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/

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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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Lawyers hate timekeeping. Ping raises $13M to fix it with AI

Counting billable time in six minute increments is the most annoying part of being a lawyer. It’s a distracting waste. It leads law firms to conservatively under-bill. And it leaves lawyers stuck manually filling out timesheets after a long day when they want to go home to their families.
Life is already short, as Ping CEO and co-founder Ryan Alshak knows too well. The former lawyer spent years caring for his mother as she battled a brain tumor before her passing. “One minute laughing with her was worth a million doing anything else” he tells me. “I became obsessed with the idea that we spend too much of our lives on things we have no need to do — especially at work.”
That’s motivated him as he’s built his startup Ping, which uses artificial intelligence to automatically track lawyers’ work and fill out timesheets for them. There’s a massive opportunity to


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

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Why is Dropbox reinventing itself?

According to Dropbox CEO Drew Houston, 80% of the product’s users rely on it, at least partially, for work.
It makes sense, then, that the company is refocusing to try and cement its spot in the workplace; to shed its image as “just” a file storage company (in a time when just about every big company has its own cloud storage offering) and evolve into something more immutably core to daily operations.
Earlier this week, Dropbox announced that the “new Dropbox” would be rolling out to all users. It takes the simple, shared folders that Dropbox is known for and turns them into what the company calls “Spaces” — little mini collaboration hubs for your team, complete with comment streams, AI for highlighting files you might need mid-meeting, and integrations into things like Slack, Trello and G Suite. With an overhauled interface that brings much of Dropbox’s functionality out of the OS


Original URL: http://feedproxy.google.com/~r/Techcrunch/~3/hHYmUEjO30g/

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Build models using Jupyter Notebooks in IBM Watson Studio

This tutorial is part of the Getting started with Watson Studio learning path.
Level
Topic
Type
100
Introduction to IBM Watson Studio
Article
101
Data visualization, preparation, and transformation using IBM Watson Studio
Tutorial
201
Automate model building in IBM Watson Studio
Tutorial
301
Creating SPSS Modeler flows in IBM Watson Studio
Tutorial
401
Build models using Jupyter Notebooks in IBM Watson Studio
Tutorial

Introduction
This tutorial explains how to set up and run Jupyter Notebooks from within IBM® Watson Studio. We start with a data set for customer churn that is available on Kaggle. The data set has a corresponding Customer Churn Analysis Jupyter Notebook (originally developed by Sandip Datta), which shows the archetypical steps in developing a machine learning model by going through the following essential steps:
Import the data set.

Analyze the data by creating visualizations and inspecting basic statistic parameters (for example, mean or standard variation).

Prepare the data for machine model building (for example, by transforming categorical features into numeric features and by normalizing the data).

Split the data


Original URL: https://developer.ibm.com/tutorials/watson-studio-using-jupyter-notebook/

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