AI Community AI Community
Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Read by thought-leaders and decision-makers around the world. Phone Number: +1-650-246-9381 Email: pub@towardsai.net
228 Park Avenue South New York, NY 10003 United States
Website: Publisher: https://towardsai.net/#publisher Diversity Policy: https://towardsai.net/about Ethics Policy: https://towardsai.net/about Masthead: https://towardsai.net/about
Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Founders: Roberto Iriondo, , Job Title: Co-founder and Advisor Works for: Towards AI, Inc. Follow Roberto: X, LinkedIn, GitHub, Google Scholar, Towards AI Profile, Medium, ML@CMU, FreeCodeCamp, Crunchbase, Bloomberg, Roberto Iriondo, Generative AI Lab, Generative AI Lab Denis Piffaretti, Job Title: Co-founder Works for: Towards AI, Inc. Louie Peters, Job Title: Co-founder Works for: Towards AI, Inc. Louis-François Bouchard, Job Title: Co-founder Works for: Towards AI, Inc. Cover:
Towards AI Cover
Logo:
Towards AI Logo
Areas Served: Worldwide Alternate Name: Towards AI, Inc. Alternate Name: Towards AI Co. Alternate Name: towards ai Alternate Name: towardsai Alternate Name: towards.ai Alternate Name: tai Alternate Name: toward ai Alternate Name: toward.ai Alternate Name: Towards AI, Inc. Alternate Name: towardsai.net Alternate Name: pub.towardsai.net
5 stars – based on 497 reviews

Frequently Used, Contextual References

TODO: Remember to copy unique IDs whenever it needs used. i.e., URL: 304b2e42315e

Resources

Take our 85+ lesson From Beginner to Advanced LLM Developer Certification: From choosing a project to deploying a working product this is the most comprehensive and practical LLM course out there!

Publication

This AI newsletter is all you need #42
Latest   Machine Learning

This AI newsletter is all you need #42

ifttt-user
0 like
April 11, 2023
Share this post

Last Updated on July 25, 2023 by Editorial Team

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

What happened this week in AI by Louie

AI agents were in the spotlight in the AI and LLM community this week because of projects like Auto-GPT. The concept is that language models such as GPT-4 can “self-prompt” or “auto-prompt” — that is, they can generate and execute their own prompts based on initial input. Through this approach, multiple instances of these models can be assigned different roles and work collaboratively towards achieving a common goal, such as executing a series of tasks, reviewing and fixing mistakes, and even developing and debugging their own code. While current AI Agent projects are still in the early stages and require further refinement, we see tremendous potential for AI Agents. Furthermore, by granting language models access to other tools, such as web search engines, mathematical engines, and information retrieval systems, we believe that many new and emergent capabilities can be unlocked.

After witnessing the rapid pace of progress and continuous newsflow of new models in NLP, we were delighted to see some of these concepts being applied to advance Computer Vision, as demonstrated by Meta’s new Segment Anything Model (SAM). The open-source release of this project is particularly noteworthy, and we hope it will help to reduce the costs and barriers to entry for many computer vision tasks. The Segment Anything Model serves as a promptable foundation model for image segmentation, allowing it to learn a general notion of objects and generate masks for any object in images or videos. Moreover, it can be linked to other tasks and other generative models. For example, it can be connected to stable diffusion to build amazing image-editing end-to-end models like Grounded Segment Anything recently shared on GitHub. We believe this is just the first of many to integrate SAM into a larger and “real-world applicable” project.

– Louie Peters — Towards AI Co-founder and CEO

Hottest News

  1. Anthropic’s $5B, 4-year plan to take on OpenAI

According to documents obtained by TechCrunch, Anthropic aims to raise up to $5 billion in the next two years to challenge competitor OpenAI using its new AI model, Claude-Next. The company also plans to enter more than a dozen major industries.

2. Meta introduced Segment Anything

Meta has introduced Segment Anything, which aims to democratize segmentation. The Segment Anything Model is a versatile, promptable model trained on a diverse dataset. It is available under a permissive open-source license (Apache 2.0).

3. Defamed by ChatGPT: My Own Bizarre Experience with Artificiality of “Artificial Intelligence”

The AI program ChatGPT was found to have generated a false claim of sexual harassment without a factual basis. This incident highlights the biases and dangers of using AI to tackle disinformation. ChatGPT relied on a cited article that was never written and quoted a statement that was never made by the newspaper.

