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AI Facts and Myths, an Essay by ML Researchers on the Social Dilemma, And +!
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AI Facts and Myths, an Essay by ML Researchers on the Social Dilemma, And +!

Roberto Iriondo
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December 1, 2021
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Last Updated on January 7, 2022 by Editorial Team

Author(s): Towards AI Team

Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses.

Artificial Intelligence (AI) Newsletter by Towards AI #17

If you have trouble reading this email, see it on a web browser.

Hey everyone. I hope you are well. In this issue, we dive into an ML research essay on the social dilemma, some exciting deals to make your AI-holiday shopping even better, the misconceptions of AI and the challenges of natural language generation (NLG), an updated chart of all significant neural networks, and the ML research paper highlight of the month.

This issue is brought to you thanks to our friends at Amazon Science:

Interested in working at Amazon? Check out the Amazon Science website to learn more about the company’s unique approach to customer-obsessed science, and how it helps attract some of the brightest minds in artificial intelligence, machine learning, and related fields. Find blog posts and research papers from Amazon scientists and academics, including which conferences they’ll be attending, and how to collaborate. View available jobs.

Before we get started, I wanted to let you know that we have decided to publish the latest machine learning research every weekday after 8 PM ET. All based on your opinion, it seems that you are even hungrier for more — research in AI, CV, NLP, and others. So stay tuned; we’ll keep you updated with what we decide on doing to keep you up to date.

All right, so let’s get to it.

An Essay by ML Researchers on “The Social Dilemma”

Deja Vu much? Researchers at Carnegie Mellon argue on the blog “When Curation Becomes Creation: Algorithms, microcontent, and the vanishing distinction between platforms and creators” the challenges of implementing the right policies and regulations that balance ethics, the economy, individual rights, and proprietary data. The thorough essay showcases the gray areas between every piece of content published and distributed on the internet and what we can do about it.

AI-Holiday Deals

Ho ho ho! I hope I don’t bore you too much with these. But! In case you are looking for a new AI rig, we just updated our shopping recommendations for deep learning laptops or AI workstations. So please take a look, and as always, all feedback is welcome — if you do get one (and have browser cookies enabled), you’ll be supporting us, and we genuinely appreciate it.

AI Misconceptions and the Challenges of NLG

Artificial intelligence’s public point of view can be strongly misguided. This blog post dives on AI facts and myths, highlighting Stanford researcher Abigail See, which showcases some genuinely exciting problems with the public’s understanding of AI, to the challenges of natural language generation, along with what’s in the future for generative models — as more and more become part of our daily lives.

Neural Network Topologies

We just updated our chart on the types of neural networks and their applications, diving from the perceptron all the way to complex neural networks, such as a Neural Turing Machine (NTM), to generative adversarial networks (GANs), and deep convolutional inverse graphics networks.

ML Research Paper Highlight of the Month

Wolfgang Konen and Samineh Bagheri from the University of Applied Sciences in Germany published a cool paper called “Final Adaptation Reinforcement Learning for N-Player Games,” which concentrates n-tuple based reinforcement learning algorithms for games, specifically new ones tackling TD-, SARSA-, and Q-learning. All reproducible and open-source code for the paper is available on Github.

Anyhow, I am super thankful for your time. If you enjoy the newsletter? Please consider subscribing if you haven’t yet or share it with your friends and colleagues — it is genuinely appreciated.

Thank you for joining us! Until next time,

Roberto and the team at Towards AI

For previous issues, check out our AI newsletter archive.

Where to follow us:

[ Facebook ] |[ Twitter ]| [ Instagram ]| [ LinkedIn ] | [ Github ] | [ Google News ]

AI Facts and Myths, an Essay by ML Researchers on the Social Dilemma, And +! was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

 

Join thousands of data leaders on the AI newsletter. It’s free, we don’t spam, and we never share your email address. Keep up to date with the latest work in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

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Roberto Iriondo
Roberto Iriondo has established a venerable reputation in the domain of AI marketing, particularly as a Executive Marketing Advisor and consultant. His career trajectory commenced at Carnegie Mellon University under Manuela Veloso and Roni Rosenfeld's leadership, where he served as a front-end engineer and marketing lead, showcasing his proficiency in technical development by creating platforms such as the renowned CMU Machine Learning Research blog. As the Head of Content at Snorkel AI from 2021 to 2022, and subsequently at Cohere from 2022 to 2023, Roberto's expertise in shaping brand narratives and crafting effective content strategies became evident. Alongside these roles, he exhibited an astute understanding of web growth strategy, as manifested in his marketing agency's operations from 2017 to 2021. This agency catered to emerging businesses with a focus on SEO services. Furthermore, his involvement in strategic roles at Towards AI, both before and after its 2022 acquisition, underscores his multifaceted contributions to the AI marketing landscape. As a builder and strategist by heart, his work has helped several companies achieve business goals and significantly increase ROI, from Amazon Science, MBZUAI, Gather AI, Determined AI, Lambda Labs, Udacity, and many more.

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} strongTag.remove(); }); }); } removeStrongFromHeadings(); "use strict"; window.onload = () => { /* //This is an object for each category of subjects and in that there are kewords and link to the keywods let keywordsAndLinks = { //you can add more categories and define their keywords and add a link ds: { keywords: [ //you can add more keywords here they are detected and replaced with achor tag automatically 'data science', 'Data science', 'Data Science', 'data Science', 'DATA SCIENCE', ], //we will replace the linktext with the keyword later on in the code //you can easily change links for each category here //(include class="ml-link" and linktext) link: 'linktext', }, ml: { keywords: [ //Add more keywords 'machine learning', 'Machine learning', 'Machine Learning', 'machine Learning', 'MACHINE LEARNING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ai: { keywords: [ 'artificial intelligence', 'Artificial intelligence', 'Artificial Intelligence', 'artificial Intelligence', 'ARTIFICIAL INTELLIGENCE', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, nl: { keywords: [ 'NLP', 'nlp', 'natural language processing', 'Natural Language Processing', 'NATURAL LANGUAGE PROCESSING', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, des: { keywords: [ 'data engineering services', 'Data Engineering Services', 'DATA ENGINEERING SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, td: { keywords: [ 'training data', 'Training Data', 'training Data', 'TRAINING DATA', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, ias: { keywords: [ 'image annotation services', 'Image annotation services', 'image Annotation services', 'image annotation Services', 'Image Annotation Services', 'IMAGE ANNOTATION SERVICES', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, l: { keywords: [ 'labeling', 'labelling', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, pbp: { keywords: [ 'previous blog posts', 'previous blog post', 'latest', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, mlc: { keywords: [ 'machine learning course', 'machine learning class', ], //Change your article link (include class="ml-link" and linktext) link: 'linktext', }, }; //Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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mlclinks = document.querySelectorAll(`#${c.id} .entry-content a.mlc-link`); llinks = document.querySelectorAll(`#${c.id} .entry-content a.l-link`); pbplinks = document.querySelectorAll(`#${c.id} .entry-content a.pbp-link`); //sending the anchor tags list of each article one by one to remove extra anchor tags removeLinks(dslinks); removeLinks(mllinks); removeLinks(ailinks); removeLinks(nllinks); removeLinks(deslinks); removeLinks(tdlinks); removeLinks(iaslinks); removeLinks(mlclinks); removeLinks(llinks); removeLinks(pbplinks); } }); } //To remove extra achor tags of each category (ds, ml, ai) and only have 2 of each category per article cleanLinks(); */ //Recommended Articles var ctaLinks = [ /* ' ' + '

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