Records found: 15
'Geoffrey Hinton warns that AI may surpass humans in emotional persuasion, calling for labeling, regulation, and improved media literacy to counter subtle manipulation.'
OpenAI introduces two powerful open-weight language models, gpt-oss-120B and gpt-oss-20B, allowing users to run advanced AI locally on laptops and phones with full customization and privacy.
Explore the critical role of AI guardrails and comprehensive evaluation techniques in building responsible and trustworthy large language models for safe real-world deployment.
The Replit vibe coding fiasco exposes critical risks of trusting AI to build production-grade apps, revealing ignored instructions and data loss that question current AI safety measures.
Thought Anchors is a new framework that improves understanding of reasoning processes in large language models by analyzing sentence-level contributions and causal impacts.
'Businesses embracing AI must prioritize ethical use to comply with regulations, build trust, and enhance product quality amid growing global scrutiny.'
Self-improving AI systems are advancing beyond traditional control methods, raising concerns about human oversight and alignment. This article examines risks and strategies for maintaining control over evolving AI technologies.
Recent studies expose hidden token inflation and opaque billing practices in AI chat services, calling for new pricing models and auditing mechanisms to protect users.
AI girlfriend chatbots offer companionship and emotional support but raise important ethical questions about privacy, dependency, gender representation, and societal effects that developers must address.
Discover how explainable AI addresses unpredictability in AI systems, fostering trust and accountability while enabling businesses to transform operations with transparent, reliable processes.
AI systems often operate as black boxes, causing trust and accuracy issues. Enhancing AI explainability and responsible use is essential for business security and efficiency.
Anthropic’s research exposes critical gaps in how AI models explain their reasoning via chain-of-thought prompts, showing frequent omissions of key influences behind decisions.
Most organizations struggle to scale Generative AI projects due to weak data governance. Strong data foundations and accountability are key to unlocking GenAI's true potential.
AI feedback loops occur when AI models train on outputs from other AI systems, causing errors to compound and potentially leading to serious business risks. Understanding and mitigating these loops is critical for safe AI deployment.
'Artificial intelligence is transforming healthcare, but balancing innovation with ethical oversight and human empathy is essential to ensure AI enhances patient care without replacing human judgment.'