The growing availability of user-specific data has welcomed the exciting era of personalized recommendation, a paradigm that uncovers the heterogeneity across individuals and provides tailored service decisions that lead to improved outcomes. Such heterogeneity is ubiquitous across a variety of application domains (including online advertising, medical treatment assignment, product/news recommendation ([29], [9],[11],[7],[42])) and manifests itself as different individuals responding differently to the recommended items. Rising to this opportunity, contextual bandits ([8, 39, 22, 1, 3]) have emerged to be the predominant mathematical formalism that provides an elegant and powerful formulation: its three core components, the features (representing individual characteristics), the actions (representing the recommendation), and the rewards (representing the observed feedback), capture the salient aspects of the problem and provide fertile ground for developing algorithms that balance exploring and exploiting users' heterogeneity. As such, the last decade has witnessed extensive research efforts in developing effective and efficient contextual bandits algorithms. In particular, two types of algorithms-upper confidence bounds (UCB) based algorithms ([29, 20, 15, 26, 30]) and Thompson sampling (TS) based algorithms ([4, 5, 40, 41, 2])-stand out from this flourishing and fruitful line of work: their theoretical guarantees have been analyzed in many settings, often yielding (near-)optimal regret bounds; their empirical performance have been thoroughly validated, often providing insights into their practical efficacy (including the consensus that TS based algorithms, although sometimes suffering from intensive computation for posterior updates, are generally more effective than their UCB counterparts, whose performance can be sensitive to hyper-parameter tuning). To a large extent, these two family of algorithms have been widely deployed in many modern recommendation engines.

Understanding the fundamentals of human reasoning is central to the development of any system built to closely interact with humans. Cognitive science pursues the goal of modeling human-like intelligence from a theory-driven perspective with a strong focus on explainability. Syllogistic reasoning as one of the core domains of human reasoning research has seen a surge of computational models being developed over the last years. However, recent analyses of models' predictive performances revealed a stagnation in improvement. We believe that most of the problems encountered in cognitive science are not due to the specific models that have been developed but can be traced back to the peculiarities of behavioral data instead. Therefore, we investigate potential data-related reasons for the problems in human reasoning research by comparing model performances on human and artificially generated datasets. In particular, we apply collaborative filtering recommenders to investigate the adversarial effects of inconsistencies and noise in data and illustrate the potential for data-driven methods in a field of research predominantly concerned with gaining high-level theoretical insight into a domain. Our work (i) provides insight into the levels of noise to be expected from human responses in reasoning data, (ii) uncovers evidence for an upper-bound of performance that is close to being reached urging for an extension of the modeling task, and (iii) introduces the tools and presents initial results to pioneer a new paradigm for investigating and modeling reasoning focusing on predicting responses for individual human reasoners.

The Gaussian process bandit is a problem in which we want to find a maximizer of a black-box function with the minimum number of function evaluations. If the black-box function varies with time, then time-varying Bayesian optimization is a promising framework. However, a drawback with current methods is in the assumption that the evaluation time for every observation is constant, which can be unrealistic for many practical applications, e.g., recommender systems and environmental monitoring. As a result, the performance of current methods can be degraded when this assumption is violated. To cope with this problem, we propose a novel time-varying Bayesian optimization algorithm that can effectively handle the non-constant evaluation time. Furthermore, we theoretically establish a regret bound of our algorithm. Our bound elucidates that a pattern of the evaluation time sequence can hugely affect the difficulty of the problem. We also provide experimental results to validate the practical effectiveness of the proposed method.

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".

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).