一个生成视觉模型以高数据效率训练并打破基于文本的CAPTCHA
Dileep George1, Wolfgang Lehrach2, Ken Kansky2
1Vicarious AI, 2 Union Square, Union City, CA 94587, USA. dileep@vicarious.com miguel@vicarious.com.
概括
这项研究引入了一种由神经科学启发的新型概率生成模型. 与深度学习相比,该模型实现了更高的概括性和数据效率,甚至打破了CAPTCHA防御.
科学领域:
- 计算机视觉
- 人工智能
- 系统神经科学
背景情况:
- 人类的视觉智能擅长从少数例子中学习,
- 目前的机器学习模型在概括和数据效率方面难以与人类相匹配.
- 现有的模型通常需要大量的数据集, 缺乏强大的推理能力.
研究的目的:
- 通过系统神经科学来开发视觉的概率生成模型.
- 实现统一的识别,细分和推理能力.
- 提高人工智能模型中的数据效率和概括性.
主要方法:
- 开发了一个视觉概率模型.
- 采用基于消息传递的推断来进行统一处理.
- 通过系统神经科学原理获得灵感.
主要成果:
- 这种模型表现出了优秀的概括和封闭推理能力.
- 在具有挑战性的文本识别基准测试中表现优于深度神经网络.
- 与深度学习模型相比,数据效率提高了300倍.
- 在基于文本的CAPTCHA中成功分割字符,打破它们的防御.
结论:
- 提出的模型为人工通用智能提供了一个有前途的方向.
- 强调数据效率和构成性在人工智能开发中的重要性.
- 这表明神经科学启发的方法可以带来更强大的AI系统.
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