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This AI newsletter is all you need #21
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This AI newsletter is all you need #21

Towards AI Team
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November 15, 2022
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Last Updated on November 16, 2022 by Editorial Team

Author(s): Towards AI Editorial 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.

What happened this week in AI by Louis

The lawsuit that could rewrite the rules of AI copyright…

Microsoft, GitHub, and OpenAI (all companies behind GitHub Copilot) are being sued for allegedly violating copyright law by reproducing open-source code using AI. The lawsuit, filed in California last week, is considered the first major copyright lawsuit involving an “AI system”, with potential damages reaching $9B.

This lawsuit is mainly because, as most of you may know, Copilot has been found to regurgitate long sections of licensed code without providing credit, which was discovered by many users. This is also something that could cause problems for image models, as similar things could happen where we generate back images or sensitive data from the training set. Because of this, the outcome of this lawsuit will set an important precedent for future instances of AI infringing on copyright, IP, or other restricted information. It will also surely spark investigation into how to prevent this from happening!

P.S.: we have the results for the NeurIPS giveaway powered by our friends at Activeloop! Congratulations to Simon and Arav! I am excited to see you there in person!

Hottest News

  1. Midjourney V4 is out!
    V4 is a new model trained from scratch by Midjourney. It has a totally new codebase and has been in the works for 9 months! Try it out on their Discord server.
  2. Deepmind Scholarships — building a stronger and more inclusive AI community
    These scholarships offer students from underrepresented groups financial support to study graduate courses relating to AI and adjacent fields, support from a DeepMind mentor, and opportunities to attend leading AI academic conferences.
    “Our vision is to help make the AI ecosystem more representative of society. The DeepMind scholarship programme aims to help extend opportunities and remove barriers for underrepresented groups so they can bring their unique perspectives and backgrounds to the exciting field of AI.”
  3. What Do Businesses Want From AI?
    In a study commissioned by Capital One, Forrester Consulting surveyed 150 data management decision-makers in North America about their organizations’ ML goals, challenges, and plans to operationalize ML. Respondents revealed that ML is beginning to drive business impact, with automated anomaly detection as the top priority in the next one year to three years.

Some additional key highlights:

  • 53% of respondents plan to improve business efficiency by leveraging ML.
  • 73% of respondents find transparency, traceability, and explainability of data flows challenging.
  • 67% of respondents intend to leverage partnerships to fill ML staff gaps.

Most interesting papers of the week

  1. TAP-Vid: A Benchmark for Tracking Any Point in a Video
    A new benchmark for tracking points on physical surfaces in videos!
  2. FactorMatte: Redefining Video Matting for Re-Composition Tasks
    They propose factor matting, an alternative formulation of the video matting problem in terms of counterfactual video synthesis that is better suited for re-composition tasks.
  3. Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale
    They find that accessible text-to-image models amplify dangerous and complex stereotypes, which are difficult to predict and not easily mitigated by users or model owners.

Enjoy these papers and news summaries? Get a daily recap in your inbox!

The Learn AI Together community section!

Meme of the week!

Yes, the time has come! AI can now even help you find love! Meme shared by vladm#8251.

Featured Community post from the Discord

Fausto_ST has made a directory of cool AI-image tools, combining his interest in this trend with his #100DaysOfNoCode challenge on Twitter 🚀

Check it out, it’s really well done and exhaustive! A really cool project that also benefits others interested in AI x Image tools 👇

https://ai-image-tools.softr.app/

AI poll of the week!

What do you think? Join the discussion on Discord.

TAI Curated section

Article of the week

Ant Colony Optimization: An overview by Chinmay Bhalerao

A group of unique problem-solving techniques and approaches that are inspired by natural processes are known as “nature-inspired algorithms.” The ant colony optimization algorithm (ACO), used in computer science and operations research, is a probabilistic method for resolving computing issues that may be simplified to finding appropriate paths through graphs. This blog briefly explains the intuition and math behind ACO.

Our must-read articles

Deal With an Imbalanced Dataset With TensorFlow, LightGBM, and CatBoost by Konstantin Pluzhnikov

Detecting Bad Posture With Machine Learning by Leon Eversberg

If you are interested in publishing with Towards AI, check our guidelines and sign up. We will publish your work to our network if it meets our editorial policies and standards.

