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One Image is All It Takes: Hijacking AI’s Visual Reasoning

27.11.2025 by qfx

A malicious actor exploits a multi-modal large language model (MLLM) controlling a vehicle by introducing a visual perturbation, which causes the system to interpret image semantics in a manner that directs the vehicle towards a predetermined destination, effectively hijacking the decision-making process.

Researchers demonstrate how a single, subtle manipulation of an image can redirect the decision-making process of powerful artificial intelligence systems.

Categories Science

Chasing the Sun’s Fury: Progress and Pitfalls in Flare Forecasting

26.11.2025 by qfx

The study demonstrates that energy released during solar flares partitions into varying forms, with larger events exhibiting a distinct distribution compared to smaller flares, suggesting a fundamental scaling relationship in the dynamics of these energetic phenomena-a reminder that even the most comprehensive theory may ultimately be consumed by the scale of reality.

A new review examines how our ability to predict solar flares-powerful eruptions that can disrupt space weather and impact Earth-is evolving from physics-based simulations to cutting-edge data-driven methods.

Categories Science

Powering Up Stability Analysis with Artificial Intelligence

26.11.2025 by qfx

A new approach uses AI to automate the complex process of predicting power grid stability, accelerating analysis and optimizing system design.

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When AI Stumbles: Mapping the Hidden Risks of Language Models

26.11.2025 by qfx

As large language models become increasingly integrated into critical applications, understanding how – and why – they fail is paramount.

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Predictive Safety: A Smarter Approach to Collision Warnings

26.11.2025 by qfx

The collision warning system’s overall performance suggests a complex interplay between responsiveness and stability, where aggressive sensitivity-while reducing time to impact- simultaneously amplifies the risk of spurious alerts and ultimately undermines trust in the system's reliability.

A new framework leverages advanced attention networks and dynamic risk assessment to dramatically improve forward collision warning systems in challenging traffic conditions.

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Beyond the Hype: Assessing AI Safety in 2025

26.11.2025 by qfx

A new report details the evolving landscape of technical and institutional safeguards designed to manage the risks of increasingly powerful artificial intelligence.

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Seeing Smoke: AI Models for Faster Wildfire Response

26.11.2025 by qfx

Forest fire detection systems struggle to reliably distinguish between genuine smoke plumes and atmospheric phenomena like sunset-colored skies, distant clouds, fog, or even faint nighttime smoke-challenges exacerbated by the difficulty of identifying smoke without thermal imaging at a distance.

New research demonstrates how advanced image recognition systems are improving the speed and accuracy of early wildfire detection.

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Smoothing the Future: A New Approach to Time-Series Forecasting

26.11.2025 by qfx

Researchers are boosting prediction accuracy by introducing a method that separates coarse-grained trends from fine-grained details in time-series data.

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AI’s Safety Net: Using Artificial Intelligence to Validate Artificial Intelligence

26.11.2025 by qfx

A system translates abstract natural language requirements into concrete monitoring practices, effectively closing the loop between intention and observation.

As AI systems become increasingly complex and permeate safety-critical applications, researchers are exploring innovative ways to ensure their reliability and prevent potentially catastrophic failures.

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Modeling Market Futures: A New Simulation Framework

26.11.2025 by qfx

The variability within each process, as measured by standard deviation, diminishes with increasing simulation length, suggesting that extended observation periods are crucial for accurately characterizing system behavior and predicting future states-a necessary condition given that all models are, ultimately, prophecies of failure.

A novel approach to long-term financial modeling incorporates realistic volatility and drift uncertainty for improved forecasting accuracy.

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