Artificial Intelligence (AI)
A Notable Advance in Human-Driven AI Video
Note: The project page for this work includes 33 autoplaying high-res videos totaling half a gigabyte, which destabilized my system on load. For this reason, I won’t link to it directly. Readers can find the URL in the paper’s abstract or PDF if they choose. One of the primary objectives in current video synthesis research…
Taking a responsible path to AGI
We’re exploring the frontiers of AGI, prioritizing readiness, proactive risk assessment, and collaboration with the wider AI community. Artificial general intelligence (AGI), AI that’s at least as capable as humans at most cognitive tasks, could be here within the coming years. Integrated with agentic capabilities, AGI could supercharge AI to understand, reason, plan, and execute…
Researchers teach LLMs to solve complex planning challenges
Imagine a coffee company trying to optimize its supply chain. The company sources beans from three suppliers, roasts them at two facilities into either dark or light coffee, and then ships the roasted coffee to three retail locations. The suppliers have different fixed capacity, and roasting costs and shipping costs vary from place to place….
A Comprehensive Guide to LLM Routing: Tools and Frameworks
Deploying LLMs presents challenges, particularly in optimizing efficiency, managing computational costs, and ensuring high-quality performance. LLM routing has emerged as a strategic solution to these challenges, enabling intelligent task allocation to the most suitable models or tools. Let’s delve into the intricacies of LLM routing, explore various tools and frameworks designed for its implementation, and…
Teaching AI to Give Better Video Critiques
While Large Vision-Language Models (LVLMs) can be useful aides in interpreting some of the more arcane or challenging submissions in computer vision literature, there’s one area where they are hamstrung: determining the merits and subjective quality of any video examples that accompany new papers*. This is a critical aspect of a submission, since scientific papers…
A catalogue of genetic mutations to help pinpoint the cause of diseases
Research Published 19 September 2023 Authors Žiga Avsec and Jun Cheng New AI tool classifies the effects of 71 million ‘missense’ mutations Uncovering the root causes of disease is one of the greatest challenges in human genetics. With millions of possible mutations and limited experimental data, it’s largely still a mystery which ones could give…
New training approach could help AI agents perform better in uncertain conditions
A home robot trained to perform household tasks in a factory may fail to effectively scrub the sink or take out the trash when deployed in a user’s kitchen, since this new environment differs from its training space. To avoid this, engineers often try to match the simulated training environment as closely as possible with…
NVIDIA AI Researchers Introduce FFN Fusion: A Novel Optimization Technique that Demonstrates How Sequential Computation in Large Language Models LLMs can be Effectively Parallelized
Large language models (LLMs) have become vital across domains, enabling high-performance applications such as natural language generation, scientific research, and conversational agents. Underneath these advancements lies the transformer architecture, where alternating layers of attention mechanisms and feed-forward networks (FFNs) sequentially process tokenized input. However, with an increase in size and complexity, the computational burden required…
Why Time is QA Tester’s Superpower and Pressure its Kryptonite! – Spritle software
Introduction In the breakneck world of software development, where new apps and features are demanded yesterday, Quality Assurance (QA) teams often find themselves under immense pressure. They’re expected to give the thumbs-up on complex software, often against a relentlessly ticking clock. But here’s the truth: squeezing QA like a lemon doesn’t magically produce better software—it…
How Well Can LLMs Actually Reason Through Messy Problems?
The introduction and evolution of generative AI have been so sudden and intense that it’s actually quite difficult to fully appreciate just how much this technology has changed our lives. Zoom out to just three years ago. Yes, AI was becoming more pervasive, at least in theory. More people knew some of the things it…