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Top 10 AI Articles for April 2022
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Top 10 AI Articles for April 2022

Editorial Team
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May 2, 2022
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Last Updated on May 2, 2022 by Editorial Team

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Artificial intelligence (AI) newsletter by Towards AI #18

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Hey everyone. I hope you are all well. In this issue, we dive into our 10 favorite AI articles published by our exceptional contributors in April, including some solid techniques on cross-validation, trends in AI, the infrastructure of enterprise-level social media, and much more.

News

If you haven’t heard, we recently announced an exciting investment in Towards AI to expand the AI platform for the AI community. We have lots of exciting new projects in the pipeline at Towards AI and we are looking forward to making Towards an essential platform for the AI community. Our goal is to make AI more accessible and play a role in ensuring that AI benefits everyone. We aim to build a community that will democratize access to AI by making it easier to learn AI, build AI tools and benefit from AI as a non-professional. Stay tuned for exciting news on the community front very soon!

Automating inventory system management: We covered an inside look at how Gather AI is building the world’s first large-scale and truly autonomous inventory management system. Gather AI’s system uses a fleet of drones powered by its proprietary Autonomy and Machine Learning platform.

Top Ten AI articles for April 2022

Trends in AI — April 2022: This includes an exciting monthly roundup of machine learning research papers and news, including Nvidia’s new h100 GPU, Google’s 540 billion parameter PaLM, Pathways, Kubric, Tensor Programs, bootstrapping reasoning with reasoning, the sparse all-MLP architecture, animating faces with deep learning, and much more.

Cross-validation types and when to use them: This article explains the most common cross-validation methods, when to apply them, and why they’re essential — with code included. Don’t overlook the author’s summary on which strategy to utilize in various situations in the conclusion section.

The combinatorial purged cross-validation method: We have heard of various techniques, but purged cross-validation is one of the most underappreciated on the internet, despite its importance. It’s a time series backtesting method with a lot of robustness. This article explains why standard cross-validation methods fail on time series and how to use purged cross-validation while grasping basic math.

How does Google generate summaries?: Google announced auto-generated summaries in Google Docs in March 2022. How cool is that! Did you know that it’s a mix of algorithms like the PEGASUS model and RNN and Transformer? Learn all about the machine learning-based model behind the new feature.

Transformers: What are they, and how can I make one?: Did the material in Google auto-generated summaries overwhelm you? No worries, we’ve got you covered. This article provides a basic overview of transformers for NLP tasks and instructions for creating a transformer for text generation using PyTorch.

Inside LinkedIn’s machine learning infrastructure: In this era of social networking, who doesn’t utilize LinkedIn? This case study reveals fascinating details regarding LinedIn’s machine learning infrastructure and how it has been built to handle large-scale scenarios.

Beginner tips for getting started with Azure machine learning: This article provides a well-considered collection of things that go well with the DP-100 and the syllabus. Microsoft’s Azure machine learning is a cloud service that assists developers in their data science endeavors. It includes a variety of tools for tracking the progress of your model, versioning your data, securely deploying your model, and more. This is a must-read if you have developed or planning to develop your AI/ML models on the cloud.

Text generation with Markov decision processes: Markov decision processes is among the most efficient methods for dealing with sequences, taming them, or using them as building blocks to generate text automatically. This article introduces the topic and beautifully designed educational visuals as well as implementation code to make learning easier.

All about ensemble techniques: Another graphic chef-d’oeuvre for learning various ensemble techniques such as voting, stacking, bagging, and boosting. It also reveals commonly overlooked techniques such as hard and soft voting, blending and k-folding, bootstrapping, pasting, and random subspace.

Introduction to Intel distribution of OpenVINO toolkit: This article discusses OpenVINO, a deep learning optimization tool. The OpenVINO toolkit offers tools and libraries that optimize neural networks by applying various techniques like pruning, quantization, and speed up inference in a hardware-agnostic approach to Intel architectures. Intel released the toolkit’s most significant update since its launch, which includes more deep-learning models, device portability, and higher inferencing performance with fewer code changes.

We are grateful for your time and hope you enjoyed reading the AI newsletter. 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,

Louie and the team at Towards AI

For previous issues, check out our AI newsletter archive.

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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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