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Unexplained dark streaks on Mars, thought to be evidence of liquid water flow in recent years, could just be marks left by blowing sand and dust, according to new artificial intelligence (AI) research. First detected by NASA’s Viking mission in 1976, these streaks are dark, narrow lines that creep down some Martian slopes and cliffs. Scientists had initially suspected that salty water runoff caused them, especially given their seasonal nature. An AI that has been taught to find streak patterns has recently called that notion into question, saying that the characteristics show up where dust and wind are strong.

AI Analysis Reveals Mars’s Dark Slope Streaks Likely Caused by Dust, Not Flowing Water

As per a Nature Communications report published on May 19, researchers used a machine learning algorithm trained on thousands of confirmed streaks to analyse over 86,000 satellite images. In one such study by Brown University, slope streaks were more likely to occur in heavily dusty regions with strong wind activity. The authors compared a global map of 500,000 streaks to climate and geology and found that dry processes were most likely to be forming these streaks.

The streaks are called slope streaks and recurrent slope lineae (RSL), and they would suggest that there is water activity on Mars. Now it seems more plausible that they were formed by thin layers of dust slipping off steep slopes rather than liquid water running over the top.

If validated, these findings could reshape the priorities of Mars exploration. Areas once believed to hold signs of ancient water — and thus possible microbial life — may be misleading. Valantinas noted that AI lets researchers rule out improbable theories from a distance, which cuts down on the need to deploy missions to less viable places. The findings might potentially make it easier to find real biosignatures on future expeditions.

This new research is helping to winnow out dead ends on Mars’s geologic history and ability to support life, scientists stated, as AI and more advanced missions shape up to hone our understanding.

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AI Model Learns to Predict Human Gait for Smarter, Pre-Trained Exoskeleton Control

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Scientists at Georgia Tech have created an AI technique that pre-trains exoskeleton controllers using existing human motion datasets, removing the need for lengthy lab-based retraining. The system predicts joint behavior and assistance needs, enabling controllers that work as well as hand-tuned versions. This advance accelerates prototype development and could improve…

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Scientists Build One of the Most Detailed Digital Simulations of the Mouse Cortex Using Japan’s Fugaku Supercomputer

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Researchers from the Allen Institute and Japan’s University of Electro-Communications have built one of the most detailed mouse cortex simulations ever created. Using Japan’s Fugaku supercomputer, the team modeled around 10 million neurons and 26 billion synapses, recreating realistic structure and activity. The virtual cortex offers a new platform for studying br…

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UC San Diego Engineers Create Wearable Patch That Controls Robots Even in Chaotic Motion

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UC San Diego engineers have developed a soft, AI-enabled wearable patch that can interpret gestures with high accuracy even during vigorous or chaotic movement. The armband uses stretchable sensors, a custom deep-learning model, and on-chip processing to clean motion signals in real time. This breakthrough could enable intuitive robot control for rehabilitation, indus…

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