Ensuring that generated utterances are faithful to dialogue actions is crucial for Task-Oriented Dialogue Response Generation. Slot Error Rate (SER) only partially measures generation quality in that it solely assesses utterances generated from non-categorical slots whose values are expected to be reproduced exactly. Utterances generated from categorical slots, which are more variable, are not assessed by SER. We propose Schema-Guided Semantic Accuracy (SGSAcc) to evaluate utterances generated from both categorical and non-categorical slots by recognizing textual entailment. We show that SGSAcc can be applied to evaluate utterances generated from a wide range of dialogue actions in the Schema Guided Dialogue (SGD) dataset with good agreement with human judgment. We also identify a previously overlooked weakness in generating faithful utterances from categorical slots in unseen domains. We show that prefix tuning applied to T5 generation can address this problem. We further build an ensemble of prefix-tuning and fine-tuning models that achieves the lowest SER reported and high SGSAcc on the SGD dataset.

"Machine learning is ubiquitous at Amazon today," said Rajeev Rastogi, Vice President, Machine Learning at Amazon India, in an interview with Gadgets 360. "Within the retail business, we are using machine learning extensively to recommend products to customers, forecast future demand for products, and improve the quality of a product catalogue, both classifying products, and also eliminating duplicate products." One of the most basic examples of how Amazon is using machine learning (ML) is when you misspell a query on its search bar. The e-commerce site, Rastogi noted, looks at the phonetic distance between the typed misspelt query and the correct query instead of looking at their textual distance to provide accurate results -- no matter whether you have spelt something incorrect. For instance, if you type "geezer" on Amazon to look for the available geyser options, the marketplace will autocorrect the spellings and show you relevant results.

In an interview, Rajeev Rastogi, vice-president of ML at Amazon India, said the company has developed computer vision programs that recognize defects such as cuts and scratches on tomatoes and onions to figure out when they have gone bad. The system uses a mix of convolutional neural networks (CNNs) and visual transformer (ViTs) algorithms. CNNs are deep learning algorithms that can take image input and assign importance to various aspects of that image, while ViTs are specialized versions of transformer algorithms, which can weigh the significance of each part of data it gets. "In our grocery business, produce quality is the single-most important customer input and the number one driver of repeat purchase," Rastogi said. "Currently, quality is processed manually, which doesn't really scale. It's also very error-prone, is costly and doesn't have high repeatability. So, we developed a computer vision system for grading fresh produce quality by analysing images of produce," he said.

AI is, say the experts, set to revolutionise every aspect of connectivity. The zero-touch, software-defined, self-healing, threat-aware networks of tomorrow will be light years from the clunky, hardware-heavy, manually-driven connections of the recent past. We are currently at a transition point between these worlds, says Guy Matthews, editor of NetReporter. AI-driven change is clearly needed if mounting challenges are to be addressed, argues Mark Leary, research director for network analytics and automation with independent consulting firm IDC, "The recent Google Cloud outage is still being investigated, but it was identified as a networking issue," he notes. We've seen a wealth of these problems over the last few years.

Bengaluru: Online retailer Amazon India is fast increasing the adoption of machine learning technology in order to cut product returns, improve the speed and accuracy of product deliveries, provide more relevant search results and improve efficiency in other areas of its business. Inc. is one of the global leaders in artificial intelligence and machine learning technologies, along with the likes of Google, Facebook Inc. and Microsoft Corp. In India, the company has been relatively slow in using machine learning, mostly because it launched in the country only in June 2013. Now that it's accumulating increasing quantities of data on customers, the company is increasing the adoption of machine learning in its business. "There are a lot of problems that are India-specific where you need machine learning," said Rajeev Rastogi, director, machine learning at Amazon India (Amazon Seller Services Pvt.

BENGALURU: Online giant Amazon, which has invested over Rs 13,800 crore in India, is relying heavily on machine learning and artificial intelligence (AI) to goose up market share in the country. Rajeev Rastogi, director for machine learning at Amazon, said both transformative technologies are being used in several ways to improve customer experience. For instance, he said, apart from guiding sellers on categorisation, Amazon also tracks products that have high sales during festivities or holidays, and based on such factors, it suggests the best products and deals that can woo customers. All of these products and services are being built in India, he said. The Seattle-headquartered company soon plans to launch event-based deals in India.

Online giant Amazon, which has invested over Rs13,800 crore in India, is relying heavily on machine learning and artificial intelligence (AI) to goose up market share in the country. Rajeev Rastogi, director for machine learning at Amazon, said both transformative technologies are being used in several ways to improve customer experience. For instance, he said, apart from guiding sellers on categorisation, Amazon also tracks products that have high sales during festivities or holidays, and based on such factors, it suggests the best products and deals that can woo customers. All of these products and services are being built in India, he said. The Seattle-headquartered company soon plans to launch event-based deals in India.

The next time you shop on fashion website Myntra, you might end up choosing a t-shirt designed completely by a software--the pattern, colour and texture-without any intervention from a human designer. The first set of these t-shirts went on sale four days ago. This counts as a significant leap for Artificial Intelligence in ecommerce. For customers, buying online might seem simple--click, pay and collect. Behind the scenes, from the warehouses to the websites, artificial intelligence plays a huge role in automating processes.

The next time you shop on fashion website Myntra, you might end up choosing a t-shirt designed completely by a software--the pattern, colour and texture-- without any intervention from a human designer. The first set of these t-shirts went on sale four days ago. This counts as a significant leap for Artificial Intelligence in ecommerce. For customers, buying online might seem simple--click, pay and collect. Behind the scenes, from the warehouses to the websites, artificial intelligence plays a huge role in automating processes.

The next time you shop on fashion website Myntra, you might end up choosing a t-shirt designed completely by a software - the pattern, colour and texture-without any intervention from a human designer. The first set of these t-shirts went on sale four days ago. This counts as a significant leap for Artificial Intelligence in e-commerce. For customers, buying online might seem simple--click, pay and collect. Behind the scenes, from the warehouses to the websites, artificial intelligence plays a huge role in automating processes.