4. Poe’s AI chatbot app now lets you make your bots using prompts

Poe, created by the Q&A site Quora, enables users to experiment with cutting-edge AI technologies through a mobile interface. With Poe, users can build their own chatbots using prompts and an existing bot as the foundation. Additionally, users have the option to pay for access to more advanced bots powered by new language models, such as GPT-4 and Claude+.

5. Thoughts on a Crazy Week in AI News

Last week’s topic was the concerns surrounding recent advancements in systems such as ChatGPT and GPT-4. With significant AI news, including the letter and the Times Magazine post, there is a growing need for greater transparency on the inner workings of these large AI systems, including their training and evaluation processes.

Three 5-minute reads/videos to keep you learning

  1. StackLLaMA: A hands-on guide to train LLaMA with RLHF

This blog post demonstrates the various steps involved in training a LlaMa model to answer questions on Stack Exchange using RLHF. The approach combines Supervised Fine-tuning (SFT), Reward/preference modeling (RM), and Reinforcement Learning from Human Feedback (RLHF). By employing these techniques, HuggingFace has released the StackLLaMA model.

2. A Recipe for Training Large Models

This report offers a valuable compilation of practical tips to consider when training a larger machine learning model. It discusses crucial topics such as initialization and experimentation strategies and offers various examples of useful tools that may come in handy.

3. AI might not steal your job, but it could change it

AI has the potential to impact around 300 million jobs worldwide. The legal industry is among the fields where AI shows promise in automating language-based jobs, but human guidance remains essential. This article presents an unbiased analysis of how AI is likely to impact various industries.

4. ML papers of the week

This is a thoughtfully curated and high-quality list of machine learning (ML) papers that one can regularly refer to. With so much hype around ML in online discussions, it is important to go back to the source and understand the current trends.

5. 5 Lessons Learned from Adding ChatGPT to a Mature Product

Mito shared the concrete lessons they learned from launching their AI feature. The biggest lesson was that adding a chatbot that generates code is easy, but actually figuring out how that code interacts with your existing product is much harder.

Papers & Repositories

  1. Torantulino/Auto-GPT

This is an experimental open-source project aimed at making GPT-4 fully autonomous. Auto-GPT is an app showcasing the capabilities of GPT-4 in autonomously managing businesses to increase their net worth.

2. mayooear/gpt4-pdf-chatbot-langchain

This is a chatbot powered by GPT-4 and LangChain designed for handling large PDF documents. With this technology, users can create a question-answering chatbot that can process several large PDF files. The tech stack used includes LangChain, Pinecone, Typescript, Openai, and Next.js.

3. HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace

HuggingGPT is a framework that utilizes LLMs to connect various AI models in machine learning communities to solve AI tasks. It employs ChatGPT to plan tasks upon receiving a user request, select models based on their available function descriptions in Hugging Face, execute each subtask using the chosen AI model and summarize the response according to the execution results.

4. Summary of ChatGPT/GPT-4 Research

This paper offers a comprehensive survey of ChatGPT and GPT-4 and their potential applications across various domains. The study aims to provide insights into ChatGPT’s capabilities, potential implications, and ethical concerns while also offering direction for future advancements in this field.

5. ChatGPT Survey: Performance on NLP datasets

This is a survey of 141 arXiv papers that compared ChatGPT to other NLP models. It revealed that ChatGPT’s performance was lower than anticipated. The reasons for this included suboptimal utilization, biased results, and incomplete multilingual evaluation.

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

The Learn AI Together Community section!

Announcing Our Discord Job Board

We have launched a new job board on the Learn AI Discord community that allows any company to share their job openings directly with our community, and you can apply within Discord. Simply scroll up the channel to view all the job offers. This job board is powered by our friends at @Freeflow, and once you share a job, you can also share it with other partnered servers to reach more potential candidates. We hope you find this resource helpful.

If you have a job opening to share and are looking for candidates, please take a look at the job board here and consider sharing it with our community.

