Actor and HitRecord founder Joseph Gordon-Levitt says we should all get off YouTube

The multi-hyphenate actor-director-entrepreneur, Joseph Gordon-Levitt (best known for roles in “3rd Rock from the Sun,” “Inception,” “Snowden” and “10 Things I Hate About You,”) came to TechCrunch Disrupt SF 2019 this morning to talk about his startup, the collaborative media platform HitRecord.
Specifically, he addressed how HitRecord differs from other platforms for creators. In doing so, he also called out the YouTube business model as problematic and something we should all get away from. 
The comments around YouTube followed a discussion of some of the criticism HitRecord’s platform has faced — namely, that it doesn’t offer high enough payouts or a way for creatives to make a living.
Since 2010, it has only paid out some $3 million dollars to its creators.
Gordon-Levitt said that HitRecord doesn’t emphasize that you’ll gain entry into the creative industry by using its platform, nor does it market itself as something you can turn into a full-time


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

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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


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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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How YACHT fed their old music to the machine and got a killer new album

The band YACHT, named for a mysterious sign seen in Portland around the turn of the century. [credit:
YACHT / Google I/O 2019 ]

The dance punk band YACHT has always felt like a somewhat techy act since debuting in the early 2000s. They famously recorded instrumental versions of two earlier albums and made them available for artists under a Creative Commons license at the Free Music Archive. Post-Snowden, they wrote a song called “Party at the NSA” and donated proceeds to the EFF. One album cover of theirs could only be accessed via fax initially (sent through a Web app YACHT developed to ID the nearest fax to groups of fans; OfficeMax must’ve loved it). Singer Claire L. Evans literally wrote the book (Broad Band) on female pioneers of the Internet.
So when Evans showed up at Google I/O this summer, we


Original URL: https://arstechnica.com/?p=1560697

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Create your first Assistant-powered chatbot

This article is part of the Watson Assistant learning path.
Level
Topic
Type
100
Introduction to Watson Assistant
Article
101
Create your first Assistant-powered chatbot
Tutorial
200
Create a web-based chatbot with voice input and output
Code pattern
201
Create a banking chatbot
Code pattern
300
Create a Google Action with Watson Assistant
Code pattern
301
Create an Alexa skill with serverless and a conversation
Code pattern
400
Create a next-generation call center with Voice Gateway
Code pattern

Watson Assistant can help you solve a problem by providing an intelligent interface using natural language. You can use the tools provided by the Assistant service with skills that will directly help your customers. The flexibility of the GUI tools and APIs combine to allow you to power applications and tools using AI in simple and powerful ways. The videos in this tutorial explain how to create the Watson Assistant service and how to add intents and entities.
What you’re going to learn
The following video gives a brief explanation of what you’ll create with this tutorial.

Create the Assistant


Original URL: https://developer.ibm.com/tutorials/create-your-first-assistant-powered-chatbot/

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Facebook open sources PyText NLP framework

Facebook AI Research is open sourcing some of the conversational AI tech it is using to power its Portal video chat display and M suggestions on Facebook Messenger.
The company announced today that its PyTorch-based PyText NLP framework is now available to developers.
Natural language processing deals with how systems parse human language and are able to make decisions and derive insights. The PyText framework, which the company sees as a conduit for AI researchers to move more quickly between experimentation and deployment will be particularly useful for tasks like document classification, sequence tagging, semantic parsing and multitask modeling, among others, Facebook says.
The company has built the framework to fit pretty seamlessly into research and production workflows with an emphasis on robustness and low-latency to meet the company’s real-time NLP needs. The product is responsible for models powering more than a billion daily predictions at Facebook.

Another big highlight is the framework’s modularity, allowing it


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Amazon Comprehend adds customized language lists to machine learning tool

Last year Amazon announced Comprehend, a natural language processing tool to help companies extract common words and phrases from a corpus of information. Today, a week ahead of its Re:invent customer conference, Amazon announced an enhancement to Comprehend that allows developers to build lists of specialized words and phrases without machine learning domain knowledge.
“Today we are excited to bring new customization features to Comprehend, which allow developers to extend Comprehend to identify natural language terms and classify text which is specialized to their team, business or industry,” Matt Wood, GM for deep learning and AI wrote in a blog post announcing the enhancement.
The key aspect of this is that Amazon is handling all of the complexity, allowing developers to add customized lists without having deep machine learning or natural language processing background. “Under the hood, Comprehend will do the heavy lifting to build, train, and host the customized machine learning


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Google Kubeflow, machine learning for Kubernetes, begins to take shape

Ever since Google created Kubernetes as an open source container orchestration tool, it has seen it blossom in ways it might never have imagined. As the project gains in popularity, we are seeing many adjunct programs develop. Today, Google announced the release of version 0.1 of the Kubeflow open source tool, which is designed to bring machine learning to Kubernetes containers.
While Google has long since moved Kubernetes into the Cloud Native Computing Foundation, it continues to be actively involved, and Kubeflow is one manifestation of that. The project was only first announced at the end of last year at Kubecon in Austin, but it is beginning to gain some momentum.
David Aronchick, who runs Kubeflow for Google, led the Kubernetes team for 2.5 years before moving to Kubeflow. He says the idea behind the project is to enable data scientists to take advantage of running machine learning jobs on Kubernetes clusters.


Original URL: http://feedproxy.google.com/~r/Techcrunch/~3/Fdky_5NIppM/

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Juro grabs $2M to take the hassle out of contracts

UK startup Juro, which is applying a “design centric approach” and machine learning tech to help businesses speed up the authoring and management of sales contracts, has closed $2m in seed funding led by Point Nine Capital.
Prior investor Seedcamp also contributed to the round. Juro is announcing Taavet Hinrikus (TransferWise’s co-founder) as an investor now too, as well as Michael Pennington (Gumtree co-founder) and the family office of Paul Forster (co-founder of Indeed.com).
Back in January 2017 the London-based startup closed a $750,000 (£615k) seed round, though CEO and co-founder Richard Mabey tells us that was really better classed as an angel round — with Point Nine Capital only joining “late” in the day.
“We actually could have strung it out to Series A,” he says of the funding that’s being announced now. “But we had multiple offers come in and there is so much of an explosion in demand for the [machine learning] that


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