Introduction: Why “Objectives” Matter More Than You Think Every organization implementing AI faces the same challenge: how to balance innovation with responsibility. ISO/IEC 42001 provides the framework, but most people miss its core insight. The entire standard revolves around a deceptively simple concept: objectives. Not compliance checkboxes or technical specifications, but the fundamental question of […]
Alibaba’s Qwen3-Max: Production-Ready Thinking Mode, 1T+ Parameters, and Day-One Coding/Agentic Bench SignalsMarkTechPoston September 24, 2025 at 3:20 pm Alibaba has released Qwen3-Max, a trillion-parameter Mixture-of-Experts (MoE) model positioned as its most capable foundation model to date, with an immediate public on-ramp via Qwen Chat and Alibaba Cloud’s Model Studio API. The launch moves Qwen’s 2025 cadence from preview to production and centers on two variants: Qwen3-Max-Instruct for standard reasoning/coding tasks and Qwen3-Max-Thinking for
The post Alibaba’s Qwen3-Max: Production-Ready Thinking Mode, 1T+ Parameters, and Day-One Coding/Agentic Bench Signals appeared first on MarkTechPost.
Alibaba has released Qwen3-Max, a trillion-parameter Mixture-of-Experts (MoE) model positioned as its most capable foundation model to date, with an immediate public on-ramp via Qwen Chat and Alibaba Cloud’s Model Studio API. The launch moves Qwen’s 2025 cadence from preview to production and centers on two variants: Qwen3-Max-Instruct for standard reasoning/coding tasks and Qwen3-Max-Thinking for
The post Alibaba’s Qwen3-Max: Production-Ready Thinking Mode, 1T+ Parameters, and Day-One Coding/Agentic Bench Signals appeared first on MarkTechPost. Read More
Agentic Vibe Coding Startup Emergent Secures $23 million in Series A fundingAnalytics India Magazineon September 24, 2025 at 4:29 pm The platform experienced swift adoption, with Emergent exceeding $15 million in annual recurring revenue (ARR) within just 90 days.
The post Agentic Vibe Coding Startup Emergent Secures $23 million in Series A funding appeared first on Analytics India Magazine.
The platform experienced swift adoption, with Emergent exceeding $15 million in annual recurring revenue (ARR) within just 90 days.
The post Agentic Vibe Coding Startup Emergent Secures $23 million in Series A funding appeared first on Analytics India Magazine. Read More
Why Do Language Models Hallucinate?KDnuggetson September 24, 2025 at 4:00 pm In this article, we look at five revelations from the paper “Why Do Language Models Hallucinate?”
In this article, we look at five revelations from the paper “Why Do Language Models Hallucinate?” Read More
Generative AI in retail: Adoption comes at high security costAI Newson September 24, 2025 at 4:16 pm The retail industry is among the leaders in generative AI adoption, but a new report highlights the security costs that accompany it. According to cybersecurity firm Netskope, the retail sector has all but universally adopted the technology, with 95% of organisations now using generative AI applications. That’s a huge jump from 73% just a year
The post Generative AI in retail: Adoption comes at high security cost appeared first on AI News.
