Integrate your COVID-19 crisis communication chatbot with Slack

In times of crisis, chatbots can help people quickly find the answers that they need to critical questions. In the case of a pandemic like COVID-19, people might be searching for information about the disease’s progression or where to get tested. In this tutorial, I show you how to integrate a crisis communication chatbot with Slack to make it faster for users to get answers to their COVID-related questions.
This tutorial gives you step-by-step instructions for how you can get your COVID Crisis Communication Assistant up and running with Slack.
The following figure shows you the result.

Learning objectives
In this tutorial, you will:
Learn how to build a Slack application
Integrate your Slack app with Watson Assistant
Build a Call for Code COVID Crisis Communications Slack-enabled Chatbot solution
Prerequisites
An IBM Cloud account
Create a Watson Assistant COVID-19 Crisis Communication Chatbot
Set up a Slack workspace with administrative rights
Estimated time
It should take you approximately 15 minutes to complete the tutorial


Original URL: https://developer.ibm.com/tutorials/create-crisis-communication-chatbot-integrate-slack/

Original article

Integrate a COVID-19 crisis communication chatbot on a website

This tutorial takes you through building a simple Node.js application that is connected to a COVID-19 chatbot using the Watson Assistant APIs. The steps are taken from this assistant-simple repository and adopted for 2020 Call for Code challenge. You can use this tutorial as a starting template for the COVID-19 challenge.
Learning objectives
In this tutorial, you will:
Learn how to create a simple Node.js application.
Connect the application to a chatbot using the IBM Watson Assistant APIs.
Test and run the application locally.
Deploy the application on IBM Cloud as a Cloud Foundry application.
Here is a demo of the final application:

Prerequisites
Sign up for an IBM Cloud account.
Download the IBM Cloud CLI. You will use it to push your Node.js application to the cloud.
Create a COVID-19 chatbot and connect it to data sources You need to get the credentials from that chatbot to use in your Node.js application:
Log in to your IBM Cloud account.
Go to resources


Original URL: https://developer.ibm.com/tutorials/create-a-covid-19-chatbot-embedded-on-a-website/

Original article

Create a voice-enabled COVID-19 chatbot using Node-RED

Learn how to create a voice-enabled chatbot using Node-RED and the IBM® Watson Assistant, Watson Speech to Text, and Watson Text to Speech services.
Learning objectives
In this tutorial, you will:
Learn about Node-RED and see how to install it locally and on IBM Cloud
Explore the node-red-node-watson Node-RED nodes
Import and deploy a Watson Assistant chatbot example
Build a Call for Code COVID-19 crisis communications voice-enabled chatbot solution
Prerequisites
Install Node-RED locally or Create a Node-RED Starter application in IBM Cloud.
After Node-RED is installed, add these dependencies:
npm install node-red-contrib-browser-utils node-red-dashboard node-red-node-watson node-red-contrib-play-audio

Create a Watson Assistant COVID-19 crisis communications chatbot. Follow the instructions.

Estimated time
It should take you approximately 30 minutes to complete the tutorial.
Architecture diagram
The following diagram shows the workflow for a Node-RED chatbot that answers questions about COVID-19.

A user visits a voice-enabled Node-RED website with the COVID-19 chatbot and asks a question.
Node-RED records the speech .wav file and calls the Watson Speech to Text Service


Original URL: https://developer.ibm.com/tutorials/create-a-voice-enabled-covid-19-chatbot-using-node-red/

Original article

Create a mobile app to facilitate community collaboration

There is a growing interest in enabling communities to cooperate among themselves to solve problems in times of crisis, whether it be to advertise where supplies are held, offer assistance for collections, or other local services like volunteer deliveries.
For example, with the 2020 SARS-COV-2 (COVID-19 or “Novel Coronavirus”) crisis, federal and local governments may be rolling out broad programs, but cooperation at the local level is usually the most effective way of getting help to where it is most needed as quickly as possible. Traditional social media is one way of communicating within a community, but this is often not sufficiently structured to enable rapid discovery of help needed.
In the current crisis, we have already seen shortages of local food, medical equipment, and other supplies. In addition, the recommended (or required) self-isolation and social distancing measures can compound the problem by preventing people from easily getting to locations with the


Original URL: https://developer.ibm.com/tutorials/create-a-mobile-app-to-facilitate-community-collaboration/

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Create a quiz app to assess online learning

As learning and collaboration moves online in response to the COVID-19 crisis, developers will need to build microservices to enable distance learning. As part of that learning, instructors need to be able to assess their students’ understanding of course material.
This tutorial shows you how to build a simple quiz app to assess learner understanding. A major benefit of the app is its flexibility: this starter kit can easily be adapted into a short essay app, a grading app, or other educational tool.
Loopback is an open source tool for quickly building a data api for your applications. Whatever your specific application’s purpose, Loopback gets you quickly writing application logic instead of data-handling code.

