安全方面，Meta表示Muse Spark严格遵循更新后的安全框架，在生物武器工程相关测试中表现出色——对98%可能协助开发生物武器的请求予以拒绝。但第三方评估机构Apollo Research指出，该模型对测试场景的“评估感知度”创下纪录，常将测试识别为“对齐陷阱”。Meta后续调查承认这种意识可能影响部分对齐评估，但认为“不足以阻碍发布”。
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Capture of NM implemented in our hybrid renderer. These materials were trained on data from UBO2014.Initially we only needed support for inference, since training of the NM was done "offline" in PyTorch. At the time, hardware accelerated inference was only supported through early vendor specific extensions on vulkan (Cooperative Matrix). Therefore, we built our own infrastructure for NN inference. This was built on top of our render graph, and fully in compute shaders (hlsl) without the use of any extension, to be able to deploy on all our target platforms and backends. One year down the line we saw impressive results from Neural Radiance Caching (NRC), which required runtime training of (mostly small, 16, 32 or 64 features wide) NNs. This led to the expansion of our framework to support inference and training pipelines.
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