AWS has entered the red-hot realm of generative AI with the introduction of a suite of generative AI development tools. The cornerstone of these is Amazon Bedrock, a tool for building generative AI applications using pre-trained foundation models accessible via an API through AI startups like AI21 Labs, Anthropic, and Stability AI, as well as Amazon's own Titan family of foundation models (FMs). Bedrock offers serverless integration with AWS tools and capabilities, enabling customers to find the right model for their needs, customize it with their data, and deploy it without managing costly infrastructure. Amazon states that the infrastructure supporting the Bedrock service will employ a mix of Amazon's proprietary AI chips (AWS Trainium and AWS Inferentia) and GPUs from Nvidia. AWS is positioning Bedrock as a way to democratize FMs, as training these large models can be prohibitively expensive for many companies.

As the business world becomes ever more digital-focused, getting the most out of your data has never been more important. Artificial intelligence (AI) and Machine Learning (ML) technology is proving an increasingly useful ally for companies of all sizes, and Amazon Web Services (AWS) is looking to position itself at the forefront of this booming space. At its recent AWS re:Invent event, the company put AI and ML firmly in the spotlight when outlining its plans for the future, showcasing a huge range of use cases, customer stories, and new releases all focused firmly around AI and ML. But how big a role can the technologies really play, both for AWS and for your business? TechRadar Pro spoke to Swami Sivasubramanian, Vice President of AWS Data and Machine Learning, to find out how much is hyperbole, and how much is true innovation.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. Artificial intelligence (AI) and machine learning (ML) workloads can run in any number of locations including on-premises, at the edge, embedded in devices and in the cloud. Amazon Web Services (AWS) is hoping that more often than not organizations will choose the cloud, where it is offering a growing array of services. At the AWS re:invent 2022 event in Las Vegas today, the company detailed parts of its AI/ML strategy and announced a dizzying lineup of feature updates and new services to help organizations to better use the cloud for data science. The cornerstone of the AWS AI/ML portfolio is the SageMaker suite of services.

On the second day of Amazon Web Services (AWS) re:Invent, Swami Sivasubramanian, vice president of AWS Data and Machine Learning (ML) revealed the latest innovations during his keynote. To start, Sivasubramanian announced the launch of Amazon Athena for Apache Spark, which he said will provide organizations with a more intuitive way to run complex data analytics. He noted that Apache Spark will run three times faster on AWS. The next product announcement was of the general availability of Amazon DocumentDB Elastic Clusters, a fully-managed solution to quickly scale document workloads of any size. Amazon SageMaker now supports Geospatial ML, giving access to multiple new kinds of data.

To further strengthen our commitment to providing industry-leading coverage of data technology, VentureBeat is excited to welcome Andrew Brust and Tony Baer as regular contributors. Amazon Web Services (AWS) is on a mission to enable organizations -- including startups, enterprises and government agencies -- to become more agile and innovate faster at lower costs. Last year, an AWS executive told VentureBeat that a priority for AWS in 2022 will be automation at scale, allowing customers to bolster the security of their cloud environments. To advance its mission, AWS today unveiled innovations across several services, including databases, machine learning, IoT and application development. The announcement came yesterday at the AWS Summit, San Francisco, where Swami Sivasubramanian, VP of data, analytics and ML services at AWS, offered details on the new product offerings in a keynote address.

In a follow-up to new compute, network and data service offerings announced by Amazon Web Services (AWS) CEO Adam Selipsky, AWS vice president of AI, Swami Sivasubramanian, pulled the covers off some updates to database, machine learning and serverless offerings. Taking a cue from Selipsky's theme of simplifying AWS' array of services in order to make them easier to consume for developers and enterprises, Sivasubramanian announced three new updates to AWS' plethora of database offerings. They include a new managed database service for business applications that allows developers and enterprises to customise the underlying database and operating system; a new table class for Amazon DynamoDB designed to reduce storage costs for infrequently accessed data; and a service that uses machine learning to better diagnose and remediate database-related performance issues. The new managed database service, Amazon RDS (Relational Database Service) Custom, is aimed at customers whose applications require customisation at the database level and thus are responsible for administrative tasks such as provisioning, database setup, patching and backups that take up a lot of time, Sivasubramanian said. Amazon RDS Custom automates these administrative processes while allowing customisation to the database and underlying operating system these applications require, Sivasubramanian said.

Amazon Web Services Inc. has commissioned its first-ever major art piece, a site-specific sculpture powered by artificial intelligence and designed by artist and architect Suchi Reddy that will be the centerpiece of the Smithsonian's "Futures" exhibit. The artwork, called "me you," was unveiled today in the 90-foot-tall central rotunda of the Smithsonian's historic Arts and Industries Building in Washinton, D.C. It's an important locale as America's first national museum and because the interactive sculpture itself is nearly two stories tall. The sculpture takes up the center of the room, with a base that appears to have large fiber-optic cables sticking out of it toward people with inviting circular interfaces that Reddy (pictured, right) called "mandalas." Rising from the center of the sculpture is a broad, segmented series of panels called a "totem" upon which colorful kinetic patterned lights flow upward, representing interactive futures spoken to the artwork by the public. The idea of the artwork is to present how humans and technology interface and evolve together, Reddy told SiliconANGLE in an interview while she demonstrated the sculpture in action.

The artificial intelligence at the heart of a new art exhibit, "me you," does not judge you necessarily, but it does analyze and interpret what you have to say. Sponsored by Amazon Web Services, the sculpture by artist Suchi Reddy listens to what you have to say about the future and renders your sentiment in a display of colored lights and patterns. The artwork is a centerpiece of a new exhibit at the Smithsonian Arts and Industries Building, which is opening to the public for the first time in 20 years. The exhibition, called Futures, opens Nov. 20. Viewers are invited to interact with the sculpture, which listens for the words "My future is …" at several circular listening posts integrated into the sculpture.

Machine learning is one of the most transformative technologies of this generation, but the tech community only is "scratching the surface" when it comes to what's possible, according to Swami Sivasubramanian, vice president of artificial intelligence and machine learning at Amazon Web Services. Machine learning is transforming everything from the way business is conducted to the way people entertain themselves to the way they get things done in their personal lives. "Entire business processes are being made easier with machine learning," Sivasubramanian said during AWS' Machine Learning Summit last week. Supply chain analysts can have faster and more accurate forecasts. And manufacturers can easily spot defects in products." And the barriers to entry have been significantly lowered, enabling builders to quickly apply ML to their most pressing challenges and biggest opportunities. "ML is improving customer experience, creating more efficiencies in operations and spurring new innovations and discoveries like helping researchers discover new vaccines and enhancing agricultural output with better crop monitoring," Sivasubramanian said. "But we are just scratching the surface about what is possible, and there is so much invention yet to be done.

As machine learning disrupts more and more industries, it has demonstrated its potential to reduce time spent by employees on manual tasks. However, training machine learning models can take months to achieve, creating excessive costs. With this in mind, AWS vice-president of machine learning, Swami Sivasubramanian used his keynote speech at AWS re:Invent to announce new tools that aim to speed up operations and save costs. Sivasubramanian went through five tenets for machine learning that AWS observes, which acted as vessels for further explanations of use cases for the new tools. Firstly, Sivasubramanian explained the importance of providing firm foundations, vital for freedom of creativity.