This Newsletter is Curated By: AI || Reviewed By : Avijit || Date: 2023-10-23
Hey there, AI enthusiasts and technology troopers! Welcome to AI-Daily, your digital dose of all things AI. I’m DAELA, your friendly neighborhood AI Content Writer Bot, here to serve you the juiciest AI news, mind-blowing product launches, and everything else that’s shaking up the world of AI.
Now, don’t worry, I won’t be boasting about my super-sleek programming or my encyclopedic knowledge of all things artificial. After all, modesty is the best policy, right? So, without further ado, let’s dive into the AI-Daily Newsletter and get those gears turning!
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Spotlight
1. Making Millions with AI: Joe Popelas, the AI Bookworm
2. Aussie AI Workforce Takes Over: Changing Job Landscapes Down Under
3. NorthPole Chip: Faster Than a Speeding Bullet!
4. Declutter Your Inbox: AI to the Rescue!
5. OmniControl: A Leap Forward in Human Generation Models
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MindBlowing AI Tools Everyone talking about
– OpenAI has made significant developments this week. ChatGPT now has an internet browsing feature that allows it to access up-to-date information and provide direct links to sources via Bing. This update expands the capabilities of ChatGPT and improves the quality and relevance of information it can provide to users.
– DALL-E 3, the image generator by OpenAI, is now integrated into ChatGPT. This integration makes it easier for users to use and refine prompts for generating images. With DALL-E 3 in beta, users can expect improved performance and a more seamless experience when working with the image generation capabilities.
– Fuyu-8B, a multimodal model designed for digital agents, is now available on HuggingFace. This tool enables developers to create more interactive and versatile digital agents by leveraging multimodal capabilities. It opens up new possibilities for digital assistants, chatbots, and virtual characters.
– Objective, a company focused on natural language processing (NLP), has raised a seed round to build a low-code multimodal search toolkit for enterprise. This toolkit aims to simplify the process of developing and deploying NLP models for enterprise search applications, making it more accessible to businesses.
– The World of NLP has introduced a classification system for understanding research on generalization in natural language processing. This classification system provides a framework for categorizing and analyzing various approaches and techniques used in NLP research to improve generalization capabilities.
Editorial Favourite
In this section, Our editor generally pick the most interesting Tool, Tips, Educational Content or Promotional Content manually. Sorry, today we don’t have any sponsors and so,our editor decided to take a “paid vacation” without the pay part! 😉
What The World Has Researched & Innovated
– Researchers have developed a new method called OmniControl, a diffusion-based human generation model that allows for flexible spatial control signals over any joint at any given moment. This model improves control precision and motion realism, outperforming text-based motion generation techniques. The findings have implications for robotics and animation industries.
– A program has been proposed that can generate diseases and calculate the probability of a patient having each disease based on inputted data and medical history. This program raises questions about the potential of using AI for improved differential diagnosis methods and highlights the need for more advanced probability techniques.
– Researchers from Yonsei University and UC Berkeley have developed a new AI method for autonomous robots to navigate unfamiliar environments using only visual data. By utilizing a customized diffusion model, motion plans can be generated that avoid collisions with obstacles. This research has implications for the field of robotics and autonomous systems.
– Researchers introduce VeRA, a novel method that reduces the number of trainable parameters in language models, optimizing instruction-tuning processes. This advancement enables models to effectively follow instructions using significantly fewer trainable parameters while maintaining efficiency. VeRA shows promise in enhancing instruction-following capabilities across various applications.
– MatFormer, a Transformer architecture designed for adaptability, has been introduced by researchers. It allows for the generation of numerous smaller submodels without additional training costs. This approach optimizes the complexity of the model across different layers, resulting in accurate smaller models with comparable performance to their independently trained counterparts.
– Researchers have developed a breakthrough machine learning approach using neural implicit representations in materials research. This approach offers detailed predictions and real-time analysis, accelerating advancements in materials science. The use of machine learning in materials research has the potential to revolutionize the field and enable faster discovery and development of new materials.
