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AMD CEO Lisa Su shifts focus to enterprise AI as PC market faces uncertainty. Client revenue up 37% despite memory shortages affecting the consumer segment.

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Microsoft just launched a detector revealing how attackers plant hidden backdoors in AI models—and the warning signs are chilling. This episode breaks down the emerging threat of AI model poisoning, where neural networks are compromised from the inside with "sleeper agents" that activate on command.

Discover the three critical behavioral anomalies that expose poisoned models: sudden attention shifts, memorization bias toward malicious data, and fragmented trigger activation. We examine Microsoft's latest research on backdoor detection, explain why traditional security tests fail to catch these threats, and explore what this means for organizations deploying AI systems.

Key topics covered:

The difference between model collapse and model poisoning

How attackers inject Trojan instructions into neural network parameters

Why just 250 poisoned documents can compromise large language models

Microsoft's new scanner tool and its limitations

Practical detection strategies for AI security teams

Whether you're an AI developer, security professional, or tech lover concerned about AI safety, this analysis provides the knowledge you need without the hype.

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