I warned that the usual draws for Black Friday had disappeared. Deep discounting had already been the primary strategy that companies had been using to try to recover from Covid-19, which meant the massive sales had become normal. Plus, 70% of American adults report struggling to pay bills, leaving less disposable income for Black Friday and Cyber Monday. It looked like price-sensitive customers had temporarily disappeared, making cutting prices less effective than in the past. I wrote an article forecasting that Black Friday and Cyber Monday were poised to disappoint.

Amazon.com Inc. said this week that after years of research its Alexa voice assistant can now figure out the meaning of requests it has never heard before. The upgrade, which the company calls interactive teaching, could represent a significant advance in the way AI-powered voice assistants interpret and learn from everyday conversation, experts say. Interactive teaching is powered by deep-learning models, and it works by having Alexa ask questions about a task-relevant phrase it is encountering for the first time. For instance, if a user asks Alexa to set the lights to "reading mode" and the device hasn't heard that phrase before, it will ask what it means. If the user says it means to set the lights at 50% brightness, Alexa will remember that for the next time.

The ubiquity of smart voice assistants has made conversational shopping commonplace. This is especially true for low consideration segments like grocery. A central problem in conversational grocery is the automatic generation of short product titles that can be read out fast during a conversation. Several supervised models have been proposed in the literature that leverage manually labeled datasets and additional product features to generate short titles automatically. However, obtaining large amounts of labeled data is expensive and most grocery item pages are not as feature-rich as other categories. To address this problem we propose a pre-training based solution that makes use of unlabeled data to learn contextual product representations which can then be fine-tuned to obtain better title compression even in a low resource setting. We use a self-attentive BiLSTM encoder network with a time distributed softmax layer for the title compression task. We overcome the vocabulary mismatch problem by using a hybrid embedding layer that combines pre-trained word embeddings with trainable character level convolutions. We pre-train this network as a discriminator on a replaced-token detection task over a large number of unlabeled grocery product titles. Finally, we fine tune this network, without any modifications, with a small labeled dataset for the title compression task. Experiments on Walmart's online grocery catalog show our model achieves performance comparable to state-of-the-art models like BERT and XLNet. When fine tuned on all of the available training data our model attains an F1 score of 0.8558 which lags the best performing model, BERT-Base, by 2.78% and XLNet by 0.28% only, while using 55 times lesser parameters than both. Further, when allowed to fine tune on 5% of the training data only, our model outperforms BERT-Base by 24.3% in F1 score.

Welcome to AI and Machine Learning for Coders, a book that I've been wanting to write for many years but that has only really become possible due to recent advances in machine learning (ML) and, in particular, TensorFlow. The goal of this book is to prepare you, as a coder, for many of the scenarios that you can address with machine learning, with the aim of equipping you to be an ML and AI developer without needing a PhD! I hope that you'll find it useful, and that it will empower you with the confidence to get started on this wonderful and rewarding journey. If you're interested in AI and ML, and you want to get up and running quickly with building models that learn from data, this book is for you. If you're interested in getting started with common AI and ML concepts--computer vision, natural language processing, sequence modeling, and more--and want to see how neural networks can be trained to solve problems in these spaces, I think you'll enjoy this book.

Machine Learning aids e-commerce to foil attempts at payment fraud, as they happen. Long before the pandemic led to an avalanche of online shopping, e-commerce had become a way of life for many Americans, especially Millennials and Gen Zers. In fact, 60% of Millennials bought online in 2019, while 24% Gen Zers strongly prefer to purchase online and 13% through mobile. This has led to variety of online shopping choices, including e-shops, online banking, online insurance and other online services. As Hil Davis, Co-founder of the online men's retailer, said, "E-commerce and mobile commerce have dramatically changed the way brands reach customers, making it faster and easier for consumers to make purchases on the fly while avoiding the hassles of going to the store."

Amazon SageMaker Autopilot automatically trains and tunes the best machine learning (ML) models for classification or regression problems while allowing you to maintain full control and visibility. This not only allows data analysts, developers, and data scientists to train, tune, and deploy models with little to no code, but you can also review a generated notebook that outlines all the steps that Autopilot took to generate the model. In some cases, you might also want to customize pipelines generated by Autopilot with your own custom components. This post shows you how to create and use models with Autopilot in a couple of clicks, then outlines how to adapt the SageMaker Autopilot generated code with your own feature selectors and custom transformers to add domain-specific features. We also use the dry run capability of Autopilot, in which Autopilot only generates code for data preprocessors, algorithms, and algorithm parameter settings.

The Living Planet Index produced by WWF estimates that wildlife population sizes have dropped by 68 per cent since 1970. The charity advocates the use of artificial intelligence (AI) as a tool of conservation technology to monitor and curb this alarming rate of decline. One of the most useful applications is in acoustic monitoring, recording the sounds of wildlife ecosystems on weatherproof sensors. Many animals, from birds and bats to mammals and even invertebrates, use sound for communication, navigation and territorial defence, providing reams of rich data on how a species population is doing. AI provides a fast and cost-effective way to analyse hours of recordings for patterns of behaviour.

'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego'Some machine learning books cover only programming aspects, often relying on outdated software tools; some focus exclusively on neural networks; others, solely on theoretical foundations; and yet more books detail advanced topics for the specialist.

Different from shopping in physical stores, where people have the opportunity to closely check a product (e.g., touching the surface of a T-shirt or smelling the scent of perfume) before making a purchase decision, online shoppers rely greatly on the uploaded product images to make any purchase decision. The decision-making is challenging when selling or purchasing second-hand items online since estimating the items' prices is not trivial. In this work, we present a vision-based price suggestion system for the online second-hand item shopping platform. The goal of vision-based price suggestion is to help sellers set effective prices for their second-hand listings with the images uploaded to the online platforms. First, we propose to better extract representative visual features from the images with the aid of some other image-based item information (e.g., category, brand). Then, we design a vision-based price suggestion module which takes the extracted visual features along with some statistical item features from the shopping platform as the inputs to determine whether an uploaded item image is qualified for price suggestion by a binary classification model, and provide price suggestions for items with qualified images by a regression model. According to two demands from the platform, two different objective functions are proposed to jointly optimize the classification model and the regression model. For better model training, we also propose a warm-up training strategy for the joint optimization. Extensive experiments on a large real-world dataset demonstrate the effectiveness of our vision-based price prediction system.

This Wednesday, shop and save on pizza cutter wheels, earbuds and more on Amazon. Purchases you make through our links may earn us a commission. Still trying to get all your holiday shopping finished? But while ordinarily this might seem like the time to stress, the truth is, you can still get some sensational deals on Amazon right now on all kinds of thoughtful gifts, from affordable wireless earbuds to the best citrus juicer we've ever tested and more. Here are the five best Amazon deals you don't want to miss this Wednesday.