The retail industry is among the leaders in generative AI adoption, but a new report highlights the security costs that accompany it. According to cybersecurity firm Netskope, the retail sector has all but universally adopted the technology, with 95% of organisations now using generative AI applications. That’s a huge jump from 73% just a year
The post Generative AI in retail: Adoption comes at high security cost appeared first on AI News. Read More
The Download: accidental AI relationships, and the future of contraceptionMIT Technology Reviewon September 24, 2025 at 12:10 pm This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. It’s surprisingly easy to stumble into a relationship with an AI chatbot The news: The first large-scale computational analysis of the Reddit community r/MyBoyfriendIsAI, which is dedicated to discussing AI relationships, found that…
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. It’s surprisingly easy to stumble into a relationship with an AI chatbot The news: The first large-scale computational analysis of the Reddit community r/MyBoyfriendIsAI, which is dedicated to discussing AI relationships, found that… Read More
AI News September 09 24 2025 | 24-Hour Intelligence Update Executive Summary Three major developments reshaped the AI landscape yesterday. OpenAI’s massive infrastructure expansion gained momentum with five new data center sites worth $400 billion. Microsoft made a strategic pivot by integrating Anthropic’s Claude models into Copilot, diversifying beyond OpenAI for the first time in […]
Martin Frederik, Snowflake: Data quality is key to AI-driven growthAI Newson September 23, 2025 at 4:34 pm As companies race to implement AI, many are finding that project success hinges directly on the quality of their data. This dependency is causing many ambitious initiatives to stall, never making it beyond the experimental proof-of-concept stage. So, what’s the secret to turning these experiments into real revenue generators? AI News caught up with Martin
The post Martin Frederik, Snowflake: Data quality is key to AI-driven growth appeared first on AI News.
As companies race to implement AI, many are finding that project success hinges directly on the quality of their data. This dependency is causing many ambitious initiatives to stall, never making it beyond the experimental proof-of-concept stage. So, what’s the secret to turning these experiments into real revenue generators? AI News caught up with Martin
The post Martin Frederik, Snowflake: Data quality is key to AI-driven growth appeared first on AI News. Read More
Generative AI Myths, Busted: An Engineers’s Quick GuideTowards Data Scienceon September 23, 2025 at 6:08 pm A super simple and quick guide to how generative AI works, the myths around it, and why it won’t replace engineers anytime soon.
The post Generative AI Myths, Busted: An Engineers’s Quick Guide appeared first on Towards Data Science.
A super simple and quick guide to how generative AI works, the myths around it, and why it won’t replace engineers anytime soon.
The post Generative AI Myths, Busted: An Engineers’s Quick Guide appeared first on Towards Data Science. Read More
Accurate and Efficient Low-Rank Model Merging in Core Spacecs.AI updates on arXiv.orgon September 23, 2025 at 4:00 am arXiv:2509.17786v1 Announce Type: cross
Abstract: In this paper, we address the challenges associated with merging low-rank adaptations of large neural networks. With the rise of parameter-efficient adaptation techniques, such as Low-Rank Adaptation (LoRA), model fine-tuning has become more accessible. While fine-tuning models with LoRA is highly efficient, existing merging methods often sacrifice this efficiency by merging fully-sized weight matrices. We propose the Core Space merging framework, which enables the merging of LoRA-adapted models within a common alignment basis, thereby preserving the efficiency of low-rank adaptation while substantially improving accuracy across tasks. We further provide a formal proof that projection into Core Space ensures no loss of information and provide a complexity analysis showing the efficiency gains. Extensive empirical results demonstrate that Core Space significantly improves existing merging techniques and achieves state-of-the-art results on both vision and language tasks while utilizing a fraction of the computational resources. Codebase is available at https://github.com/apanariello4/core-space-merging.
arXiv:2509.17786v1 Announce Type: cross
Abstract: In this paper, we address the challenges associated with merging low-rank adaptations of large neural networks. With the rise of parameter-efficient adaptation techniques, such as Low-Rank Adaptation (LoRA), model fine-tuning has become more accessible. While fine-tuning models with LoRA is highly efficient, existing merging methods often sacrifice this efficiency by merging fully-sized weight matrices. We propose the Core Space merging framework, which enables the merging of LoRA-adapted models within a common alignment basis, thereby preserving the efficiency of low-rank adaptation while substantially improving accuracy across tasks. We further provide a formal proof that projection into Core Space ensures no loss of information and provide a complexity analysis showing the efficiency gains. Extensive empirical results demonstrate that Core Space significantly improves existing merging techniques and achieves state-of-the-art results on both vision and language tasks while utilizing a fraction of the computational resources. Codebase is available at https://github.com/apanariello4/core-space-merging. Read More