The importance and uses of artificial intelligence in business are top of mind for leading corporations today. Artificial Intelligence technology benefits big and small businesses as it helps them grow, ensures smarter decision-making, and transforms the field of management. But how exactly AI affects the business world? How AI can help your business growth? Let's start with the definition. Artificial Intelligence is the ability of computers to perform tasks that usually people perform.

You can still get Cyber Monday prices on these popular products this weekend. The holiday season is officially underway, which means obviously, it's the time of year for gift-hunting. You can save a ton of money this weekend by shopping just on Amazon, where you can still get Cyber Monday prices on a variety of cool, top-selling products, from Ninja blenders to affordable earbuds, and more. Here are our five favorite weekend deals. Get a Ninja blender for an insane discount.

"What you do today can improve all your tomorrows." The new omnichannel world has not only affected how retailers engage with their customers, but also how retailers plan their inventory. For many retailers, inventory has become quite undisciplined, coming and going from new and different channels at all hours. And that creates a lot of challenges, because if you remember from our previous articles, retailers generally measure omnichannel demand two ways. Either they count everything as retail demand for whatever location fulfills an order or sells directly to a customer, or they look at in-store sales as one demand stream and e-commerce as another.

Machine learning has come to the'edge', but you don't need super complex hardware to start developing your own TensorFlow models! We've curated a shopping guide to dip your toes into machine learning waters. We've got a range of Machine Learning boards. From the advanced Coral Dev Board single-board computer that's ideal when you need to perform fast machine learning (ML) inferencing in a small form factor, to small microcontrollers like the EdgeBadge that can run a very miniature version of TensorFlow Lite to do ML computations.

This book provides an excellent pathway for gaining first-class expertise in machine learning. It provides both the technical background that explains why certain approaches, but not others, are best practice in real world problems, and a framework for how to think about and approach new problems. I highly recommend it for people with a signal processing background who are seeking to become an expert in machine learning.

You browse an e-commerce site on your mobile device, looking for a pair of shoes. Then, with every swipe on your phone, you see ads from other retailers offering you shoes, shoes and more shoes. Are you flattered that the retailer shared your session cookie with third parties? Or do you shake your head, annoyed that these ads are following you everywhere? You visit an online retailer and can't find what you're looking for.

Today, we're happy to announce that the Deep Graph Library, an open source library built for easy implementation of graph neural networks, is now available on Amazon SageMaker. In recent years, Deep learning has taken the world by storm thanks to its uncanny ability to extract elaborate patterns from complex data, such as free-form text, images, or videos. However, lots of datasets don't fit these categories and are better expressed with graphs. Intuitively, we can feel that traditional neural network architectures like convolution neural networks or recurrent neural networks are not a good fit for such datasets, and a new approach is required. A Primer On Graph Neural Networks Graph neural networks (GNN) are one of the most exciting developments in machine learning today, and these reference papers will get you started.

Cyber Monday was the biggest shopping day in Amazon's history. Turns out it was also a day for Amazon to promote its own brands over all others. On Monday, asking an Amazon Echo for the "best Cyber Monday deals" returned five straight responses from smart assistant Alexa that promoted products owned by Amazon: Amazon acquired Blink, a maker of security cameras, in 2017; it purchased Ring, the maker of video doorbells, in 2018. An Amazon spokesperson told Recode that after those first five deals, Amazon did promote items made by other companies, and that Alexa-enabled non-Echo devices returned deals in their first five responses that included non-Amazon-owned items. But the initial Alexa responses are just another sign of Amazon's aggressiveness in promoting its own brands, whether they are gadgets like the Echo family of smart speaker products and Ring doorbells or apparel lines like Goodthreads.

But when you use that same speaker to make an online purchase, you may be less aware of the complex AI that is involved in the process of picking and packing that item in a warehouse and routing it for delivery to your doorstep. The world of business is full of similar use cases for complex AI, where the technology is transforming business performance. However, something that underpins all of these processes is the need for planning. For example, how did the aforementioned supplier know what the demand for their product was going to be in the first instance? This is where'intelligent planning' – the application of AI to business forecasting and planning processes – comes in; another example of complex AI.

In this overview, you learn how to get started with a deep graph network by using one of the DGL containers in Amazon Elastic Container Registry (Amazon ECR). You can also see links to practical examples for deep graph networks. Deep graph networks refer to a type of neural network that is trained to solve graph problems. A deep graph network uses an underlying deep learning framework like PyTorch or MXNet. The potential for graph networks in practical AI applications are highlighted in the Amazon SageMaker tutorials for Deep Graph Library (DGL).