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#37 GraphRAG, SAM 2, Embeddings, Discord Chatbot, LSTM Project!
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#37 GraphRAG, SAM 2, Embeddings, Discord Chatbot, LSTM Project!

Towards AI Editorial Team
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August 22, 2024
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Last Updated on September 2, 2024 by Editorial Team

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! This week, we dive into applied AI developments, fundamental concepts, real-world discussions, and more. Dive in and enjoy the read!

What’s AI Weekly

This week in What’s AI, I focus on the new hype in LLMs: GraphRAG. GraphRAG is a powerful extension to the Retrieval-Augmented Generation (RAG) stack, making a lot of noise thanks to Microsoft and LlamaIndex’s contributions. But the question remains: Should YOU be using it? That’s what I covered in this week’s issue. Read it here!

— Louis-François Bouchard, Towards AI Co-founder & Head of Community

This issue is brought to you thanks to AI & Big Data Expo:

Book your free conference and expo ticket to AI & Big Data Expo Europe 2024

Join us for one of the most anticipated technology events of the year, the AI and Big Data Expo Europe 2024.

Event Highlights:

  • 6 Co-Located Events — A comprehensive exploration of AI and Big Data with six co-located events.
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Date & Location: 1–2 October 2024 at the RAI, Amsterdam.

Your in-person ticket will also grant you access to the co-located events exploring IoT Tech, Intelligent Automation, Cyber Security & Cloud, Unified Communications, Edge Computing, and Digital Transformation!

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Learn AI Together Community section!

Featured Community post from the Discord

Gere030199 has created Miao AI, a text and voice chatbot that can be used directly in your Discord Server. Miao can perform various automations such as in-depth web searches, image generation/modification, and analyzing attached files/images. It is also perfect for resumes, analysis, language learning, coding, math, and more! Check it out here and support a fellow community member. Share your thoughts and questions in the Discord thread!

AI poll of the week!

That’s what makes us a community: our shared love for AI. Beyond the hype, what do you think is the most essential concept/tool for anyone joining AI now? Tell us in the thread on Discord.

Collaboration Opportunities

The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week!

1. Mangoo1814 is using sklearn and TensorFlow to predict stock market prices. There are many things to test, such as weighing different TAs, implementing graphical formulas, and using LSTM and RNN with different data types. They are looking for someone to partner with, so if this sounds fun, get in touch in the thread!

2. Nicepheonix is looking for someone to collaborate with on a keyword detection project. If you can help, connect in the thread!

Meme of the week!

Meme shared by ghost_in_the_machine

TAI Curated section

Article of the week

Context Retrieval Optimization with Gaussian Mixture Model for Complex Queries by Vitaly Bulgakov

This article explores how GMM can enhance the efficiency of information retrieval, making it easier to tackle complex queries. It is a great read for AI enthusiasts and researchers who want to explore the future of context-aware systems.

Our must-read articles

1. Instruction Fine-Tuning Large Language Models for Summarization: Step-by-Step Guide by Youssef Hosni

This tutorial walks you through setting up your working environment, including downloading the necessary dependencies and loading the required dataset and LLM. It also demonstrates how to test the model using zero-shot inferencing, establishing a baseline for comparison.

2. SAM 2 (Segment Anything Model 2) is Amazing, But We Need to understand SAM 1 by JAIGANESAN

You might have seen the exciting news about SAM 2 from Meta, along with some amazing videos showcasing its capabilities. The Segment Anything Model (SAM) is indeed impressive and this article breaks down the parts of SAM.

3. Embeddings — The Blueprint of Contextual AI by Abhinav Kimothi

This article explains embeddings and how they are revolutionizing the way machines understand context, enabling more accurate and nuanced interactions. It is a must-read for anyone interested in the future of AI and natural language processing.

4. Explainable AI for CLIP: The Architecture Explanation and its Application for Segment Anything by Yuki Shizuya

Explainability is one of the crucial topics for AI models. Recent complicated AI tends to be a black box algorithm, making it difficult for humans to understand why the AI delivers those results. This article introduces the architecture of CLIP_Surgery and its application.

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.

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.

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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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