Weekly AI Podcast

In this week’s What’s AI podcast, Louis Bouchard interviewed Jérémy Cohen, the founder, and CEO of Think Autonomous. This interview delves into the self-driving car industry and the different machine learning-related roles within it, including a discussion on how to become a self-driving car engineer. The conversation also covers topics such as LiDAR technology and its functionality, the comparison between using LiDAR versus cameras on self-driving cars, and demystifying industry myths like the $200,000 salary for a computer vision engineer. Listen on YouTube, Spotify, or Apple Podcasts!

Meme of the week!

Meme shared by DrDub#0108

Featured Community post from the Discord

rmarquet#2048 has shared a GitHub repository containing a collection of artificial intelligence projects, that features detailed documentation and code. It covers various topics and techniques in the field of AI, including computer vision and reinforcement learning. Check it out here and support a fellow community member. Share your feedback in the thread here!

AI poll of the week!

Join the discussion on Discord.

TAI Curated section

Article of the week

Unpacking the Differences: Machine Learning in Research versus Production Environments by Youssef Hosni

This article examines the disparities between machine learning in research environments and production environments. The implementation of machine learning differs significantly depending on the context in which it is used. Furthermore, it delves into the components of production machine learning and the challenges it poses, to provide a better understanding of the complexities involved.

Our must-read articles

The Complete Guide to Spiking Neural Networks by Ali Moezzi

Cosine Similarity for 1 Trillion Pairs of Vectors by Rodrigo Agundez

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.

Job offers

Senior/Principal Software Engineer — Conversational AI @NinjaTech AI (Palo Alto, California)

Lead Site Reliability Engineer @ASAPP (Remote)

Member of Technical Staff, Data Acquisition @Cohere (Remote)

Full stack engineer (Frontend Focus) @Cresta (Remote)

Senior Python Backend Engineer @Chattermill (Remote)

Manager, Data Science @Angi (Remote)

Senior Machine Learning Engineer — Computer Vision @BenchSci (Remote)

Interested in sharing a job opportunity here? Contact sponsors@towardsai.net.

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

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments 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.

Published via Towards AI

, , , , ,
Share this post

Related posts

RAGent: A Multi-Agent PDF Whisperer Built on LangChain + LangGraph
Latest, Machine Learning

RAGent: A Multi-Agent PDF Whisperer Built on LangChain + LangGraph

Dwaipayan Bandyopadhyay
1 like
A Novel and Practical Meta‑Booster for Supervised Learning
Data Science, Latest, Machine Learning

A Novel and Practical Meta‑Booster for Supervised Learning

Shenggang Li
1 like
Your Data is the New Currency: Are You Protecting It?
Artificial Intelligence, Data Science, Latest, Machine Learning

Your Data is the New Currency: Are You Protecting It?

Harshit Kandoi
0 like
Beyond Search: 86.4% MMLU, 77.6 MTEB, and the New Architecture of Policy Understanding
Latest, Machine Learning

Beyond Search: 86.4% MMLU, 77.6 MTEB, and the New Architecture of Policy Understanding

