Ddos 
Meta recently unveiled a white paper titled “Frontier AI Framework,” which defines two categories of high-risk AI systems: high-risk and critical-risk. Both classifications pertain to AI systems that could potentially be exploited for cybersecurity attacks or in fields related to chemical and biological weaponry. The fundamental distinction lies in the severity of their consequences—while high-risk systems pose a manageable yet significant threat, critical-risk systems could lead to catastrophic, uncontrollable outcomes.
Meta emphasized that its risk assessment for AI systems is not based on a single, objective metric but rather on a comprehensive evaluation that integrates insights from both internal researchers and external experts, with final decisions subject to review by senior executives. The company also acknowledged that there is currently no precise, quantifiable standard for measuring AI system risks, meaning that assessments largely rely on ongoing research.
Regarding high-risk AI systems, Meta has adopted a restrictive internal access policy, ensuring that such systems will not be made publicly available until sufficient protective measures have been implemented. For critical-risk AI systems, the company plans to enforce stringent security protocols to mitigate potential threats. Should a security breach occur, all development activities will be suspended until the system’s safety can be fully assured.
Previously, Meta pledged to develop general AI systems capable of performing a wide range of tasks and to make these systems publicly accessible. However, this commitment has sparked market concerns, particularly given that Meta’s Llama large-scale natural language model is open-source and has been downloaded hundreds of millions of times. Furthermore, Llama has been leveraged in the development of military AI applications, raising concerns about heightened security risks.
The release of the “Frontier AI Framework” white paper appears to be Meta’s response to growing apprehensions surrounding its AI development strategy, underscoring the critical importance of integrating security safeguards into artificial intelligence systems.
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