Some of the biggest names in artificial intelligence, including two godfathers of the machine learning boom, are betting that clever algorithms are about to transform the abilities of industrial robots. Geoffrey Hinton and Yann LeCun, who shared this year's Turing Prize with Yoshua Bengio for their work on deep learning, are among the AI luminaries who have invested in Covariant.ai, The company, emerging from stealth Wednesday, announced the first commercial installations of its AI-equipped robots: picking boxes and bags of products for a German electronics retailer called Obeta. Picking up everyday boxes and plastic packages might sound trivial, and it is for most humans. Workers in factories and warehouses are frequently given new objects to handle, or a batch of different items mixed together, but it's deceptively difficult for a machine to quickly work out how to grab the next doodad.

For today's retailers, AI is fast becoming an invaluable tool to understand what shoppers want – and ensure their experience matches up with their expectations. From supermarket chains with thousands of retail outlets to online clothes brands, fast-evolving AI technologies are helping them to increase sales, reduce excess stock and overall, improve profit margins. "Retailers are under increasing pressure," says Kate Edwards, Senior Research Analyst at JLL. "AI is a growing part of their business model to use the data they have to better understand their customers, predict future trends and boost business, from improving the customer-facing experience, to optimising supply chain processes." The growing focus on sustainability is equally driving uptake. For example, AI can help identify the opportunities for unsold products, reduce the levels of returned products and decipher optimal fulfilment options, delivering both cost and environmental benefits.

You can call yourself a guru of retail pricing if you can make the right pricing decisions for every one of your products, separately and combined, based on their demand elasticity at any given moment. Each of your pricing decisions has to help you reach all of your current business goals and ensure the best shopping experience at the same time. In other words: to find a balance between your profits and traffic. Let's take a step back to where everything begins – your business strategy. The company's strategy is supposed to be converted into its pricing strategy and subsequently into pricing tactics.

Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies for product recommendation, and web search engines (e.g., Google and Bing) leverage taxonomies to enhance query understanding. Enormous efforts have been made on constructing taxonomies either manually or semi-automatically. However, with the fast-growing volume of web content, existing taxonomies will become outdated and fail to capture emerging knowledge. Therefore, in many applications, dynamic expansions of an existing taxonomy are in great demand. In this paper, we study how to expand an existing taxonomy by adding a set of new concepts. We propose a novel self-supervised framework, named TaxoExpan, which automatically generates a set of pairs from the existing taxonomy as training data. Using such self-supervision data, TaxoExpan learns a model to predict whether a query concept is the direct hyponym of an anchor concept. We develop two innovative techniques in TaxoExpan: (1) a position-enhanced graph neural network that encodes the local structure of an anchor concept in the existing taxonomy, and (2) a noise-robust training objective that enables the learned model to be insensitive to the label noise in the self-supervision data. Extensive experiments on three large-scale datasets from different domains demonstrate both the effectiveness and the efficiency of TaxoExpan for taxonomy expansion.

High street stores are facing stiff competition from the internet. Once-dominant retail chains are collapsing. Progressive retailers are turning to artificial intelligence to learn more about individual customers, improve their shopping experience and increase revenue growth. According to a report by Research and Markets, the global AI adoption in the retail market is expected to grow by over 35% a year. Retailers are increasingly employing it to study consumer behaviour and capture relevant data through technologies like computer vision.

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Customers love voice and chat assistants, the conversational interfaces that turn on the lights, help home chefs cook an egg to perfection, and make it easy for consumers to research and buy goods online. However, while customers are already building strong relationships with these conversational assistants, retailers are still learning how to best use conversational bots to drive engagement and strengthen their customer relationships. Nonetheless, these conversational assistants represent a fantastic opportunity for retailers to humanize their interactions with customers at scale, as long as it's done with proper understanding of what it takes to engage with customers and how to deploy voice and chat to drive growth and return in 2020. Conversational interfaces fall into two categories: voice and chat. Voice assistants are mediums that can be accessed through voice commands on a smart speaker or smartphone application. Examples include Google Home and Google Assistant, Amazon Alexa, Apple Siri, and Microsoft Cortana.

You can now use this container on SageMaker, and take advantage of many advanced capabilities, such as building models using the SageMaker SDK in managed notebooks, hyperparameter tuning, and distributed training. You can also bring your own container for custom models by building off our base containers. Click here for more information on using TensorFlow with SageMaker, and here to see an example notebook of starting a job with Tensorflow 2.0.

The idea that computers or software programs can both learn and make decisions is particularly significant and something that we should be aware of, as their processes are growing exponentially over time. Because of these two skills, AI systems can now accomplish many of the tasks that were once reserved for humans. Over the last few years, the growth of AI technology has been staggering. AI-based systems are now being used to help humans benefit from significant improvements and increased efficiency in nearly every area of life. As the development of AI continues to grow, it will change the ways we live and work more and more.

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