1496 stories tagged AI · page 9 of 63
Researchers proposed a system to make AI's scientific reasoning transparent. This could help scientists audit AI-generated ideas like they would their own notes. The tool creates a structured record of AI hypotheses and experiments, making it easier to follow the AI's thought process.
Researchers have developed a way to catch AI chatbots when they make up false information. This is especially important for systems that need to provide accurate details, like recommending restaurants or booking appointments.
Researchers have developed a neuro-agentic control framework that combines AI planning with traditional security systems. This could make industrial IoT environments safer by reducing the risk of costly downtime and physical damage from cyberattacks.
Researchers created MedRealMM, a real-world benchmark for medical AI that includes real patient-doctor conversations and medical images. This could lead to more accurate and reliable AI tools for online healthcare consultations.
Researchers created a new test to measure AI's ability to connect clues scattered across long documents. This could improve how AI understands complex real-world information.
Microsoft's CEO has issued a stark warning about the risks of frontier AI labs accessing corporate intellectual property. Companies are being urged to take proactive measures to safeguard their sensitive data.
In a surprising blog post, Microsoft CEO Satya Nadella warned enterprises about the dangers of relying on proprietary AI models from companies like Anthropic and OpenAI, urging businesses to maintain control over their AI systems to avoid vendor lock-in and unforeseen risks.
Researchers created LongMedBench, a new benchmark for AI medical assistants that simulates real-world patient care over time. Built from real EHR data (MIMIC-IV), it evaluates how well AI can handle ongoing treatments, repeated visits, and evolving health conditions.
Researchers have developed a new method called KV-PRM that makes multi-agent AI systems more efficient. This could lead to faster, more capable AI collaborations in complex scenarios. The technique reduces the computational cost of evaluating long interactions, making it practical for real-world applications.
Researchers introduced HALO, a method to enhance existing AI language models with minimal extra computation. It selectively refines responses, improving accuracy without wasting resources.
Researchers developed a new AI planning method called GATS that reduces computational costs and improves reliability by eliminating LLM calls during inference. It could make AI assistants more efficient at complex tasks.
Local governments and residents are increasingly opposing the construction of AI data centers due to their massive energy demands and environmental impact. These protests are growing as tech companies expand their AI infrastructure.
Researchers introduced CogniConsole, a tool that improves AI reliability by managing how tasks are framed and context is selected. This approach shifts focus from model capabilities to better control during interactions.
Researchers introduced ARCANA, a multi-agent AI framework that solves ARC-AGI-2 tasks by iteratively perceiving, hypothesizing, executing, and reflecting. It operates under strict test-time and hardware constraints, using specialized agents to build scene graphs, propose DSL programs, verify candidates, and synthesize failure-driven feedback.
Researchers developed a new AI framework where multiple AI agents debate legal cases, improving accuracy by up to 8%. This approach could make legal analysis faster and more reliable for professionals and the public.
The rapid expansion of AI infrastructure is creating new inflation risks. Companies are spending heavily on AI hardware and data centers, which could push prices higher. This is a shift from the recent trend of falling tech prices.
Researchers developed AgentKGV, an AI framework that improves the accuracy of knowledge graphs by verifying facts. This could make AI systems more reliable for everyday users. The system uses a two-stage training process to identify and correct errors in large-scale data sets.
A new analysis reveals that AI code review benchmarks often test the wrong things, leading to misleading results. This highlights why AI tools still struggle with real-world coding tasks.
Samsung is now requiring users to consent to their health data being used to train AI models to continue using Samsung Health. This raises privacy concerns for many users.
OpenAI has announced that its latest model, GPT 5.6, will be the preferred AI for Microsoft Copilot 365. This move comes amid rumors of a potential breakup between the two tech giants. Despite the speculation, both companies are committed to their current partnership.
OpenAI is focusing on families by hiring a product manager to create experiences for caregivers and older adults. This move highlights how AI is becoming more integrated into daily household routines.
NVIDIA is creating AI models that are optimized to work seamlessly with their hardware. This approach could make AI systems faster and more efficient for everyone.
Meta has pulled a controversial AI feature from Instagram that let users generate images based on others' posts. The company admitted the tool 'missed the mark' after widespread criticism.
Isitsecure is a new open-source tool that scans AI-generated code for security vulnerabilities. It combines static and dynamic testing to help developers build safer applications.