AGI

China Mobile Shanghai showcased its 5G-Advanced network monetization strategy, powered by Huawei’s GainLeap and AI, during a Shanghai Shenhua match. The “5G-A Exclusive Package for Shenhua Football Fans” offers enhanced speeds, exclusive content, and fan perks, moving beyond basic connectivity. AI-powered infrastructure dynamically allocates resources, ensuring premium subscribers enjoy improved performance (e.g., reduced QR code latency, faster uploads). This initiative explores whether experience-driven, community-targeted packages can unlock sustainable revenue growth for telecom operators.

An MIT study investigated the cognitive effects of LLM use compared to search engines and independent thought. EEG data revealed reduced brain activity and lower ownership of work among LLM users, suggesting decreased cognitive engagement. Participants who initially used LLMs exhibited weaker neural connectivity and cognitive processing even after switching to independent work, while those who used LLMs after independent work showed cognitive benefits. This highlights the potential for LLMs to hinder cognitive development if used as a substitute for critical thinking, urging cautious AI integration.

Applications are critical in modern organizations but are prime targets for cyberattacks. AI-driven AppSec tools are emerging to address increasing software complexity and security risks, offering automation and predictive capabilities. Best practices include shifting security left, combining AI with traditional methods, enabling continuous learning, keeping humans in the loop, and aligning with compliance. Leading tools like Apiiro, Mend.io, Burp Suite, PentestGPT, and Garak offer features like intelligent vulnerability detection, automated remediation guidance, continuous monitoring, risk prioritization, and DevOps integration, crucial for building resilient software.

Google’s Kent Walker urged the EU to adopt a more strategic regulatory approach to AI to effectively compete globally, especially with China. He cited China’s high AI adoption rates compared to the EU’s lower rates, attributing this to significant government investment and less burdensome regulations. Walker proposed a three-pronged strategy: smart policy focused on real-world AI effects, workforce development for AI skills, and scaling up beyond basic applications to embrace scientific breakthroughs. He emphasized removing regulatory hurdles, fostering research, and broadly implementing AI to stimulate EU growth.

A BCG study reveals a widening AI adoption gap: only 5% of companies significantly benefit financially, while 60% see marginal gains. These leaders, termed “future-built,” experience higher revenue growth and EBIT margins. They reinvest AI gains, prioritizing core business function reinvention and agentic AI adoption. Success hinges on executive-led strategy, business-IT collaboration, and workforce upskilling. Laggards face a “vicious cycle” due to leadership gaps and lack of focus, emphasizing the need for organizational change to avoid falling behind.

A visit to Huawei’s Shanghai Acoustics R&D Centre revealed their ambitious push into automotive sound engineering. Huawei combines objective measurements with psychoacoustic principles, aiming for audiophile-grade sound in vehicles. The HUAWEI SOUND ULTIMATE Series features innovations like seat-specific 4D surround sound, tangential force woofers, and independent sound zones. Huawei’s significant R&D investment challenges established players, but scalability, cost-effectiveness, and consumer adoption will determine their success in the automotive market.

At Huawei Connect 2025, Huawei detailed its plan to open-source its AI software stack by year-end, including CANN, the Mind series, and OpenPangu models. This move aims to address developer challenges and foster collaboration. CANN will offer open interfaces for its compiler, while the Mind series development environment commits to full open-source. Huawei also plans to open-source its UB OS Component for flexible OS integration and prioritize compatibility with PyTorch and vLLM. The success hinges on initial release quality, community support, and clear governance.

Researchers have developed CAMIA, a novel Context-Aware Membership Inference Attack, that exposes privacy vulnerabilities in AI models by detecting data memorization during training. CAMIA outperforms existing methods by monitoring the evolution of model uncertainty throughout text generation, identifying subtle indicators of memorization at the token level. Evaluations on Pythia and GPT-Neo models showed significant accuracy improvements with CAMIA compared to previous attacks. This research highlights the privacy risks of training AI models on large datasets and emphasizes the need for privacy-enhancing technologies. CAMIA’s efficiency makes it a practical tool for auditing AI models.

The cybersecurity industry faces a growing need for aggressive containment features but must balance rapid response with ethical considerations. Automatically quarantining critical systems can be detrimental, highlighting the importance of ethical cybersecurity practices. ManageEngine advocates for a “trust by design” philosophy, embedding fairness, transparency, and accountability into its products. The company’s “SHE AI principles”—Secure, Human, and Ethical AI—address the ethical implications of AI-driven security. Organizations should adopt cybersecurity ethics charters, embed ethics in technology decisions, and operationalize ethics through training and controls.

Samsung has introduced TRUEBench, a novel AI benchmark specifically designed to evaluate large language model (LLM) performance in real-world enterprise contexts. Addressing the limitations of traditional benchmarks, TRUEBench assesses AI across diverse business tasks, multilingual capabilities, and the ability to understand unstated user intents. It leverages a comprehensive suite of metrics across 10 categories and 46 sub-categories, based on Samsung’s internal AI deployments. Through its open-source platform on Hugging Face, Samsung aims to establish TRUEBench as an industry standard for AI productivity measurement.