R. Thompson (PhD)
1 like

Feedback ↓

Sign Up for the Course
`; } else { console.error('Element with id="subscribe" not found within the page with class "home".'); } } }); // Remove duplicate text from articles /* Backup: 09/11/24 function removeDuplicateText() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, strong'); // Select the desired elements const seenTexts = new Set(); // A set to keep track of seen texts const tagCounters = {}; // Object to track instances of each tag elements.forEach(el => { const tagName = el.tagName.toLowerCase(); // Get the tag name (e.g., 'h1', 'h2', etc.) // Initialize a counter for each tag if not already done if (!tagCounters[tagName]) { tagCounters[tagName] = 0; } // Only process the first 10 elements of each tag type if (tagCounters[tagName] >= 2) { return; // Skip if the number of elements exceeds 10 } const text = el.textContent.trim(); // Get the text content const words = text.split(/\s+/); // Split the text into words if (words.length >= 4) { // Ensure at least 4 words const significantPart = words.slice(0, 5).join(' '); // Get first 5 words for matching // Check if the text (not the tag) has been seen before if (seenTexts.has(significantPart)) { // console.log('Duplicate found, removing:', el); // Log duplicate el.remove(); // Remove duplicate element } else { seenTexts.add(significantPart); // Add the text to the set } } tagCounters[tagName]++; // Increment the counter for this tag }); } removeDuplicateText(); */ // Remove duplicate text from articles function removeDuplicateText() { const elements = document.querySelectorAll('h1, h2, h3, h4, h5, strong'); // Select the desired elements const seenTexts = new Set(); // A set to keep track of seen texts const tagCounters = {}; // Object to track instances of each tag // List of classes to be excluded const excludedClasses = ['medium-author', 'post-widget-title']; elements.forEach(el => { // Skip elements with any of the excluded classes if (excludedClasses.some(cls => el.classList.contains(cls))) { return; // Skip this element if it has any of the excluded classes } const tagName = el.tagName.toLowerCase(); // Get the tag name (e.g., 'h1', 'h2', etc.) // Initialize a counter for each tag if not already done if (!tagCounters[tagName]) { tagCounters[tagName] = 0; } // Only process the first 10 elements of each tag type if (tagCounters[tagName] >= 10) { return; // Skip if the number of elements exceeds 10 } const text = el.textContent.trim(); // Get the text content const words = text.split(/\s+/); // Split the text into words if (words.length >= 4) { // Ensure at least 4 words const significantPart = words.slice(0, 5).join(' '); // Get first 5 words for matching // Check if the text (not the tag) has been seen before if (seenTexts.has(significantPart)) { // console.log('Duplicate found, removing:', el); // Log duplicate el.remove(); // Remove duplicate element } else { seenTexts.add(significantPart); // Add the text to the set } } tagCounters[tagName]++; // Increment the counter for this tag }); } removeDuplicateText(); //Remove unnecessary text in blog excerpts document.querySelectorAll('.blog p').forEach(function(paragraph) { // Replace the unwanted text pattern for each paragraph paragraph.innerHTML = paragraph.innerHTML .replace(/Author\(s\): [\w\s]+ Originally published on Towards AI\.?/g, '') // Removes 'Author(s): XYZ Originally published on Towards AI' .replace(/This member-only story is on us\. 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 = [ /* ' ' + '

Subscribe to our AI newsletter!

' + */ '

Take our 85+ lesson From Beginner to Advanced LLM Developer Certification: From choosing a project to deploying a working product this is the most comprehensive and practical LLM course out there!

'+ '

Towards AI has published Building LLMs for Production—our 470+ page guide to mastering LLMs with practical projects and expert insights!

' + '
' + '' + '' + '

Note: Content contains the views of the contributing authors and not Towards AI.
Disclosure: This website may contain sponsored content and affiliate links.

' + 'Discover Your Dream AI Career at Towards AI Jobs' + '

Towards AI has built a jobs board tailored specifically to Machine Learning and Data Science Jobs and Skills. Our software searches for live AI jobs each hour, labels and categorises them and makes them easily searchable. Explore over 10,000 live jobs today with Towards AI Jobs!

' + '
' + '

🔥 Recommended Articles 🔥

' + 'Why Become an LLM Developer? Launching Towards AI’s New One-Stop Conversion Course'+ 'Testing Launchpad.sh: A Container-based GPU Cloud for Inference and Fine-tuning'+ 'The Top 13 AI-Powered CRM Platforms
' + 'Top 11 AI Call Center Software for 2024
' + 'Learn Prompting 101—Prompt Engineering Course
' + 'Explore Leading Cloud Providers for GPU-Powered LLM Training
' + 'Best AI Communities for Artificial Intelligence Enthusiasts
' + 'Best Workstations for Deep Learning
' + 'Best Laptops for Deep Learning
' + 'Best Machine Learning Books
' + 'Machine Learning Algorithms
' + 'Neural Networks Tutorial
' + 'Best Public Datasets for Machine Learning
' + 'Neural Network Types
' + 'NLP Tutorial
' + 'Best Data Science Books
' + 'Monte Carlo Simulation Tutorial
' + 'Recommender System Tutorial
' + 'Linear Algebra for Deep Learning Tutorial
' + 'Google Colab Introduction
' + 'Decision Trees in Machine Learning
' + 'Principal Component Analysis (PCA) Tutorial
' + 'Linear Regression from Zero to Hero
'+ '
', /* + '

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.

