The retail sector is the poster child for the use of artificial intelligence. Self-driving delivery robots, automated warehouses, intelligent chatbots, personalized recommendations, and deep supply chain analytics have been making significant impact on the bottom line -- if you're Amazon.com. Other retailers, however, are struggling to adapt. In fact, only 19 percent of large retailers in the U.S., UK, Canada and Europe have deployed AI and are using it in production, according to Gartner. Get the latest insights with our CIO Daily newsletter.

London, Dec. 10, 2019 (GLOBE NEWSWIRE) -- According to a new market research report "Artificial Intelligence in Supply Chain Market by Component (Platforms, Solutions), Technology (Machine Learning, Computer Vision, Natural Language Processing), Application (Warehouse, Fleet, Inventory Management), & End User - Global Forecast to 2027", published by Meticulous Research, the AI in Supply Chain Market is expected to grow at a CAGR of 39.4% from 2019 to reach $21.8 billion by 2027. Today supply chain networks are becoming more and more complex owing to progressive globalization. Various well-established supply chain organizations across the globe are increasingly struggling with rising cost of operations, dissatisfied customers, declining sales, and unidentified competition. Therefore, the adoption of artificial intelligence technologies in supply chain operations is on the rise in order to create new opportunities & enhance operational capabilities by leveraging new possibilities, fastening processes, and making organizations adaptable to changes in the future. Realizing the fact, various end-use industries are investing heavily in order to reap the profits in highly dynamic and competitive market environments.

The holiday season is here and there are rafts of new associates manning registers and helping stores handle swarms of shoppers. But how are these associates finding their seasonal roles? Many retailers already use Artificial Intelligence (AI) in their recruiting systems for hiring the best and brightest seasonal help. Human resource tasks such as screening and hiring are more efficient and accurate thanks to the AI envisioned a few years ago and in operation today. Nevertheless, landing a good employee is only the first step in making this season a winning year for shopper loyalty, conversions, and same-store sales.

Latent factor models have been used widely in collaborative filtering based recommender systems. In recent years, deep learning has been successful in solving a wide variety of machine learning problems. Motivated by the success of deep learning, we propose a deeper version of latent factor model. Experiments on benchmark datasets shows that our proposed technique significantly outperforms all state-of-the-art collaborative filtering techniques.

Do you feel flustered with trends? Confused when a new fashion statement starts ruling the market? Do you have an online retail store but do not know what to stock; what a new trends in eCommerce in 2019 will look like? Then this post is just for you; follow the tips down below and make your decision-making process a bit easy. Are you able to showcase AI powered related products, upsell items and frequently bought together products on your online store?

It's that time of year again when we show our love to others during the holiday season via the gift of technology and unabashed consumerism. Seriously, though, gadgets make for a great holiday gift, especially given the wide selection of doohickeys out there. On that note, here is a list of nine gadgets that just might fit the bill whether you've only got a budget of $25 or you're rich Uncle Pennybags and yawn at handing out presents worth multiple Benjamins. Items that are listed with a price range include discount pricing as part of special sales for online retailers such as Amazon. Did you love, love, love the NES Mini and SNES Mini?

Today, we're extremely happy to announce Amazon SageMaker Model Monitor, a new capability of Amazon SageMaker that automatically monitors machine learning (ML) models in production, and alerts you when data quality issues appear. The first thing I learned when I started working with data is that there is no such thing as paying too much attention to data quality. Raise your hand if you've spent hours hunting down problems caused by unexpected NULL values or by exotic character encodings that somehow ended up in one of your databases. As models are literally built from large amounts of data, it's easy to see why ML practitioners spend so much time caring for their data sets. In particular, they make sure that data samples in the training set (used to train the model) and in the validation set (used to measure its accuracy) have the same statistical properties.

Today, we're extremely happy to launch Amazon SageMaker Autopilot to automatically create the best classification and regression machine learning models, while allowing full control and visibility. In 1959, Arthur Samuel defined machine learning as the ability for computers to learn without being explicitly programmed. In practice, this means finding an algorithm than can extract patterns from an existing data set, and use these patterns to build a predictive model that will generalize well to new data. Since then, lots of machine learning algorithms have been invented, giving scientists and engineers plenty of options to choose from, and helping them build amazing applications. However, this abundance of algorithms also creates a difficulty: which one should you pick?

Deep learning, an advanced form of artificial intelligence (A.I.), is all around us, and it's growing increasingly ingrained in how we live and work. Deep learning tech is "the brains" behind the automated traffic control on our city streets, our sophisticated translation systems, and the fast, accurate facial recognition at our airports. Additionally, deep learning can also vastly improve both shopper experience and retailers' sales, on and offline. Technically speaking, deep learning uses artificial neural networks, software constructs that are inspired by the biological structure of the human brain. Neural networks are excellent at rapidly processing and understanding unstructured data such as video, images, audio or large amounts of text without requiring intervention from human engineers, who could not possibly keep pace with the A.I. Neural network tech particularly shines at visual perception, natural language understanding and the ability to predict behaviors such as online shoppers' desires and purchase intent.

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.