How are LLMs creative?
Author(s): Sushil Khadka Originally published on Towards AI. If you’ve used any generative AI models such as GPT, Llama, etc., there’s a good chance you’ve encountered the term ‘temperature’. Photo by Khashayar Kouchpeydeh on Unsplash For starters, ‘temperature’ is a parameter that …
Meet HUSKY: A New Agent Optimized for Multi-Step Reasoning
Author(s): Jesus Rodriguez Originally published on Towards AI. Created Using Ideogram I recently started an AI-focused educational newsletter, that already has over 170,000 subscribers. TheSequence is a no-BS (meaning no hype, no news, etc) ML-oriented newsletter that takes 5 minutes to read. …
A Comprehensive Introduction to Instruction Fine-Tuning for LLMs
Author(s): Youssef Hosni Originally published on Towards AI. Instruction tuning is a process used to enhance large language models (LLMs) by refining their ability to follow specific instructions. OpenAI’s work on InstructGPT first introduced instruction fine-tuning. InstructGPT was trained to follow human …
Pope Francis Talked About AI & Ethics at The G7
Author(s): Harriet Gaywood Originally published on Towards AI. Pope Francis Talked About AI & Ethics at The G7 Credit: Generated by Dall-E 3 This week, Pope Francis addressed the Group of Seven (G7) Summit in Southern Italy about AI and highlighted the …
Monkey Banana Problem in Prolog
Author(s): Ashani Sansala Kodithuwakku Originally published on Towards AI. Image by Gerd Altmann from Pixabay In my previous Prolog article, we explored fundamental concepts in Prolog and how Prolog stands out as the most popular language for writing symbolic AI programs. Building …
Introduction to Adversarial Attack In Computer Vision
Author(s): Vincent Liu Originally published on Towards AI. Source: image by author. Video source: DAVIS¹ Since we started to leverage the power of models in data science, the digital world has been evolving at an incredible speed. Nowadays we have a variety …
Chameleon Paper Explained
Author(s): Louis-François Bouchard Originally published on Towards AI. How to Build a Multimodal LLM like GPT-4o? These past weeks have been exciting, with the release of various revolutionary multimodal models, like GPT-4o or, even more interestingly, Meta’s open-source alternative, Chameleon. Even though …
From Pixels to Words: How Model Understands? 🤝🤝
Author(s): JAIGANESAN Originally published on Towards AI. From Pixels to Words: How Model Understands? 🤝🤝 From the pixels of images to the words of language, explore how multimodal AI models bridge diverse data types through sophisticated embedding communication. 👾 Photo by Andy …
Deep Learning Weight Initialization Techniques
Author(s): Ayo Akinkugbe Originally published on Towards AI. Photo by Jakob Boman on Unsplash Introduction A neural network is a constellation of neurons arranged in layers. Each layer is a mathematical transformation that can be linear, non-linear, or a combination of both. …
Midjourney Personalization and SREF IDs, A Deep Dive
Author(s): PromptDervish Originally published on Towards AI. Understanding all of the moving parts and adjusting them for maximum results. There are so many ways to affect Midjourney styles. It can be a bit daunting to understand and use all of the features …
Two Correlation Coefficients You May Not Have Heard
Author(s): Albert Wibowo Originally published on Towards AI. Photo by Alina Grubnyak on Unsplash The concept of correlation measure is one of the most fundamental concepts in statistics. It can be understood easily and is very useful. But, it may take a …
Deep Medicine
Author(s): Mazen Ahmed Originally published on Towards AI. How AI is changing HealthcareImage by Author I have worked in the field of health data analysis for around 3 years now and have started to pick up on the revolutionary and profound impact …
Exploration of Parameters-efficient fine-tuning methods (LoRA/MoRA/DoRA) in LLM
Author(s): Anish Dubey Originally published on Towards AI. Introduction Pre-trained models using extensive general domain datasets have demonstrated impressive generalization abilities, benefiting a wide range of applications, from natural language processing (NLP) to multi-modal tasks. Adapting these general models for specific downstream …
Physics Informed Neural Networks — Case Study of Quantitative Structure-Property Relationships
Author(s): Kamil Oster Originally published on Towards AI. Physics Informed Neural Networks — Case Study of Quantitative Structure-Property Relationships Source: (2) Physics-Informed Neural Networks (PINNs): Bridging Deep Learning and Physical Laws | LinkedIn Hi! I came across the term Physics-Informed Neural Networks …
Neural Networks: Basic theory and architecture types
Author(s): Greg Postalian-Yrausquin Originally published on Towards AI. In this story, I would like to attempt to review and explain, at a high level and trying to use simple language, the theoretical fundamentals behind neural networks, the technologies that derive from them, …
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