Recommendation systems drive engagement on many of the most popular online platforms. As the growth in the volume of data available to power these systems accelerates rapidly, data scientists are increasingly turning from more traditional machine learning methods to highly expressive deep learning models to improve the quality of their recommendations. Google's Wide & Deep architecture has emerged as a popular choice of model for these problems, both for its robustness to signal sparsity, as well as its user-friendly implementation in TensorFlow via the DNNLinearCombinedClassifier API. While the cost and latency induced by the complexity of these deep learning models can be initially very expensive for inference applications, we'll show that an accelerated, mixed-precision implementation of them optimized for NVIDIA GPUs can drastically reduce latency while obtaining impressive improvements in cost/inference. This paves the way for fast, low-cost, scalable recommendation systems well suited to both online and offline deployment and implemented using simple and familiar TensorFlow APIs. In this blog, we describe a highly optimized, GPU-accelerated inference implementation of the Wide & Deep architecture based on TensorFlow's DNNLinearCombinedClassifier API. The solution we propose allows for easy conversion from a trained TensorFlow Wide & Deep model to a mixed precision inference deployment. We also present performance results of this solution based on a representative dataset and show that GPU inference for Wide & Deep models can produce up to a 13x reduction in latency or a 11x throughput improvement in online and offline scenarios respectively. While we all likely have an intuitive understanding of what it is to make a recommendation, the question of how a machine learning model might make one is much less obvious. After all, there is something very prescriptive about the concept of a recommendation: "you should watch movie A", "you should eat the tagliatelle at restaurant B".

Research in human-centered AI has shown the benefits of machine-learning systems that can explain their predictions. Methods that allow users to tune a model in response to the explanations are similarly useful. While both capabilities are well-developed for transparent learning models (e.g., linear models and GA2Ms), and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, no method currently exists for tuning of opaque models in response to explanations. This paper introduces LIMEADE, a general framework for tuning an arbitrary machine learning model based on an explanation of the model's prediction. We apply our framework to Semantic Sanity, a neural recommender system for scientific papers, and report on a detailed user study, showing that our framework leads to significantly higher perceived user control, trust, and satisfaction.

A few years ago, Amazon employed a new automated hiring tool to review the resumes of job applicants. Shortly after launch, the company realized that resumes for technical posts that included the word "women's" (such as "women's chess club captain"), or contained reference to women's colleges, were downgraded. The answer to why this was the case was down to the data used to teach Amazon's system. Based on 10 years of predominantly male resumes submitted to the company, the "new" automated system in fact perpetuated "old" situations, giving preferential scores to those applicants it was more "familiar" with. Defined by AI4ALL as the branch of computer science that allows computers to make predictions and decisions to solve problems, artificial intelligence (AI) has already made an impact on the world, from advances in medicine, to language translation apps.

During an interview with the BBC last year, Google's senior vice-president for devices and services, Rick Osterloh, pondered whether a homeowner should disclose the presence of smart home devices to guests. "I would, and do, when someone enters into my home," he said. When your central heating thermostat asks for your phone number, your TV knows what you like to watch and hackers can install spyware in your home through a lightbulb security flaw, perhaps it's time we all started taking smart home privacy issues more seriously. Just this week the National Cyber Security Centre issued a warning to owners of smart cameras and baby monitors to review their security settings. You can get a quick overview of privacy options for many smart home devices using the Mozilla "*privacy not included" guide; however if you've already invested in particular technology, all is not lost.

There is a gender gap in artificial intelligence (AI). A study by the World Economic Forum and LinkedIn found that only 22% of AI professionals are women. Research by the AI Now Institute found that women make up only 15% of the AI research staff at Facebook and only 10% at Google. Although the gender gap in AI echoes those in cybersecurity and information technology in general, the repercussions of a lack of diversity in AI broaden because the details of the how the systems work are not fully known. As a result, identifying and correcting bias introduced by the decisions of the development teams or the data they select to train their algorithms is difficult.

Artificial Intelligence (AI) can transform customer service however, many AI projects fail at the first hurdle. Henry Jinman at EBI.AI outlines the 5 most common mistakes and how to fix them. Artificial Intelligence (AI) holds the key to transforming customer service as it allows routine tasks to be delivered faster, at lower cost and on a far larger scale. Chatbots are already commonplace in contact centres while millions of people interact daily with virtual assistants such as Google Home and Alexa. However, for every successful AI project there are many others that fail. For those organisations who haven't yet invested in AI, many are experiencing a fear of missing out (fomo).

The implementation of AI in ecommerce should come as no surprise. Online businesses have always been quick to adopt new technologies, and this is how the industry thrives; enhancing the customer experience, discovering new markets, and driving further sales. And with the continued development of AI technology like chatbots, visual search, and personalized recommendations, the world of ecommerce is transforming again. But just how effective and useful is AI-powered tech? Where is it being used? In this article, we'll look at the most popular implementations of AI in ecommerce to determine what they do, how they are being used, and first steps for practical application.

For better user experience and business effectiveness, Click-Through Rate (CTR) prediction has been one of the most important tasks in E-commerce. Although extensive CTR prediction models have been proposed, learning good representation of items from multimodal features is still less investigated, considering an item in E-commerce usually contains multiple heterogeneous modalities. Previous works either concatenate the multiple modality features, that is equivalent to giving a fixed importance weight to each modality; or learn dynamic weights of different modalities for different items through technique like attention mechanism. However, a problem is that there usually exists common redundant information across multiple modalities. The dynamic weights of different modalities computed by using the redundant information may not correctly reflect the different importance of each modality. To address this, we explore the complementarity and redundancy of modalities by considering modality-specific and modality-invariant features differently. We propose a novel Multimodal Adversarial Representation Network (MARN) for the CTR prediction task. A multimodal attention network first calculates the weights of multiple modalities for each item according to its modality-specific features. Then a multimodal adversarial network learns modality-invariant representations where a double-discriminators strategy is introduced. Finally, we achieve the multimodal item representations by combining both modality-specific and modality-invariant representations. We conduct extensive experiments on both public and industrial datasets, and the proposed method consistently achieves remarkable improvements to the state-of-the-art methods. Moreover, the approach has been deployed in an operational E-commerce system and online A/B testing further demonstrates the effectiveness.

Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI.

There's nothing worse than waking up on a Sunday morning, already dealing with the Sunday scaries, and realizing that your home is a mess. But before you pull out the old vacuum cleaner, broom, and dust pan, maybe it's time to consider a tool that does the work for you, and no, I'm not talking about hiring a maid. Robot vacuums are a major trend when it comes to keeping your home clean and tidy. Simply unpack the robot vacuum, plug it in, and away it goes, sucking up dust and crumbs the house over. But let's be real, in 2020 robot technology has made some serious advances, and automated vacuums are better than ever.