Author: Advanced AI Editor

In this paper, we survey recent advances in Reinforcement Learning (RL) for reasoning with Large Language Models (LLMs). RL has achieved remarkable success in advancing the frontier of LLM capabilities, particularly in addressing complex logical tasks such as mathematics and coding. As a result, RL has emerged as a foundational methodology for transforming LLMs into LRMs. With the rapid progress of the field, further scaling of RL for LRMs now faces foundational challenges not only in computational resources but also in algorithm design, training data, and infrastructure. To this end, it is timely to revisit the development of this domain,…

This is a guest post co-written with Skello. Skello is a leading human resources (HR) software as a service (SaaS) solution focusing on employee scheduling and workforce management. Catering to diverse sectors such as hospitality, retail, healthcare, construction, and industry, Skello offers features including schedule creation, time tracking, and payroll preparation. With approximately 20,000 customers and 400,000 daily users across Europe as of 2024, Skello continually innovates to meet its clients’ evolving needs. One such innovation is the implementation of an AI-powered assistant to enhance user experience and data accessibility. In this post, we explain how Skello used Amazon Bedrock…

The global AI in healthcare market is experiencing explosive growth. According to a Fortune Business Insights report, the market is projected to scale from $39.25 billion in 2025 to approximately $504.17 billion by 2032, at a CAGR of 44.0%. Such growth is expected to be driven by rising demand for AI-enabled diagnostics, imaging, drug discovery, clinical workflow automation, and remote patient monitoring — areas where traditional systems are increasingly inadequate. With technology giants aggressively moving into the space, select MedTech players are also emerging as prime beneficiaries. Two such names, Butterfly Network BFLY and Omnicell OMCL, stand out as well-positioned…

For humans, identifying items in a scene — whether that’s an avocado or an Aventador, a pile of mashed potatoes or an alien mothership — is as simple as looking at them. But for artificial intelligence and computer vision systems, developing a high-fidelity understanding of their surroundings takes a bit more effort. Well, a lot more effort. Around 800 hours of hand-labeling training images effort, if we’re being specific. To help machines better see the way people do, a team of researchers at MIT CSAIL in collaboration with Cornell University and Microsoft have developed STEGO, an algorithm able to identify…

SEOs (and their bosses) are rapidly adopting AI search optimization, even if they’re still figuring out what it all means, according to a new survey from Aleyda Solis. By the numbers: More than 200 senior SEOs worldwide shared how they’re referring to this emerging discipline. 36% say their clients/managers simply call it “AI search optimization.” 27% stick with SEO (but for AI platforms). 18% call it generative engine optimization (GEO). Others use terms like AEO (answer engine optimization) or even LLMO. Yes, and… Nearly 91% said leadership asked about AI search visibility in the past year. Yes, the topic is…

(Source: VideoFlow/Shutterstock) Artificial intelligence (AI) is being rapidly integrated into public sector operations. In 2024 alone, federal agencies reported more than 1,700 AI use cases, more than double the number from the prior year. With half of these concentrated in departments managing sensitive national missions such as healthcare, veteran services and homeland security, the need to secure AI systems in government are both urgent and complex. Success relies on an end-to-end approach to address risks, maintain compliance and build systems that are both explainable and resilient. Prioritizing Trust and Accountability One of the foundational challenges of securing AI in the…

Tesla is looking to make a big splash with Robotaxi in a new market, as the company was spotted testing validation vehicles in one region where it has not yet launched its ride-hailing service. After launching Robotaxi in Austin in late June, Tesla followed up with a relatively quick expansion to the Bay Area of California. Both service areas are operating with a geofence that is expansive: In Texas, it is 173 square miles, while in the Bay Area, it is roughly 400 square miles. Tesla has been transparent that it is prioritizing safety, but it believes it can expand…

Reward Models (RMs) are critical for improving generation models via Reinforcement Learning (RL), yet the RM scaling paradigm in visual generation remains largely unexplored. It primarily due to fundamental limitations in existing approaches: CLIP-based RMs suffer from architectural and input modality constraints, while prevalent Bradley-Terry losses are fundamentally misaligned with the next-token prediction mechanism of Vision-Language Models (VLMs), hindering effective scaling. More critically, the RLHF optimization process is plagued by Reward Hacking issue, where models exploit flaws in the reward signal without improving true quality. To address these challenges, we introduce RewardDance, a scalable reward modeling framework that overcomes these…

In real-world video and image analysis, businesses often face the challenge of detecting objects that weren’t part of a model’s original training set. This becomes especially difficult in dynamic environments where new, unknown, or user-defined objects frequently appear. For example, media publishers might want to track emerging brands or products in user-generated content; advertisers need to analyze product appearances in influencer videos despite visual variations; retail providers aim to support flexible, descriptive search; self-driving cars must identify unexpected road debris; and manufacturing systems need to catch novel or subtle defects without prior labeling.In all these cases, traditional closed-set object detection…

Tatiana Serebryakova/iStock/Getty Images Plus via Getty ImagesFollow ZDNET: Add us as a preferred source on Google.ZDNET’s key takeawaysStable Audio 2.5 is designed to help brands build a “sonic identity.”The model was trained on a fully licensed dataset.Custom tracks can be used in ads, retail locations, and elsewhere.Stability AI just made it easier for brands to create custom, AI-generated audio, thereby negating the need to spend time and money on elaborate recording and production processes.The UK-based company unveiled Stable Audio 2.5 on Wednesday, describing the new model on their website as “the first audio generation model designed specifically for enterprise-grade sound-production.” Also: 4 ways…