Amazon Kendra is an intelligent search service powered by machine learning (ML), enabling organizations to provide relevant information to customers and employees, when they need it. Amazon Kendra uses ML algorithms to enable users to use natural language queries to search for information scattered across multiple data souces in an enterprise, including commonly used document storage systems like Microsoft OneDrive. OneDrive is an online cloud storage service that allows you to host your content and have it automatically sync across multiple devices. We're excited to announce that we have updated the OneDrive connector for Amazon Kendra to add even more capabilities. For example, we have added support to search OneNote documents.

The artificial intelligence for IT operations (AIOps) platform market is moving faster than can really be imagined, both in terms of vendors as well as the capabilities that make up AIOps solutions. This was driven by a continued increase of unmanageable data volumes and continued desire for business insights. To address these changes since the last Forrester Wave on AIOps two years ago, Forrester published the AIOps Reference Architecture, which defines the 18 functions required to deliver AIOps solutions. Additionally, we published guidance on the different perspectives of how to approach AIOps to address a growing organizational need to help put AIOps into practice. The Forrester Wave: Artificial Intelligence For IT Operations, Q4 2022 is focused on technology-centric AIOps solutions, which was a key element of the entry criteria in addition to single-code base and/or UI, stand-alone product, and domain-agnostic interoperability. Operationally aligned and process-centric AIOps vendors were not included in this research but will be highlighted in future Forrester research efforts.

To accelerate progress in AI, PyTorch is moving to a new, independent PyTorch Foundation, under the Linux Foundation umbrella. The project will join the Linux Foundation with a diverse governing board composed of representatives from AMD, Amazon Web Services, Google Cloud, Meta, Microsoft Azure, and Nvidia, with the intention to expand over time. The PyTorch Foundation will act as a responsible steward for the technology and support PyTorch through conferences, training courses, and other initiatives. The foundation's mission is to drive adoption of AI tooling by fostering and sustaining an ecosystem of open source, vendor-neutral projects with PyTorch. It will democratize state-of-the-art tools, libraries, and other components to make these innovations accessible to everyone.

Since 2016, when we partnered with the AI community to create the PyTorch framework for AI research, open collaboration has been essential to its success. With thousands of contributors who have built more than 150,000 projects on it, PyTorch has become one of the leading platforms for research and production across the AI community. Today, Mark Zuckerberg announced that the project will transition to a newly launched PyTorch Foundation, which will be part of the nonprofit Linux Foundation, a technology consortium whose core mission is the collaborative development of open-source software. The creation of the PyTorch Foundation ensures that decisions will be made in a transparent and open manner by a diverse group of board members for many years to come. The governing body will be composed of representatives from AMD, Amazon Web Services, Google Cloud, Meta, Microsoft Azure and Nvidia, with the intention to expand further over time.

Today we released .NET 7 Preview 5. This preview of .NET 7 includes improvements to Generic Math which make the lives of API authors easier, a new Text Classification API for ML.NET that adds state-of-the-art deep learning techniques for natural language processing, various improvements to source code generators and a new Roslyn analyzer and fixer for RegexGenerator and multiple performance improvements in the areas of CodeGen, Observability, JSON serialization / deserialization and working with streams. If you're on macOS, we recommend using the latest Visual Studio 2022 for Mac preview. Now, let's get into some of the latest updates in this release. The goal of observability is to help you better understand the state of your application as scale and technical complexity increases. The exposed methods can be used in performance critical scenarios to enumerate the Tag objects without any extra allocations and with fast items access.

As the field of machine learning has grown very rapidly, so too have its many subfields. In the last decade, machine learning on graphs has taken off, especially with impressive advances in approaches to graph deep learning in recent years. This has been a major boon to the application areas that process graph-structured data, such as computational chemistry, transportation networks, social networks, recommender systems, or healthcare. Graphs can also be seen as a generalisation of other domains (e.g., grids or sets), and using graph-based ML architectures where established models such as CNNs and RNNs have traditionally been used is often advantageous. However, growth also brings along a number of challenges.

Unleash the power of machine learning (ML) through hands-on learning and compete for prizes and glory. The AWS DeepRacer League is the world's first global autonomous racing competition driven by reinforcement learning; bringing together students, professionals, and enthusiasts from almost every continent. I'm Tomasz Ptak, a senior software engineer at Duco, an AWS Machine Learning Hero, an AWS DeepRacer competitor (named Breadcentric), a hobbyist baker, and a leader of the AWS Machine Learning Community on Slack, where we learn, race, and help each other start and grow our adventures in the cloud. It's my pleasure to unveil the exciting details of the upcoming 2022 AWS DeepRacer League season. It's a complete program that has helped over 175,000 individuals from over 700 businesses, educational institutions, and organizations begin their educational journey into machine learning through fun and rivalry.

Ministers and public bodies must reveal the architecture behind algorithms that influence exam results, housing benefit allocations and pothole repairs, under new transparency standards. The UK government has published a transparency standard for algorithms, the series of instructions that a computer follows to complete a task or produce a single outcome. Algorithms have become the focus of increasing controversy, whether through their role in deciding A-level results last year or making decisions about benefit claims. Under the new approach, government departments and public sector bodies will be required to explain where an algorithm was used, why it was used and whether it achieved its aim. There will also be an obligation to reveal the architecture behind the algorithm. It will be tested by several government departments and public sector bodies in the coming months before being reviewed again and formally launched next year.

Back in July, I wrote about how Amazon Robotics is using AWS to build the next generation of robots in our warehouses. In that post, I shared how even the most seemingly mundane aspects of a system are crucial to achieving fully functional robots as opposed to just automation. "In the coming years, we will see a rapid evolution of robotic capabilities, reliability, and safety, all driven by the seamless integration of emerging technologies, cloud, and newly connected hardware." Now, the reality is that this isn't so much of a prediction as it as an observation of what's already happening all around us. Robots are becoming more and more commonplace in our world, playing roles in everything from performing surgery, to cleaning the floor, to teaching young children.

With ODSC West 2021 just around the corner, we couldn't be more excited to announce our lineup of keynote and featured speakers. Collectively these speakers have founded companies, won numerous awards, and published hundreds of articles. Before becoming a Professor in the Computer Science Department at Stanford University, Carlos Guestrin's roles included the Amazon Professor of Machine Learning at the Computer Science & Engineering Department of the University of Washington, the Finmeccanica Associate Professor at Carnegie Mellon University, and the Senior Director of Machine Learning and AI at Apple. He is also the co-founder of Turi, Inc, which developed a platform for developers and data scientists to build and deploy intelligent applications. Lama Nachman's research at Intel Labs is focused on creating contextually aware experiences that understand users through sensing and sense-making, anticipate their needs, and act on their behalf.