Back when most business applications were monolithic, ensuring their resilience was by no means easy. But given the way apps run in 2025 and what’s expected of them, maintaining monolithic apps was arguably simpler. Back then, IT staff had a finite set of criteria on which to improve an application’s resilience, and the rate of change…
Mistral AI has pulled back the curtain on Magistral, their first model specifically built for reasoning tasks. Magistral arrives in two flavours: a 24B parameter open-source version called Magistral Small that anyone can tinker with, and a beefier enterprise edition, Magistral Medium, aimed at commercial applications where advanced reasoning capabilities matter most. “The best human…
Rethinking Audio-Based Human-Computer Interaction Machines that can respond to human speech with equally expressive and natural audio have become a major goal in intelligent interaction systems. Audio-language modeling extends this vision by combining speech recognition, natural language understanding, and audio generation. Rather than relying on text conversions, models in this space aim to understand and…
Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a missed turn. But for an autonomous vehicle or a delivery robot,…
The Inefficiency of Static Chain-of-Thought Reasoning in LRMs Recent LRMs achieve top performance by using detailed CoT reasoning to solve complex tasks. However, many simple tasks they handle could be solved by smaller models with fewer tokens, making such elaborate reasoning unnecessary. This echoes human thinking, where we use fast, intuitive responses for easy problems…
Post-training methods for pre-trained language models (LMs) depend on human supervision through demonstrations or preference feedback to specify desired behaviors. However, this approach faces critical limitations as tasks and model behaviors become very complex. Human supervision is unreliable in these scenarios as LMs learn to mimic mistakes in demonstrations or exploit inherent flaws in feedback…
Key Takeaways: Researchers from Google DeepMind, the University of Michigan & Brown university have developed “Motion Prompting,” a new method for controlling video generation using specific motion trajectories. The technique uses “motion prompts,” a flexible representation of movement that can be either sparse or dense, to guide a pre-trained video diffusion model. A key innovation…
LLMs are increasingly seen as key to achieving Artificial General Intelligence (AGI), but they face major limitations in how they handle memory. Most LLMs rely on fixed knowledge stored in their weights and short-lived context during use, making it hard to retain or update information over time. Techniques like RAG attempt to incorporate external knowledge…
Transformer models have significantly influenced how AI systems approach tasks in natural language understanding, translation, and reasoning. These large-scale models, particularly large language models (LLMs), have grown in size and complexity to the point where they encompass broad capabilities across various domains. However, applying these models to new, specialized tasks remains a complex operation. Each…