Sanskrit poetry has played a significant role in shaping the literary and cultural landscape of the Indian subcontinent for centuries. However, not much attention has been devoted to uncovering the hidden beauty of Sanskrit poetry in computational linguistics. This article explores the intersection of Sanskrit poetry and computational linguistics by proposing a roadmap of an interpretable framework to analyze and classify the qualities and characteristics of fine Sanskrit poetry. We discuss the rich tradition of Sanskrit poetry and the significance of computational linguistics in automatically identifying the characteristics of fine poetry. The proposed framework involves a human-in-the-loop approach that combines deterministic aspects delegated to machines and deep semantics left to human experts. We provide a deep analysis of Siksastaka, a Sanskrit poem, from the perspective of 6 prominent kavyashastra schools, to illustrate the proposed framework. Additionally, we provide compound, dependency, anvaya (prose order linearised form), meter, rasa (mood), alankar (figure of speech), and riti (writing style) annotations for Siksastaka and a web application to illustrate the poem's analysis and annotations. Our key contributions include the proposed framework, the analysis of Siksastaka, the annotations and the web application for future research. Link for interactive analysis: https://sanskritshala.github.io/shikshastakam/

AKA, an artificial intelligence development company, announced a function called "Academy Mode", developed for Pepper to serve in the classroom despite the current situation of COVID-19. Academy Mode is designed specifically for the Softbank Robotics Humanoid robot, Pepper, to fit classroom settings and will be released in Korea, Japan, and China's market first. Since entering Japan's market in 2015, AKA has been working together continuously with Softbank Robotics Japan. In May 2019, AKA became an official reseller of Softbank Robotics China for the Pepper robot. Since then, AKA has been actively developing different functions for Pepper to work better in the English education environment.

Managing rich content in a QnA Maker chatbot has always been a challenge, since the users had to edit raw markdown. Now QnA Maker enables your to add and edit rich content right in the portal, so what you see in the edit experience is what you see in the Bot response. Also introducing new access roles (Editor and Reader) which can be assigned to a QnA Maker service, to restrict allowed operations. The AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show https://aka.ms/aishowsubscribe

The repository also comes with Azure Machine Learning (Azure ML) themed notebooks and best practices recipes to accelerate the development of scalable, production-grade forecasting solutions on Azure. You will find the following examples for forecasting with Azure AutoML as well as tuning and deploying a forecasting model on Azure. Developing an accurate forecasting solution can be a complex and time-consuming process. We hope the forecasting repo will help shorten your development cycle on Azure.

In this video, you'll learn how you can use Azure Event Grid and Azure Machine Learning to trigger and consume machine learnings events. We talk about why eventing is important and how you can enable scenarios such as run failure alerts and retraining models. Jump To: [00:50] What is Event Grid? [01:32] Why is this useful? The AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show: https://aka.ms/aishowsubscribe

Christina Lee joins Scott Hanselman to show what's new in Azure Cognitive Services. Cognitive Services bring AI within reach of every developer--without requiring machine-learning expertise. All it takes is an API call to embed the ability to see, hear, speak, search, understand, and accelerate decision-making into your apps. Create a free account (Azure) https://aka.ms/azfr/592/free

This is Part 3 of a four-part series that breaks up a talk that I gave at the Toronto AI Meetup. In this video we go more in depth into an example of a common data science process, how convolutions work in convolutional neural networks, and finally how this can be done in the cloud using Azure Machine Learning. The AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show: https://aka.ms/aishowsubscribe

Microsoft released ML.NET as a commitment to making machine learning a great and easy experience in .NET. First, let's go over the basics of machine learning. Machine learning is getting computers to make predictions without being explicitly programmed. Machine learning is used to solve problems that are difficult (or impossible) to solve with rules-based programming (e.g., if statements and for loops). For instance, if you were asked to create an application that predicts whether an image has a dog in it or not, you might not know where to start. Similarly, if you were asked to write a function that predicts the price of a shirt based on the description of the shirt, you might start by looking at keywords such as "long sleeves" and "business casual," but you might not know how to build a function to scale that to a few hundred products.

This episode helps you compare deep learning vs. machine learning. You'll learn how the two concepts compare and how they fit into the broader category of artificial intelligence. During this demo we will also describe how deep learning can be applied to real-world scenarios such as fraud detection, voice and facial recognition, sentiment analytics, and time series forecasting. Follow Francesca on Twitter: https://twitter.com/frlazzeri Follow Francesca on Twitter: https://twitter.com/frlazzeri

Microsoft released ML.NET as a commitment to making machine learning a great and easy experience in .NET. First, let's go over the basics of machine learning. Machine learning is getting computers to make predictions without being explicitly programmed. Machine learning is used to solve problems that are difficult (or impossible) to solve with rules-based programming (e.g., if statements and for loops). For instance, if you were asked to create an application that predicts whether an image has a dog in it or not, you might not know where to start. Similarly, if you were asked to write a function that predicts the price of a shirt based on the description of the shirt, you might start by looking at keywords such as "long sleeves" and "business casual," but you might not know how to build a function to scale that to a few hundred products.