',*/ ]; var replaceText = { '': '', '': '', '
': '
' + ctaLinks + '
', }; Object.keys(replaceText).forEach((txtorig) => { //txtorig is the key in replacetext object const txtnew = replaceText[txtorig]; //txtnew is the value of the key in replacetext object let entryFooter = document.querySelector('article .entry-footer'); if (document.querySelectorAll('.single-post').length > 0) { //console.log('Article found.'); const text = entryFooter.innerHTML; entryFooter.innerHTML = text.replace(txtorig, txtnew); } else { // console.log('Article not found.'); //removing comment 09/04/24 } }); var css = document.createElement('style'); css.type = 'text/css'; css.innerHTML = '.post-tags { display:none !important } .article-cta a { font-size: 18px; }'; document.body.appendChild(css); //Extra //This function adds some accessibility needs to the site. function addAlly() { // In this function JQuery is replaced with vanilla javascript functions const imgCont = document.querySelector('.uw-imgcont'); imgCont.setAttribute('aria-label', 'AI news, latest developments'); imgCont.title = 'AI news, latest developments'; imgCont.rel = 'noopener'; document.querySelector('.page-mobile-menu-logo a').title = 'Towards AI Home'; document.querySelector('a.social-link').rel = 'noopener'; document.querySelector('a.uw-text').rel = 'noopener'; document.querySelector('a.uw-w-branding').rel = 'noopener'; document.querySelector('.blog h2.heading').innerHTML = 'Publication'; const popupSearch = document.querySelector$('a.btn-open-popup-search'); popupSearch.setAttribute('role', 'button'); popupSearch.title = 'Search'; const searchClose = document.querySelector('a.popup-search-close'); searchClose.setAttribute('role', 'button'); searchClose.title = 'Close search page'; // document // .querySelector('a.btn-open-popup-search') // .setAttribute( // 'href', // 'https://medium.com/towards-artificial-intelligence/search' // ); } // Add external attributes to 302 sticky and editorial links function extLink() { // Sticky 302 links, this fuction opens the link we send to Medium on a new tab and adds a "noopener" rel to them var stickyLinks = document.querySelectorAll('.grid-item.sticky a'); for (var i = 0; i < stickyLinks.length; i++) { /* stickyLinks[i].setAttribute('target', '_blank'); stickyLinks[i].setAttribute('rel', 'noopener'); */ } // Editorial 302 links, same here var editLinks = document.querySelectorAll( '.grid-item.category-editorial a' ); for (var i = 0; i < editLinks.length; i++) { editLinks[i].setAttribute('target', '_blank'); editLinks[i].setAttribute('rel', 'noopener'); } } // Add current year to copyright notices document.getElementById( 'js-current-year' ).textContent = new Date().getFullYear(); // Call functions after page load extLink(); //addAlly(); setTimeout(function() { //addAlly(); //ideally we should only need to run it once ↑ }, 5000); }; function closeCookieDialog (){ document.getElementById("cookie-consent").style.display = "none"; return false; } setTimeout ( function () { closeCookieDialog(); }, 15000); console.log(`%c 🚀🚀🚀 ███ █████ ███████ █████████ ███████████ █████████████ ███████████████ ███████ ███████ ███████ ┌───────────────────────────────────────────────────────────────────┐ │ │ │ Towards AI is looking for contributors! │ │ Join us in creating awesome AI content. │ │ Let's build the future of AI together → │ │ https://towardsai.net/contribute │ │ │ └───────────────────────────────────────────────────────────────────┘ `, `background: ; color: #00adff; font-size: large`); //Remove latest category across site document.querySelectorAll('a[rel="category tag"]').forEach(function(el) { if (el.textContent.trim() === 'Latest') { // Remove the two consecutive spaces (  ) if (el.nextSibling && el.nextSibling.nodeValue.includes('\u00A0\u00A0')) { el.nextSibling.nodeValue = ''; // Remove the spaces } el.style.display = 'none'; // Hide the element } }); // Add cross-domain measurement, anonymize IPs 'use strict'; //var ga = gtag; ga('config', 'G-9D3HKKFV1Q', 'auto', { /*'allowLinker': true,*/ 'anonymize_ip': true/*, 'linker': { 'domains': [ 'medium.com/towards-artificial-intelligence', 'datasets.towardsai.net', 'rss.towardsai.net', 'feed.towardsai.net', 'contribute.towardsai.net', 'members.towardsai.net', 'pub.towardsai.net', 'news.towardsai.net' ] } */ }); ga('send', 'pageview'); -->