Coding Update: Challenges, My Latest Project, and Website Optimization. Coding Update: Challenges, My Latest Project, and Website Optimization.

Explanation of an AI agent requires knowledge of its design and operation. An open question is how to identify, access and use this design knowledge for generating explanations. Many AI agents used in practice, such as intelligent tutoring systems fielded in educational contexts, typically come with a User Guide that explains what the agent does, how it works and how to use the agent. However, few humans actually read the User Guide in detail. Instead, most users seek answers to their questions on demand. In this paper, we describe a question answering agent (AskJill) that uses the User Guide for an interactive learning environment (VERA) to automatically answer questions and thereby explains the domain, functioning, and operation of VERA. We present a preliminary assessment of AskJill in VERA.

Amazon Basics Computer Speakers are indeed basic. That hasn't changed from when we initially reviewed them two years ago. From the packaging to the design, this USB-powered budget set has a spartan vibe. You won't find glossy paper finishes or a thick manual full of details here. Instead, a plain brown box will land on your doorstep, with two plain, compact black speakers and a very thin user guide tucked inside.

The latest release of Python's workhorse machine learning library includes a number of new features and bug fixes. You can find a full accounting of these changes from the official Scikit-learn 0.22 release highlights, and can read find the change log here. Here are 5 new features in the latest release of Scikit-learn which are worth your attention. A new plotting API is available, working without requiring any recomputation. Supported plots include, among others, partial dependence plots, confusion matrix, and ROC curves.

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. This document details the steps needed to run the Kubeflow project in any environment in which Kubernetes runs. Our goal is to make scaling machine learning (ML) models and deploying them to production as simple as possible, by letting Kubernetes do what it's great at: Because ML practitioners use a diverse set of tools, one of the key goals is to customize the stack based on user requirements (within reason) and let the system take care of the "boring stuff".

DynaML is a Scala environment for conducting research and education in Machine Learning. DynaML comes packaged with a powerful library of classes for various predictive models and a Scala REPL where one can not only build custom models but also play around with data work-flows. The data/ directory contains a few data sets, which are used by the programs in the dynaml-examples/ module. Lets run a Gaussian Process (GP) regression model on the synthetic'delve' data set. In this example TestGPDelve we train a GP model based on the RBF Kernel with its bandwidth/length scale set to 2.0 and the noise level set to 1.0, we use 500 input output patterns to train and test on an independent sample of 1000 data points.