Featured jobs this week

Data Scientist @ Alethea Group (Remote, US)

Senior/Staff Machine Learning Engineer, Infrastructure & Earnin (Remote)

AI Content Fellowship @ Deepgram (Remote)

Machine Learning Engineer, Copilot Model Improvements @ Github (Remote, US)

Machine Learning Engineering Manager @ Verana Health (Remote)

AI Implementation Manager (Healthcare) @ ClosedLoop (Remote)

Interested in sharing a job opportunity here? Contact sponsors@towardsai.net or post the opportunity in our #hiring channel on discord!

If you are preparing your next machine learning interview, don’t hesitate to check out our leading interview preparation website, confetti!

This AI newsletter is all you need #21 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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Established in Pittsburgh, Pennsylvania, US — Towards AI Co. is the world’s leading AI and technology publication focused on diversity, equity, and inclusion. We aim to publish unbiased AI and technology-related articles and be an impartial source of information. Read by thought-leaders and decision-makers around the world. We have thousands of contributing writers from university professors, researchers, graduate students, industry experts, and enthusiasts. We receive millions of visits per year, have several thousands of followers across social media, and thousands of subscribers. All of our articles are from their respective authors and may not reflect the views of Towards AI Co., its editors, or its other writers. | Information for authors → https://contribute.towardsai.net | Terms → https://towardsai.net/terms/ | Privacy → https://towardsai.net/privacy/ | Members → https://members.towardsai.net/ | Shop → https://ws.towardsai.net/shop | Is your company interested in working with Towards AI? → https://sponsors.towardsai.net

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Upgrade to access all of Medium\./g, ''); // Removes 'This member-only story...' }); //Load ionic icons and cache them if ('localStorage' in window && window['localStorage'] !== null) { const cssLink = 'https://code.ionicframework.com/ionicons/2.0.1/css/ionicons.min.css'; const storedCss = localStorage.getItem('ionicons'); if (storedCss) { loadCSS(storedCss); } else { fetch(cssLink).then(response => response.text()).then(css => { localStorage.setItem('ionicons', css); loadCSS(css); }); } } function loadCSS(css) { const style = document.createElement('style'); style.innerHTML = css; document.head.appendChild(style); } //Remove elements from imported content automatically function removeStrongFromHeadings() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, h6, span'); elements.forEach(el => { const strongTags = el.querySelectorAll('strong'); strongTags.forEach(strongTag => { while (strongTag.firstChild) { strongTag.parentNode.insertBefore(strongTag.firstChild, strongTag); } 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); } else { console.log( `Cannot replace the keywords in article with id ${articles[x].id}` ); } } } else { console.log('No articles found.'); } } let key; //not part of script, added for (key in keywordsAndLinks) { //key is the object in keywords and links object i.e ds, ml, ai for (let i = 0; i < keywordsAndLinks[key].keywords.length; i++) { //keywordsAndLinks[key].keywords is the array of keywords for key (ds, ml, ai) //keywordsAndLinks[key].keywords[i] is the keyword and keywordsAndLinks[key].link is the link //keyword and link is sent to searchreplace where it is then replaced using regular expression and replace function articleFilter( keywordsAndLinks[key].keywords[i], keywordsAndLinks[key].link ); } } function cleanLinks() { // (making smal functions is for DRY) this function gets the links and only keeps the first 2 and from the rest removes the anchor tag and replaces it with its text function removeLinks(links) { if (links.length > 1) { for (let i = 2; i < links.length; i++) { links[i].outerHTML = links[i].textContent; } } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${c.id} .entry-content a.ds-link`); mllinks = document.querySelectorAll(`#${c.id} .entry-content a.ml-link`); ailinks = document.querySelectorAll(`#${c.id} .entry-content a.ai-link`); nllinks = document.querySelectorAll(`#${c.id} .entry-content a.ntrl-link`); deslinks = document.querySelectorAll(`#${c.id} .entry-content a.des-link`); tdlinks = document.querySelectorAll(`#${c.id} .entry-content a.td-link`); iaslinks = document.querySelectorAll(`#${c.id} .entry-content a.ias-link`); 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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',*/ ]; var replaceText = { '': '', '': '', '
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