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Category: robotics/AI – Page 324

The Rabbit R1 handheld AI device is a simple Android device, and a developer made the AI run on an iPhone.

The Rabbit R1 offers the ability to answer queries and perform tasks using AI, instead of using an iPhone directly. However, the work of one enterprising developer has resulted in a clone of the “iPhone-killer” which can run on an iPhone.

In X tweets on Monday, Will Hobick of Flutterflow posted that he would be posting a “cloneable template” of the Rabbit R1 app later in the week. In a follow-up post on Tuesday, he demonstrates a version of the app running on an iPhone.

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AI and robotics are rapidly advancing, raising concerns about their potential to replace humans in various tasks and sparking debates about robot rights and potential dangers Questions to inspire discussion What is the potential impact of AI and robotics on human tasks? —AI and robotics have the potential to replace humans in various tasks, sparking debates about robot rights and potential dangers.

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Most antibiotics target metabolically active bacteria, but with artificial intelligence, researchers can efficiently screen compounds that are lethal to dormant microbes.

Since the 1970s, modern antibiotic discovery has been experiencing a lull. Now the World Health Organization has declared the antimicrobial resistance crisis as one of the top 10 global public health threats.

When an infection is treated repeatedly, clinicians run the risk of bacteria becoming resistant to the antibiotics. But why would an infection return after proper antibiotic treatment? One well-documented possibility is that the bacteria are becoming metabolically inert, escaping detection of traditional antibiotics that only respond to metabolic activity. When the danger has passed, the bacteria return to life and the infection reappears.

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A new study examines whether and how well multimodal AI models understand the 3D structure of scenes and objects.

Researchers from the University of Michigan and Google Research investigated the 3D awareness of multimodal models. The goal was to understand how well the representations learned by these models capture the 3D structure of our world.

According to the team, 3D awareness can be measured by two key capabilities: Can the models reconstruct the visible 3D surface from a single image, i.e., infer depth and surface information? Are the representations consistent across multiple views of the same object or scene?

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