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人工智能世界中的数据安全.pptx

上传人: 王** 编号:171085 2024-07-23 19页 2.52MB

1、Abhishek Das-Founder,VP of EngineeringDhruv Jain-Founder,Chief Product OfficerData Security in the AI WorldIHELLO DAVE.AM HERE NOW.Only 1/3rd of AI projects have reached productionModel Output Accuracy:39%*Retool,2023 State of AI AdoptionTop pain points for AI appsData Security:33%Hallucinations:28%

2、LLMs are the new era for Natural Language ProcessingDeep Neural NetworksTransformer Architecture with Self-attentionToken Embeddings&Context Similarity OK!This is not a technical talk on LLMsFoundational(LLM)ModelsPre-trained Foundational Models1234Closed-source(e.g.GPT,Claude,PaLM)Open-source(e.g.L

3、lama)CustomCompetitive advantage Privacy/confidentialityModel behavior guardrailsSingle vs Multi-model Multi-modal Compound AI SystemsRecommendation systemsChatbotsKnowledge-base Q&ALeveraging Foundational Models for InferenceUsing Proprietary DataArchitectural PatternsUse-CasesPrompt EngineeringRAG

4、Fine-tuning23No proprietary dataNo updates/changes to model weightsUnlocking the value of your proprietary dataAdd training on domain-specific datasetUpdates/changes to model weightsAdd&continuously update domain-specific knowledge-baseNo updates/changes to model weights*Retool,2023 State of AI Adop

5、tion1Almost 75%of enterprises are looking to use RAG or Fine-Tuning architectures*75%RAG and Fine-tuning Architectures are the Growing TrendQuery ExpansionQuery RewritingRe-rankingAugmentationInference LayerUser/Application InterfacesRoutingQUERYPrompt Eng+AugmentationPost-processingLLMModel LayerRE

6、SPONSEData LayerQuery ExpansionQuery RewritingRe-rankingAugmentationInference LayerUser/Application InterfacesRoutingQUERYPrompt Eng+AugmentationPost-processingLLMData LayerModel LayerRESPONSEQuery ExpansionQuery RewritingRe-rankingAugmentationInference LayerUser/Application InterfacesRoutingQUERYPr

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本文主要探讨了在AI世界中数据安全的重要性,以及如何利用预训练的基础模型进行推理。文章指出,在AI项目中,只有1/3的项目已经达到了生产阶段,而数据安全是AI应用的主要痛点之一。文章还提到了模型输出准确率仅为39%。此外,文章讨论了AI风险,包括数据风险、隐私风险、训练数据中毒、 prompt 操纵、未授权访问和敏感数据泄露等。文章最后提到了Acante公司,该公司正在解决这些数据层风险。
如何应对挑战?" "如何利用基础模型为推理提供动力?" 如何防范?"
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