Explore AI’s role in cancer drug discovery, robotics safety concerns, and Meta’s new materials discovery tools. Plus, find the 5 best AI-powered no-code app and website building tools.

In today’s power-packed edition:

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A new study in Nature Communications explored how AI can help discover cancer treatments by predicting which gene combinations (called synthetic lethality) can kill cancer cells. 

The top model, SLMGAE, did well but still struggles to predict new gene pairs—something crucial for real-world applications. 

Improving data quality boosted accuracy, showing how AI could change cancer therapy in the future.

Recent research has raised alarms about AI safety in robots. 

The research introduced ROBOPAIR, an algorithm designed to jailbreak LLM-controlled robots.

What's concerning is that these attacks can trick robots into doing dangerous things, like causing collisions, ignoring safety rules, or even simulating bomb detonation. 

Worryingly, these hacks had a 100% success rate. 

As we adopt more AI in robotics, it's crucial to develop stronger defenses against these attacks.

Meta AI released one of the largest materials science datasets ever: OMat24, containing over 110 million calculations. 

Paired with their EquiformerV2 model, this resource helps predict critical properties for material stability. 

If you’re into materials science or AI, Meta’s tools are freely available for researchers on Hugging Face.

A new technique called Latent Action Pretraining (LAPA) is making it easier to train robots. 

By breaking down actions from internet videos, LAPA allows robots to learn from everyday human behavior, without needing specific labels. 

This could speed up and scale how robots are trained, opening up new possibilities for AI-powered systems.

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