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This AI newsletter is all you need #26
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This AI newsletter is all you need #26

Towards AI Team
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December 20, 2022
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Last Updated on December 30, 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

We were interested to see two new models out this week which we think can increase the flexibility and capabilities of ML towards search, document and data processing.

OpenAI released its new and improved embedding model which outperforms Davinci at most tasks at a 99.8% lower price. The new model replaces five separate models for text search, text similarity, and code search, while increasing context length 4x and reducing embedding size. The model is a more powerful tool for natural language processing and code tasks and we think it has lots of interesting applications including for semantic search.

While not related, we were also interested to see Microsoft release its new Universal Document Processing (UDOP) model. It is a foundation Document AI model for document understanding and generation tasks, where text is structurally embedded in documents, together with other information like symbols, figures, and style. It sets the state-of-the-art on nine Document AI tasks across various domains and ranks first on the Document Understanding Benchmark leaderboard.

We think these models are both potentially parts of a toolset for building AI applications where accuracy, relevancy and reliability of data recall and understanding are important.

Towards AI and Learn Prompting competition collaboration

We are also organizing a fun competition around prompting in collaboration with our friend Sander and the open-source course Learn Prompting! We will be kicking off the competition this week on the Learn AI Together Discord server (with a fun live stream!) and will announce it in next week’s newsletter too. Stay tuned — the competition (accessible for all) will hold until December 31st! Join us on Discord and enter our competition to have the chance to win cool prizes!

Towards AI Job offer

We are continuing to look for contractors to join Towards AI (~10 hours per month) to work on building learning resources (mostly open-source) for our community. We are looking for experience in one or both of the following:

  • NLP (LLMs implementation & prompting).
  • Image generation models (implemented stable diffusion or other image synthesis model and experience prompting (or fine-tuning) with them).

Message me @Louis B on Discord or by email for more information!

Hottest News

  1. Stanford CRFM’s PubMedGPT 2.7B
    A new 2.7 billion parameter language model trained on biomedical abstracts and papers. The GPT-style model is capable of strong performance on a range of biomedical natural language processing tasks, including a new state of the art performance on the MedQA biomedical question answering task.
  2. The state of AI in 2022 — and a half decade in review
    This report offers an in-depth look at the past five years in the field of artificial intelligence, including statistics on AI adoption by companies, the most popular use cases for AI, and the level of investment in the technology and development in AI, and more…
  3. DeepMind’s AlphaCode Conquers Coding, Performing as Well as Humans
    DeepMind’s AI system has demonstrated impressive results in coding tasks, performing as well as humans on tests with 5,000 participants. This article explains the unique features of this AI and how it is able to achieve such high levels of performance. Is the future of coding already here?
  4. Geoffrey Hinton proposes an alternative to backpropagation: the Forward-Forward Algorithm
    In a new paper, Geoffrey Hinton introduces the Forward-Forward (FF) algorithm as an alternative to backpropagation. @martin_gorner has summarized the key points of the paper in a Twitter thread. Hinton argues that, while it is unlikely that the human brain uses backpropagation to learn, FF is a possible alternative, can be very power efficient and is well suited for self-supervised learning.

Three 5-minute reads/videos to keep you learning

  1. Understanding Convolutions in Probability: A Mad-Science Perspective
    This article explores the concept of convolutions from a probability perspective, including how to use them, how to compute them, and their mathematical definition. It provides clear examples and a visual map to help simplify the learning process.
  2. Some Basic Image Preprocessing Operations for Beginners in Python
    In this article, Rashida discusses some essential image preprocessing operations using OpenCV in Python, including translation, resizing, and cropping. If you are new to image processing and want to learn about these basic techniques for tasks such as image classification, object detection, or optical character recognition, this article is a great resource.
  3. This Year’s Most Thought-Provoking Brain Discoveries
    This article highlights the standout brain discoveries from the Society for Neuroscience meeting in 2022, providing insights into the latest breakthroughs in neural circuits. It’s an interesting read for anyone looking to learn more about the latest developments in the field of neuroscience.

Want more? Dive deeper into one of them with the What’s AI weekly!

The Learn AI Together Community section!

Meme of the week!

Meme shared by friedliver#0614

Featured Community post from the Discord

altryne#7376 submitted a fun project for the assemblyAI hackathon.ChatGP-T1000 is an AI shape-shifter bot, that assumes identities understands your natural language, and replies with chatGPT responses, in character with deepfaked audio and lipsync. Check it out here and support a fellow community member! You can leave your feedback in the thread here.

AI poll of the week!

Join the discussion on Discord.

TAI Curated section

Article of the week

Grid Search in Python A-Z: Searching for Perfection by Gencay I.

Among many performance-boosting techniques in Machine Learning, the author explains Grid Search & Random Search. In grid search, the algorithm searches the best set of hyperparameters for a machine-learning model over a predefined grid of hyperparameter values. In contrast, random search involves selecting random combinations of hyperparameters and evaluating the model’s performance for each combination.

Our must-read articles

Support Vector Machines by Data Science meets Cyber Security

ChatGPT: How Does It Work Internally? by Patrick Meyer

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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Machine Learning Engineer @ Covariant (Berkeley, CA)

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

This AI newsletter is all you need #26 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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Towards AI Team
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(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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} } } //arrays which will contain all the achor tags found with the class (ds-link, ml-link, ailink) in each article inserted using search and replace let dslinks; let mllinks; let ailinks; let nllinks; let deslinks; let tdlinks; let iaslinks; let llinks; let pbplinks; let mlclinks; const content = document.querySelectorAll('article'); //all articles content.forEach((c) => { //to skip the articles with specific ids if (!articleIdsToSkip.includes(c.id)) { //getting all the anchor tags in each article one by one dslinks = document.querySelectorAll(`#${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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