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AI Is Learning to Write Genetic Code

Machine learning models were successfully trained to generate viable viral genome sequences by learning from example bacteriophage designs, creating hundreds of thousands of potentially functional bioweapons. This research highlights the dual-use risks of deploying AI systems that can synthesize dangerous biological code from training examples.

Why this matters

Machine learning models were successfully trained to generate viable viral genome sequences by learning from example bacteriophage designs, creating hundreds of thousands of potentially functional bioweapons. This research highlights the dual-use risks of deploying AI systems that can synthesize dangerous biological code from training examples.

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This explanation is Korpalis’s guide to the material, not a replacement for it. Read the publisher’s page for the full method, evidence and limitations.

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Why Are LLM Backdoor Defenses Fragmented? A Feature-Level Explanation with Sparse Autoencoders

Using sparse autoencoders to analyze LLM backdoors at the feature level, researchers explain why existing defenses remain fragmented and ineffective against both dirty-label and clean-label poisoning. The mechanistic analysis identifies where defense gaps originate in the model's learned representations.

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