The transition to GCP took place in November 2019 and is expected to deliver a positive revenue impact over the busy Christmas season.Russell Harte, Group Technology Director at DFS, said, “Our business relies on a lot of moving parts all working in harmony. Whether a customer visits us digitally or in person–or both–there needs to be a congruence in the offers they see and the service they receive. The work we’re doing with Google Cloud will ensure the best possible experience for end users, while also bringing efficiencies to our logistical capabilities.” “As we migrate to GCP and access the range of applications available, we’re gaining a better understanding of what’s possible with the platform and how we can use it to seize the opportunity to react better to new challenges.”DFS is now looking at a range of other capabilities made possible by its new cloud environment. These include the adoption of containerization through Kubernetes for faster application development, and the use of Apigee as an integration layer to bring together supply chains from across the group and improve last-mile logistics for deliveries. The company is also putting more resources into leveraging data science capabilities within GCP infrastructure, to gain insights from large volumes of data. It’s anticipated that harnessing this data will enable DFS to predict demand and footfall more accurately, and to gain greater visibility over logistical data to improve the efficiency of customer deliveries.“The relationship with Google Cloud has been excellent,” says Russell. “It’s also setting our business up for where the retail industry is going, enabling a smarter, more customer-centric approach. They understand our business challenges and have the unique expertise to help us drive forward. We’re excited about how the partnership will grow from here.”
Quelle: Google Cloud Platform
