Let’S Build An Insultbot

在线内容审核是一个必须决定哪些内容保持在线，哪些内容应该被审核甚至删除的过程；没有人愿意被调解，当我成为 YouTube、Facebook 和 Twitter 上数次在线调解的目标时，我就不高兴了。“凝固汽油弹女孩”是一个著名的案例，Facebook 将其归类为裸体照片，这是一张历史图片。“甚至连挪威首相本人也将照片转发到 Facebook，但很快就被删除了。” 互联网的保管人：平台、内容审核和塑造社交媒体的隐藏决策塔尔顿·吉莱斯皮 (Tarleton Gillespie) 的书什么是安全地在线审核内容的最佳和最有效的方法？几位编程专家正在竭尽全力。就像在弦论中一样，他们收集了最好的，但还没有实际的答案出来。在这门课程中，我想分享一个同样不容易解决的小案例：直接侮辱（即骂人）。机器人是一种经典的模式识别：它给出一个数字，你必须决定你对侮辱的容忍度。这个数字，已经是深度学习的标准，是一种适当的、松散的说法。我们将使用两个 MIT 许可的谷歌公共工具：TensorFlow.js 和 Angular。前者是一个深度学习工具，可以自由创建智能Web应用，而后者是一个免费的框架，可以创建前端应用，不需要创建服务器。松散地说。我们将使用两个 MIT 许可的 Google 公共工具：TensorFlow.js 和 Angular。前者是一个深度学习工具，可以自由创建智能Web应用，而后者是一个免费的框架，可以创建前端应用，不需要创建服务器。松散地说。我们将使用两个 MIT 许可的 Google 公共工具：TensorFlow.js 和 Angular。前者是一个深度学习工具，可以自由创建智能Web应用，而后者是一个免费的框架，可以创建前端应用，不需要创建服务器。

Published 12/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.76 GB | Duration: 1h 51m

Getting ready to welcome trollers and alike

What you’ll learn

How to use a pretrained model for content moderation
Using Angular Material, basics
Implementing artificila intelligence in Angular
How to use TensorFlowjs (deep learning)

Requirements

No programming experience needed, basics of Angular may be beneficial

Description

Online Content moderation is the process by which one must decide what stays online vs. what should be moderated or even eliminated; no one is happy to be moderated, I was not when I was the target of several online moderations on YouTube, Facebook and Twitter. The “Napalm girl” was a famous case in which Facebook classified as nudity, which is a historical picture.”even the prime minister of Norway herself, reposted the photo to Facebook, only to have it quickly removed.” Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media Book by Tarleton GillespieWhat is the best and most efficient way to safely moderate content online?Several programming savvies are doing their best. Like in String Theory, they gather the best, but no practical answer came out yet.On this course, I want to share a trivial case, which was not also easy to solve: direct insult (i.e., calling people names).The bot is a classical pattern recognition: it gives out a number, and you must decide your tolerance for insults. This number, already standard on deep learning, is a sort of properly, loosely speaking.We are going to use two MIT license Google public tools: TensorFlow.js and Angular. The former is a deep learning tool for freely creating smart web application, whereas the latter is a free framework for creating frontend application, no need to create servers.

Overview
Section 1: Preliminaries

Lecture 1 Meet our Gitbook, an ebook for assisting on learning

Lecture 2 Meet our app, a bot for detecting insults

Lecture 3 Meet our github repository

Section 2: Getting started

Lecture 4 Initials

Lecture 5 Installing @tensorflow-models/toxicity: getting ready to be insulted by trollers

Lecture 6 Getting to know the library: a basic parametrization

Lecture 7 Getting to know the library: “******”, testing this insult

Section 3: Creating an User Interface using Angular Material

Lecture 8 Getting started

Lecture 9 Getting started, part II

Lecture 10 Using Angular Material to create a card

Lecture 11 Adding a button to our card

Lecture 12 Adding an input for moderation to our card

Lecture 13 Connect button to our logic

Lecture 14 Preparing the code for showing the moderation result from input

Lecture 15 Just some final adjustments, and we are ready to go: “you suck!”

Lecture 16 Closing lines, just a final test

Section 4: Take a pick on our course before buying it!

Lecture 17 Error type I and II on online content moderation: parametrizing @tensorflow-mode

Lecture 18 “****” is a way to insult black people in America; is our bot able to flag it?

Lecture 19 Getting to know me briefly

Angular programmers interested in artificial intelligence,Artificial intelligence practitioners interested in web development,People interested in online content moderation