ai诈骗现阶段研究进展及其成果

近期神经网络研究的巨大发展始于2010年左右，当时开始出现可用的大型公共数据集。一个名为ImageNet的大型图像集合包含了约1,400万张带注释的图像，这催生了[ImageNet大规模视觉识别挑战赛](https://image-net.org/challenges/LSVRC/)。2012年，卷积神经网络首次被用于图像分类，使得分类错误率大幅下降（从近30%降至16.4%）。2015年，微软研究院的ResNet架构达到了人类水平的准确率。从那时起，神经网络在许多任务中都表现得非常成功：|年份|实现人类水平准确率||-|-||2015|[图像分类](https://doi.org/10.1109/ICCV.2015.123)||2016|[对话语音识别](https://arxiv.org/abs/1610.05256)||2018|[自动化机器翻译](https://arxiv.org/abs/1803.05567)（从中文到英文）||2020|[图像描述](https://arxiv.org/abs/2009.13682)|在过去几年中，我们见证了大型语言模型的巨大成功，例如BERT和GPT-3。这主要归功于有大量的通用文本数据可供使用，让我们可以训练模型来捕捉文本的结构和含义，在通用文本集合上对它们进行预训练，然后针对更具体的任务对这些模型进行专门化。我们将在本课程的后半部分学习更多有关自然语言处理的知识。[heading1]🚀挑战[content]浏览一下互联网，在你看来，人工智能在哪里得到了最有效的应用。是在地图应用程序中，还是在语音转文字服务或视频游戏中？研究这些系统是如何构建的。[heading1][课后测试](https://red-field-0a6ddfd03.1.azurestaticap

AI幻觉的历史告诉我们，这个难题一直伴随着AI的发展，如同一个甩不掉的“影子”。那么，面对AI幻觉，我们是否只能束手无策？当然不是！近年来，研究人员们已经开发出多种技术手段，试图“驯服”这个难以捉摸的“幽灵”，让AI变得更加可靠和值得信赖。[heading2]数据“体检”：为AI打好基础[content]正如我们在前文中提到的，低质量的训练数据是导致AI幻觉的重要原因。因此，为AI模型提供“干净”、“健康”的训练数据，就如同给AI做一次全面的“体检”，是预防AI幻觉的根本措施。数据清洗：就像医生为病人清除体内的毒素一样，数据科学家们会利用各种技术手段，对AI的训练数据进行“清洗”，去除错误信息、补充缺失数据、修正不一致的内容，并尽可能消除数据中的偏见。数据增强：为了让AI模型学习到更全面的知识，我们需要为它提供更多、更丰富的训练数据，就像给学生补充各种类型的练习题，帮助他们掌握不同的知识点和解题技巧。例如，在训练一个图像识别模型时，我们可以对已有的图像进行旋转、缩放、裁剪等操作，生成更多新的样本，从而提高模型的泛化能力。

Require that developers of the most powerful AI systems share their safety test results and other critical information with the U.S.government.In accordance with the Defense Production Act,the Order will require that companies developing any foundation model that poses a serious risk to national security,national economic security,or national public health and safety must notify the federal government when training the model,and must share the results of all red-team safety tests.These measures will ensure AI systems are safe,secure,and trustworthy before companies make them public.Develop standards,tools,and tests to help ensure that AI systems are safe,secure,and trustworthy.The National Institute of Standards and Technology will set the rigorous standards for extensive red-team testing to ensure safety before public release.The Department of Homeland Security will apply those standards to critical infrastructure sectors and establish the AI Safety and Security Board.The Departments of Energy and Homeland Security will also address AI systems’ threats to critical infrastructure,as well as chemical,biological,radiological,nuclear,and cybersecurity risks.Together,these are the most significant actions ever taken by any government to advance the field of AI safety.Protect against the risks of using AI to engineer dangerous biological materials by developing strong new standards for biological synthesis screening.Agencies that fund life-science projects will establish these standards as a condition of federal funding,creating powerful incentives to ensure appropriate screening and manage risks potentially made worse by AI.Protect Americans from AI-enabled fraud and deception by establishing standards and best practices for detecting AI-generated content and authenticating official content.The Department of Commerce will develop guidance for content authentication and watermarking to clearly label AI-generated content.Federal agencies will use these tools to make it easy for Americans to know that the communications they receive from their government are authentic—and set an example for the private sector and governments around the world.