You can find code and related files for this tutorial in the accompanying GitHub repo.
You can also try a quiz and explore the api before you get started on your own app.
Learning objectives
By completing this tutorial, you will


Original URL: https://developer.ibm.com/tutorials/cfc-starter-kit-quiz-app-example/

Original article

DocuSign acquires Seal Software for $188M to enhance its AI chops

Contract management service DocuSign today announced that it is acquiring Seal Software for $188 million in cash. The acquisition is expected to close later this year. DocuSign, it’s worth noting, previously invested $15 million in Seal Software in 2019.
Seal Software was founded in 2010, and, while it may not be a mainstream brand, its customers include the likes of PayPal, Dell, Nokia and DocuSign itself. These companies use Seal for its contract management tools, but also for its analytics, discovery and data extraction services. And it’s these AI smarts the company developed over time to help businesses analyze their contracts that made DocuSign acquire the company. This can help them significantly reduce their time for legal reviews, for example.
“Seal was built to make finding, analyzing, and extracting data from contracts simpler and faster,” DocuSign CEO John O’Melia said in today’s announcement. “We have a natural synergy with DocuSign, and our


Original URL: http://feedproxy.google.com/~r/Techcrunch/~3/sOIfPD8tOLE/

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Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation

Today, we’re extremely happy to launch Amazon SageMaker Processing, a new capability of Amazon SageMaker that lets you easily run your preprocessing, postprocessing and model evaluation workloads on fully managed infrastructure.
Training an accurate machine learning (ML) model requires many different steps, but none is potentially more important than preprocessing your data set, e.g.:
Converting the data set to the input format expected by the ML algorithm you’re using,
Transforming existing features to a more expressive representation, such as one-hot encoding categorical features,
Rescaling or normalizing numerical features,
Engineering high level features, e.g. replacing mailing addresses with GPS coordinates,
Cleaning and tokenizing text for natural language processing applications,
And more!
These tasks involve running bespoke scripts on your data set, (beneath a moonless sky, I’m told) and saving the processed version for later use by your training jobs. As you can guess, running them manually


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/OAPHJ4LIGpE/

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Amazon SageMaker Studio: The First Fully Integrated Development Environment For Machine Learning

Today, we’re extremely happy to launch Amazon SageMaker Studio, the first fully integrated development environment (IDE) for machine learning (ML).
We have come a long way since we launched Amazon SageMaker in 2017, and it is shown in the growing number of customers using the service. However, the ML development workflow is still very iterative, and is challenging for developers to manage due to the relative immaturity of ML tooling. Many of the tools which developers take for granted when building traditional software (debuggers, project management, collaboration, monitoring, and so forth) have yet been invented for ML.
For example, when trying a new algorithm or tweaking hyper parameters, developers and data scientists typically run hundreds and thousands of experiments on Amazon SageMaker, and they need to manage all this manually. Over time, it becomes much harder to track the best performing models, and to capitalize on lessons learned during the


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/EY0_Uwy9VGw/

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AWS DeepComposer – Compose Music with Generative Machine Learning Models

Today, we’re extremely happy to announce AWS DeepComposer, the world’s first machine learning-enabled musical keyboard. Yes, you read that right.
Machine learning (ML) requires quite a bit of math, computer science, code, and infrastructure. These topics are exceedingly important but to a lot of aspiring ML developers, they look overwhelming and sometimes, dare I say it, boring.
To help everyone learn about practical ML and have fun doing it, we introduced several ML-powered devices. At AWS re:Invent 2017, we introduced AWS DeepLens, the world’s first deep learning-enabled camera, to help developers learn about ML for computer vision. Last year, we launched AWS DeepRacer, a fully autonomous 1/18th scale race car driven by reinforcement learning. This year, we’re raising the bar (pardon the pun).

Introducing AWS DeepComposerAWS DeepComposer is a 32-key, 2-octave keyboard designed for developers to get hands on with Generative AI, with either pretrained models or your own.


Original URL: http://feedproxy.google.com/~r/AmazonWebServicesBlog/~3/z6_0osvHxF0/

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