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This AI newsletter is all you need #18
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This AI newsletter is all you need #18

Towards AI Team
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October 25, 2022
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Last Updated on November 1, 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

You must’ve heard about Stability AI and its recent $101 million funding for open-sourced AI, which is such good news for all of us in AI. As exciting as it can be, this shows how much power open-source can have and how promising a company based around that is. I hope this kind of news will propel the open-source way for our field and I deeply hope the big companies will follow this path. I believe giving knowledge away (for free) is much more valuable to a company in the end than selling a product that you are the only one contributing to, where the open-source community ends up catching up to you, forcing you to iterate and compete constantly. In today’s world, you can get help from anyone, anywhere, so why not do so? Why not help science progress and get your products even better by having thousands of hands getting into your work and improving it as Stability AI did with Stable Diffusion? Look what it did. Stable Diffusion is even better and more popular than DALLE now.

Speaking of which, what is even more exciting than open-source getting funding and support right now is diffusion-based models. There are new papers every day using such diffusion models to generate images, generate 3D models, generate videos, edit images from sketches or from text and more. It is just incredible and has completely replaced GANs or transformers, but are they scalable? I’d love to know what you guys think. Is diffusion here to stay and build upon, or is it just another step towards a better approach to come within a few months?

Hottest News

  1. Google’s new AI can hear a snippet of song — and then keep on playing
    This new technique is based on a paper and framework called AudioLM, used for high-quality audio generation with long-term consistency. It maps the input audio to a sequence of discrete tokens and casts audio generation as a language modeling task in this representation space!
  2. A dataset of 5,85 billion CLIP-filtered image-text pairs!
    The dataset LAION-5B is 14x bigger than LAION-400M, previously the biggest openly accessible image-text dataset in the world.
  3. Deloitte’s fifth edition of its State of AI in the Enterprise research report is out!
    Deloitte “surveyed more than 2,600 global executives on how businesses and industries are deploying and scaling artificial intelligence (AI) projects. Most notably, the Deloitte report found that while AI continues to move tantalizingly closer to the core of the enterprise — 94% of business leaders agree that AI is critical to success over the next five years — for some, outcomes seem to be lagging.”

Most interesting papers of the week

  1. Compressed Vision for Efficient Video Understanding
    “We propose a framework enabling research on hour-long videos with the same hardware that can now process second-long videos.”
  2. TOKEN MERGING: YOUR VIT BUT FASTER
    A simple method to increase the throughput of existing ViT (visison transformer) models without needing to train.
  3. Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation
    A Transformer with a novel fine- and coarse-grained attention for music generation. Transformer with a novel fine- and coarse-grained attention for music generation.

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

The Learn AI Together Community section!

Meme of the week!

That is simply amazing work by AI — now that’s what I call fresh sashimi! Meme shared by dimkiriakos#2286.

Featured Community post from the Discord

Shubham Trivedi just shared a notebook on market segmentation and is looking for your feedback! If you have a few minutes available, please have a look and let him know what you think (Shubham.Trivedi#1648 on discord)!

Market Segmentation for Online Healthcare Provider

AI poll of the week!

Join the discussion on Discord.

TAI Curated section

Towards AI Tutorials

At Towards AI, we are big fans of Tutorial & Practical content with code and examples to help learn, understand and implement AI and Machine Learning models and applications. In pursuit of this goal, we have commissioned a series of blog posts (with accompanying Python code and Google Colab files) on various Machine Learning algorithms and topics from our Technical Editor Pratik Shukla. The first (of many) mini series focuses on the Gradient Descent algorithm, from fundamental principles through to a comparison of the variants with elaborated code examples in Python:

  1. The Gradient Descent Algorithm
  2. Mathematical Intuition behind the Gradient Descent Algorithm
  3. The Gradient Descent Algorithm & its Variants

We are also establishing a new Tutorials & Practicals page on our website, where we will host this type of content, both published by our own editorial team and submitted from our contributors, and work to improve its organization and accessibility. We also want to provide a better forum for authors and readers to interact on this content and for readers to ask questions or help as they work through the project. As such, we are also establishing a new Towards AI Tutorials and Practicals forum in our Learn AI Together Community. So please join if you have any questions, let’s learn together!

Article of the week

Gradient Descent Optimization by Rohan Jagtap

For another perspective on Gradient Descent, by chance this week one of our contributors published another Tutorial together with implementation of a gradient descent optimizer in TensorFlow.

Our must-read articles

GELU: Gaussian Error Linear Unit Code (Python, TF, Torch) by Poulinakis Kon

Overview of Computer Vision Tasks & Applications by Youssef Hosni

Why Should Euclidean Distance Not Be the Default Distance Measure? by Harjot Kaur

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

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This AI newsletter is all you need #18 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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} 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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