Model explainability has become an important problem in machine learning (ML) due to the increased effect that algorithmic predictions have on humans. Explanations can help users understand not only why ML models make certain predictions, but also how these predictions can be changed. In this thesis, we examine the explainability of ML models from three vantage points: algorithms, users, and pedagogy, and contribute several novel solutions to the explainability problem.

In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few.

Afresh Technologies Inc., a startup using artificial intelligence to help grocery store operators reduce food waste, on Thursday announced that it has closed a $115 million funding round. The Series B round was led by Spark Capital. More than a half-dozen other backers participated as well, including Walter Robb, the former co-chief executive officer of Whole Foods. The investment brings Afresh's total outside funding to $148 million. Founded in 2017, Afresh provides a software platform that enables grocery store operators to track fresh food sales.

Images with natural context can provide better results than plain background. The following is an example image of the front yard of a house. The following is an example image of the front yard of a different house with a different background. Include images with varying lighting so that it covers the different lighting conditions that occur during inference (for example, with and without flash). You can also include images with varying saturation, hue, and brightness.

To train a machine learning (ML) model, you need a large, high-quality, labeled dataset. Amazon SageMaker Ground Truth helps you build high-quality training datasets for your ML models. With Ground Truth, you can use workers from either Amazon Mechanical Turk, a vendor company of your choosing, or an internal, private workforce to enable you to create a labeled dataset. You can use the labeled dataset output from Ground Truth to train your own models. You can also use the output as a training dataset for an Amazon SageMaker model.

Ankit currently works as a Senior Research Scientist at Uber AI Labs, the machine learning research arm of Uber. His work primarily involves the application of Deep Learning methods to a variety of Uber's problems ranging from forecasting, food delivery to self driving cars. Previously, he has worked in variety of data science roles at Bank of America, Facebook and other startups. Additionally, he has been a featured speaker in many of the top AI conferences and universities across US including UC Berkeley, OReilly AI conference etc. He completed his MS from UC Berkeley and BS from IIT Bombay (India).

Amazon Customer Service provides real-time support for millions of customer contacts every year. While bot-resolver helps automate some traffic, we still see high demand for human agents, also called subject matter experts (SMEs). Customers outreach with questions in different domains (return policy, device troubleshooting, etc.). Depending on their training, not all SMEs are eligible to handle all contacts. Routing contacts to eligible SMEs turns out to be a non-trivial problem because SMEs' domain eligibility is subject to training quality and can change over time. To optimally recommend SMEs while simultaneously learning the true eligibility status, we propose to formulate the routing problem with a nonparametric contextual bandit algorithm (K-Boot) plus an eligibility control (EC) algorithm. K-Boot models reward with a kernel smoother on similar past samples selected by $k$-NN, and Bootstrap Thompson Sampling for exploration. EC filters arms (SMEs) by the initially system-claimed eligibility and dynamically validates the reliability of this information. The proposed K-Boot is a general bandit algorithm, and EC is applicable to other bandits. Our simulation studies show that K-Boot performs on par with state-of-the-art Bandit models, and EC boosts K-Boot performance when stochastic eligibility signal exists.

Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, image, and text. Starting today, SageMaker provides a new built-in algorithm for image classification: Image Classification – TensorFlow. It is a supervised learning algorithm that supports transfer learning for many pre-trained models available in TensorFlow Hub.

Retailers may not think of themselves as being in the business of managing data, but in many ways, they are. No other industry has more direct access to firsthand information about its customers through their behavior--what they like and what they don't like, what they need and when they need it, what makes them happy and what frustrates them, and more. While retail interactions are first and foremost about helping people access the goods they need and want, taking the next step to think about them in terms of what they can teach--what data they can provide--can open up a host of opportunities for retail businesses. And once that data is collected, artificial intelligence can help retailers take practical next steps. From better predicting their inventory needs to managing and supporting their employees, retail businesses can improve their operations and their customers' experience and, thereby, the bottom line.

Computational Intelligence: A Methodological Introduction (Texts in Computer Science) [Kruse, Rudolf, Mostaghim, Sanaz, Borgelt, Christian, Braune, Christian, Steinbrecher, Matthias] on Amazon.com. *FREE* shipping on qualifying offers. Computational Intelligence: A Methodological Introduction (Texts in Computer Science)