Apple's HomePod smart speaker will be discontinued according to a statement the company provided to TechCrunch this evening. Existing users will receive software updates and support through Apple Care according to the company. On the U.S. website, the space gray color is listed as "Sold Out" but there are still models available in white. However, this move will not signal the end of the HomePod product line. Apple's HomePod Mini will continue to be sold. HomePod mini has been a hit since its debut last fall, offering customers amazing sound, an intelligent assistant, and smart home control all for just $99.

Apple will discontinue its original HomePod four years after first releasing the smart speaker. The Cupertino, California-based tech giant says it will instead focus on its new and smaller HomePod mini, which went on sale in November for $99. "We are discontinuing the original HomePod, it will continue to be available while supplies last through the Apple Online Store, Apple Retail Stores and Apple Authorized Resellers," Apple said in a statement, reported by TechCrunch. "We are focusing our efforts on HomePod mini." Apple didn't immediately respond Saturday to USA TODAY's request for comment.

Between discarded packaging, shipping fees, inventory shortages and damaged merchandise, returns cost retailers a fortune. In 2017, retail returns cost roughly $350 billion, and they were projected to rise to $550 million in 2020. While some returns are inevitable (with reasons ranging from defective products to late deliveries), others are entirely avoidable. In fact, 46% of shoppers surveyed in Narvar's 2019 The State of Online Returns report said their No. 1 reason for returning products was incorrect size, fit or color. Only 3% intentionally bought multiple items knowing they'd return some.

If the numbers are any indication, you might think chatbots and voice assistants were poised to take over the world. Since the start of the pandemic, nearly a quarter of businesses have increased their spending on artificial intelligence, and 75 percent plan to continue or launch new initiatives post-pandemic. Global spending on AI is expected to double by 2024. AI is quickly becoming a cornerstone of customer service especially, but consumer sentiment is mixed. Fifty percent of customers believe chatbots and VAs make it harder to resolve an issue, but 37% say they'd prefer to get immediate help from a bot than wait on a human.

AWS IoT Greengrass is software that extends cloud capabilities to local devices. This enables devices to collect and analyze data closer to the source of information, react autonomously to local events, and communicate securely with each other on local networks. Local devices can also communicate securely with AWS IoT Core and export IoT data to the AWS Cloud. AWS IoT Greengrass developers can use AWS Lambda functions and prebuilt connectors to create serverless applications that are deployed to devices for local execution. The following diagram shows the basic architecture of AWS IoT Greengrass. AWS IoT Greengrass makes it possible for customers to build IoT devices and application logic. Specifically, AWS IoT Greengrass provides cloud-based management of application logic that runs on devices. Locally deployed Lambda functions and connectors are triggered by local events, messages from the cloud, or other sources. In AWS IoT Greengrass, devices securely communicate on a local network and exchange messages with each other without having to connect to the cloud. AWS IoT Greengrass provides a local pub/sub message manager that can intelligently buffer messages if connectivity is lost so that inbound and outbound messages to the cloud are preserved. Through secure connectivity in the local network. Device security credentials function in a group until they are revoked, even if connectivity to the cloud is disrupted, so that the devices can continue to securely communicate locally. MQTT messaging over the local network between devices, connectors, and Lambda functions using managed subscriptions. MQTT messaging between AWS IoT and devices, connectors, and Lambda functions using managed subscriptions. Shadows can be configured to sync with the AWS Cloud. Automatic IP address detection that enables devices to discover the Greengrass core device. Central deployment of new or updated group configuration.

CMR Shopping Mall, a subsidiary of the CMR Group, is a known brand in Andhra Pradesh with a strong presence in textiles, jewellery, and real estate. While pandemic has put a dent on the shopping mall business, CMR is picking up momentum, with an average footfall of 4,000-10,000 every day. However, as a large retailer, CMR Shopping Mall's technology adoption was subpar. Due to the scarcity of skilled workforce amid pandemic, the retailer had to bear the brunt of fraudulent activities and inefficiency in its supply chain management. Moreover, CMR Shopping Mall was beset by price wars and was struggling with tax structure complexities.

The use of data dropout to screen out unwanted data is just one of several ways that organizations can control their data--and how much they want of it--for their artificial intelligence. It is a way to assure that the data you're using is relevant for the business problem you want your AI to address. Data scientists use data dropout in AI to eliminate upfront all data that is deemed to be extraneous to a particular AI process. For instance, if all you care about are the demographics for the state of Indiana, you can exclude the data that comes in from other states that is irrelevant to your study. The processing time for data is reduced, and the time to market for AI results is expedited and the quality and value of the data that you input into your AI application is improved.

Amazon will open its first physical store outside the US today - but the shopping experience will be a bit different. Amazon Fresh is in Ealing, London, and it is much smaller than a supermarket. It will sell prepared meals, some groceries, and Amazon devices, as well as having a counter for collecting and returning online orders. Shoppers will scan a smartphone QR code to open the store's gates and their purchases will be tallied using ceiling cameras and shelf weight sensors. The technology can also register when someone has put an item back on the shelf, if they change their mind, for instance.

Chris Fregly is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is also the founder of the Advanced Spark, TensorFlow, and KubeFlow Meetup Series based in San Francisco. Chris regularly speaks at AI and Machine Learning conferences across the world including the O'Reilly AI, Strata, and Velocity Conferences. Previously, Chris was Founder at PipelineAI where he worked with many AI-first startups and enterprises to continuously deploy ML/AI Pipelines using Apache Spark ML, Kubernetes, TensorFlow, Kubeflow, Amazon EKS, and Amazon SageMaker. He is also the author of the O'Reilly Online Training Series "High Performance TensorFlow in Production with GPUs" Antje Barth is a Developer Advocate for AI and Machine Learning at Amazon Web Services (AWS) based in Düsseldorf, Germany.

Have you ever thought about how artificial intelligence could be used to detect events during live sports broadcasts? With machine learning (ML) techniques, we introduce a scalable multimodal solution for event detection on sports video data. Recent developments in deep learning show that event detection algorithms are performing well on sports data [1]; however, they're dependent upon the quality and amount of data used in model development. This post explains a deep learning-based approach developed by the Amazon Machine Learning Solutions Lab for sports event detection using Amazon SageMaker. Our solution uses a multimodal architecture utilizing video, static images, audio, and optical flow data to develop and fine-tune a model, followed by boosting and a postprocessing algorithm.