Machine learning (ML) is a key area of artificial intelligence (AI) that helps computers learn from data and get better at tasks over time, without needing to be directly programmed. By recognizing patterns in data, ML algorithms can make predictions and decisions that are useful in many fields, from healthcare to finance and e-commerce. Whether it’s improving customer service or helping businesses make smarter decisions, machine learning is changing the way we interact with technology. Keep up with the latest in machine learning by following our blog for updates and insights.
In the ever-evolving world of artificial intelligence, Large Language Models (LLMs) have become the backbone of innovation. From chatbots to content generation tools, these models power some of the most sophisticated applications in use today. However, with great power comes great vulnerability — especially when it comes to model imitation via knowledge distillation (KD) . […]
Introduction: The Future of LLM Optimization Starts Here Artificial Intelligence (AI) has transformed how we interact with technology, especially through Large Language Models (LLMs) . These powerful systems have redefined natural language processing (NLP), enabling machines to understand and generate human-like text. However, as impressive as these models are, they come with significant challenges—high computational
Imagine a critical factory floor. Robots communicate flawlessly… until 10 new sensors come online. Suddenly, commands clash, data vanishes, and production grinds to a halt. This isn’t science fiction; it’s the harsh reality of today’s wireless networks buckling under change. Traditional network protocols are rigid, crumbling when environments shift – like adding users, changing traffic,
In an era where real-time communication and instant data processing are becoming the norm, streaming automatic speech recognition (ASR) has emerged as a cornerstone technology across industries—from customer service chatbots to live captioning in video conferencing platforms. However, despite significant advancements, streaming ASR still faces two major challenges: accuracy degradation due to small chunk sizes
The $100 Billion Problem: AI’s Annotation Nightmare Training AI models is expensive, slow, and painfully data-hungry. In specialized fields like healthcare or satellite imaging, labeling a single image can cost $50–$500. For a 1,000-class dataset like ImageNet? Millions. But what if you could: Meet ActiveKD and PCoreSet—a breakthrough framework from KAIST and VUNO Inc. that’s turning active learning (AL) and knowledge
The Hidden Crisis in Robotics and Autonomous Vehicles (Keywords: RGBD semantic segmentation, sensor failure, cross-modal learning) Imagine an autonomous vehicle navigating a fog-covered highway. Its depth sensor fails without warning. Instantly, its perception system degrades, risking lives. This nightmare scenario isn’t science fiction—it’s a daily reality for engineers grappling with multi-modal sensor fragility. Traditional RGBD (RGB
The Hidden Flaw Crippling Your LLM’s Reasoning Power Large language models (LLMs) promise revolutionary reasoning capabilities, yet most hit an invisible wall. Traditional training forces a brutal trade-off: Enter KDRL—a Huawei/HIT-developed framework merging KD and RL into a single unified pipeline. Results from 6 reasoning benchmarks reveal: How KDRL Shatters the KD-RL Deadlock Proposed model breakthrough lies
Unlock 57.2% Reasoning Accuracy: KDRL Revolutionary Fusion Crushes LLM Training Limits Read More »
The quest for the perfect delivery route, efficient garbage collection circuit, or life-saving emergency response path has plagued businesses and cities for decades. Traditional Vehicle Routing Problem (VRP) solvers often buckle under real-world complexity and scale, demanding expert tuning and struggling with massive datasets. But a seismic shift is occurring. Groundbreaking AI research titled “MTL-KD: Multi-Task
The Painful Reality of Shrinking Giant LLMs Large language models (LLMs) like GPT-4o and Claude 3.5 revolutionized AI—but their massive size makes deployment a nightmare. Imagine slashing compute costs by 90% while retaining 97% of performance. That’s the promise of Knowledge Distillation (KD), where a compact “student” model learns from a “teacher” LLM. Yet traditional KD
Connectionist Temporal Classification (CTC) powers countless speech recognition systems. But here’s the dirty secret: its “context-independent” assumption is a myth. Modern encoders do learn context-dependent patterns, and ignoring this wastes potential. This paper reveals how to harness this hidden power, slashing word error rates (WER) by over 13% in cross-domain tasks. If your ASR system uses CTC, this
