Retailers are now applying AI, ML, and robotics in significant parts of the value chain. Above all, AI technologies could eliminate many manual activities in assortments, promotions, and supply chains. The three most remarkable opportunities in the short to medium term are promotions, arrangement, and replenishment. Significant retailers are trying different things with AI around these areas. "Digital native" e-commerce organizations are driving the way, using AI to anticipate trends, optimize advanced warehousing and logistics, set costs, and customize advancements and promotions.
In 2017, the Economist stated that the world's most valuable resource is no longer oil, but data. Four years later, this concept is only increasing in truth. Thanks to the revolutionary promises of 5G, artificial intelligence (AI) and machine learning (ML) possibilities are transforming the value of the data collected on consumers and our habits every single day. With 5G usage predicted to explode in coming years with over 1 billion 5G connections by 2023, the possibilities of AI and ML solutions are seemingly becoming limitless. Gone are the days when your mobile phone or laptop are the only devices collecting your data.
As companies welcome more autonomous robots and other heavy equipment into the workplace, we need to ensure equipment can operate safely around human teammates. In this post, we will show you how to build a virtual boundary with computer vision and AWS DeepLens, the AWS deep learning-enabled video camera designed for developers to learn machine learning (ML). Using the machine learning techniques in this post, you can build virtual boundaries for restricted areas that automatically shut down equipment or sound an alert when humans come close. For this project, you will train a custom object detection model with Amazon SageMaker and deploy the model to an AWS DeepLens device. Object detection is an ML algorithm that takes an image as input and identifies objects and their location within the image.
As of April 5, the ionvac SmartClean 2000 robot vacuum is on sale at Walmart for just $99 -- that's 45% off its usual retail price of $179.88. Buying a robot vacuum is truly a "get what you pay for" experience where the less you're willing to spend, the fewer bells and whistles you'll find among the options in your price range. Dip under the $200 mark, and your robot vacuum "is mostly going to act as a supplemental cleaning device" to a beefier upright model, writes Mashable shopping reporter Miller Kern. That's where the ionvac SmartClean 2000 is different: It's spec lineup is stacked with features like mobile app integration, smart assistant support, automatic charging, and a three-stage cleaning system with a whopping 2000pa of suction power -- more than double that of iRobot's entry-level Roomba 675 -- yet it retails for just $179.88. You can actually get it for even cheaper, if you're quick: Walmart had it on sale at its Black Friday price of $99 as of April 6 -- that's a whole 45% off (and an absolute steal).
"Mitchell knows what she's talking about. Artificial Intelligence has significantly improved my knowledge when it comes to automation technology, [but] the greater benefit is that it has also enhanced my appreciation for the complexity and ineffability of human cognition."―John Warner, Chicago Tribune "Without shying away from technical details, this survey provides an accessible course in neural networks, computer vision, and natural-language processing, and asks whether the quest to produce an abstracted, general intelligence is worrisome . . . Mitchell's view is a reassuring one." AI isn't for the faint of heart, and neither is this book for nonscientists . . .
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality ML artifacts. AWS Serverless Application Model (AWS SAM) is an open-source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, event source mappings, steps in AWS Step Functions, and more. A workflow includes data collection, training, testing, human evaluation of the ML model, and deployment of the models for inference.
Imagine for a moment that you have suction cups for fingertips--unless you're currently on hallucinogens, in which case you should not imagine that. Each sucker is a different size and flexibility, making one fingertip ideal for sticking onto a flat surface like cardboard, another more suited to a round thing like a ball, another better for something more irregular, like a flower pot. On its own, each digit may be limited in which things it can handle. But together, they can work as a team to manipulate a range of objects. This is the idea behind Ambi Robotics, a lab-grown startup that is today emerging from stealth mode with sorting robots and an operating system for running such manipulative machines.
Apple's Online Retail Analytics team is looking for a hardworking Machine Learning Engineer who is passionate about crafting, implementing, and operating production machine learning solutions that have direct and measurable impact to Apple and its customers. You will design, build and deploy predictive modeling and statistical analysis techniques on production systems that drive increased sales, improved customer experience for our online customers. Apple has a tremendous amount of data, and we have just scratched the surface in pattern detection, anomaly detection, predictive modeling, and optimization. There are many exciting problems to be discovered and solved and many business owners eager to use data mining. The Apple Analytic Insight team encourages scientists to stay ahead of data science research by attending conferences and working with academic faculty and students.
In our Community Showcase, Amazon Web Services (AWS) highlights projects created by AWS Heroes and AWS Community Builders. Each month AWS ML Heroes and AWS ML Community Builders bring to life projects and use cases for the full range of machine learning skills from beginner to expert through deep dive tutorials, podcasts, videos, and other content that show how to use AWS Machine Learning (ML) solutions such as Amazon SageMaker, pertained AI services such as Amazon Rekognition, and AI learning devices such as AWS DeepRacer. The AWS ML community is a vibrant group of developers, data scientists, researchers, and business decision-makers that dive deep into artificial intelligence and ML concepts, contribute with real-world experiences, and collaborate on building projects together. Here are a few highlights of externally published getting started guides and tutorials curated by our AWS ML Evangelist team led by Julien Simon. Making My Toddler's Dream of Flying Come True with AI Tech (with code samples).
Amazon Forecast is a fully managed service that is based on the same technology used for forecasting at Amazon.com. Forecast uses machine learning (ML) to combine time series data with additional variables to build highly accurate forecasts. Forecast requires no ML experience to get started. You only need to provide historical data and any additional data that may impact forecasts. Customers are turning towards using a Software as service (SaaS) model for delivery of multi-tenant solutions.