I-Corps: Artificial Intelligence and Deep Learning System for Product Search

I-Corps：用于产品搜索的人工智能和深度学习系统

• 批准号: 1648728
• 负责人: Carol Mimura
• 金额: $ 5万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1648728&HistoricalAwards=false
• 关键词: Corps; Artificial; Intelligence; Deep; Learning

The broader impact/commercial potential of this I-Corps project are in various applications in ecommerce, security, robotics, etc. This project's technology enables computer systems and their users to better contextually understand products and locate them. For example, in e-commerce, this technology enables people to make informed decisions about product selection. This may enhance the e-tail experience and help to eliminate wasted time as people can easily and precisely find things they desire. The aggregated data on product searches can be used by suppliers to better understand and plan for demand. Also, in robotics, as another example, computer vision is an essential element of the technology and the multi-object detection system built into software will enable robots to be more effective as they will "understand" products or objects in the context of our world. In security, this technology will enable better detection of theft and threats. Overall, the technology has many applications - many that can make lives more convenient and safer.This I-Corps project is based on machine learning technology. Billions of data points that include images and textual information about products are trained into artificial neural networks. These neural networks understand products with context with both images and natural language. Further, the neural networks are able to self-learn with new information from the internet. This technology was developed after researching various machine learning methods for large scale objection detection and search. The result is that neural networks provide the most scalable and efficient technology in terms of object detection. The uniqueness of this system is that parts of the learning in the neural network can be changed without affecting the rest of the network and the network can self-learn from the internet about products and other choice-centric decisions.

这个I-Corps项目的更广泛的影响/商业潜力是在电子商务、安全、机器人等领域的各种应用。该项目的技术使计算机系统及其用户能够更好地从上下文中理解产品并对其进行定位。例如，在电子商务中，这项技术使人们能够在产品选择方面做出明智的决定。这可能会提高电子零售的体验，并有助于消除浪费的时间，因为人们可以轻松准确地找到他们想要的东西。供应商可以使用产品搜索的汇总数据来更好地了解和规划需求。此外，在机器人技术中，计算机视觉是该技术的重要组成部分，内置在软件中的多目标检测系统将使机器人更有效，因为它们将“理解”我们世界中的产品或物体。在安全方面，这项技术将能够更好地检测盗窃和威胁。总的来说，这项技术有很多应用——很多可以使生活更方便、更安全。这个I-Corps项目是基于机器学习技术。数十亿个数据点，包括产品的图像和文本信息，被训练成人工神经网络。这些神经网络通过图像和自然语言来理解产品。此外，神经网络能够通过互联网上的新信息进行自我学习。该技术是在研究了各种用于大规模目标检测和搜索的机器学习方法后发展起来的。结果是，神经网络在目标检测方面提供了最具可扩展性和最有效的技术。该系统的独特之处在于，神经网络中的部分学习可以在不影响网络其余部分的情况下进行改变，并且网络可以从互联网上自我学习产品和其他以选择为中心的决策。