​ Home Table of Contents CycleGAN: Unpaired Image-to-Image Translation (Part 2) Apples2Oranges Dataset Configuring Your Development Environment Need Help Configuring Your Development Environment? Project Structure Creating Our Configuration File Preprocessing Our Dataset Implementing the CycleGAN Architecture Summary Citation Information CycleGAN: Unpaired Image-to-Image Translation (Part 2)… Read More »CycleGAN: Unpaired Image-to-Image Translation (Part 2) Shivam Chandhok PyImageSearch

​ In recent times, Large Language Models have successfully been able to capture everyone’s attention with their advanced capabilities. LLMs with some outstanding language production and understanding capabilities, such as OpenAI’s GPT-3.5, the latest multimodal GPT 4, etc., are being significantly utilized by industries. Generating… Read More »Using Multi-Agent Debate for Improving Reasoning and Factual Accuracy of Large Language Models (LLMs) Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​ Literature-based hypothesis generation is the central tenet of literature-based discovery (LBD). With drug discovery as its core application field, link-based hypothesis testing (LBD) focuses on hypothesizing ties between ideas that have not been examined together before (such as new drug-disease links). Even though these… Read More »Can Language Models Generate New Scientific Ideas? Meet Contextualized Literature-Based Discovery (C-LBD) Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

​ In his first live keynote since the pandemic, NVIDIA founder and CEO Jensen Huang today kicked off the COMPUTEX conference in Taipei, announcing platforms that companies can use to ride a historic wave of generative AI that’s transforming industries from advertising to manufacturing to… Read More »Live From Taipei: NVIDIA CEO Unveils Gen AI Platforms for Every Industry Rick Merritt – Archives Page 1 | NVIDIA Blog

​ Falcon-40B Falcon-40B is a powerful decoder-only model developed by TII (Technology Innovation Institute) and trained on a vast amount of data consisting of 1,000B tokens from RefinedWeb and curated corpora. This model is available under the TII Falcon LLM License. The Falcon-40B model is… Read More »Technology Innovation Institute Open-Sourced Falcon LLMs: A New AI Model That Uses Only 75 Percent of GPT-3’s Training Compute, 40 Percent of Chinchilla’s, and 80 Percent of PaLM-62B’s Niharika Singh Artificial Intelligence Category – MarkTechPost

​ Generative AI is rapidly ushering in a new era of computing for productivity, content creation, gaming and more. Generative AI models and applications — like NVIDIA NeMo and DLSS 3 Frame Generation, Meta LLaMa, ChatGPT, Adobe Firefly and Stable Diffusion — use neural networks… Read More »NVIDIA Brings New Generative AI Capabilities, Groundbreaking Performance to 100 Million Windows RTX PCs and Workstations Jason Paul – Archives Page 1 | NVIDIA Blog

​ Valuable and often concealed information about one’s immediate surroundings can be gleaned from an object’s reflection. By repurposing them as cameras, one can do previously inconceivable image feats, such as looking through walls or up into the sky. This is challenging because several factors… Read More »MIT Researchers Present A new computer vision system turns any shiny object into a camera of sorts: Enabling an observer to see around corners or beyond obstructions Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost

​ Large language models (LLMs) may be improved via finetuning, which also allows for adding or removing desired behaviors. However, finetuning big models is prohibitively costly; for example, a LLaMA 65B parameter model consumes more than 780 GB of GPU RAM when finetuning it in… Read More »Meet QLORA: An Efficient Finetuning Approach That Reduces Memory Usage Enough To Finetune A 65B Parameter Model On A Single 48GB GPU While Preserving Full 16-Bit FineTuning Task Performance Aneesh Tickoo Artificial Intelligence Category – MarkTechPost

​ SPRING is an LLM-based policy that outperforms Reinforcement Learning algorithms in an interactive environment requiring multi-task planning and reasoning.  A group of researchers from Carnegie Mellon University, NVIDIA, Ariel University, and Microsoft have investigated the use of Large Language Models (LLMs) for understanding and… Read More »LLMs Outperform Reinforcement Learning- Meet SPRING: An Innovative Prompting Framework for LLMs Designed to Enable in-Context Chain-of-Thought Planning and Reasoning Arham Islam Artificial Intelligence Category – MarkTechPost