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Research Outline

Prepared for Michael K. | Delivered June 4, 2020

Artificial Intelligence (AI), Machine Learning and Deep Learning Case Studies

Goals

To inform and add specific examples to a thought leadership article, find 1-2 case studies on projects for each of the following topics:
  • Artificial Intelligence (AI) in its broader category.
  • Machine Learning (ML): A subset of AI focuses on machines that learn on defined data sets with a specific focus (training).
  • Deep Learning: A subset of ML, where neural networks analyze massive amounts of complex data (video, images, among others) to learn and understand with limited supervision.
The case studies must be from recognizable companies or sources highlighted in the public press. Find 1-2 case studies for each topic (1-2 for AI, 1-2 for ML and 1-2 for Deep Learning).

Early Findings

Artificial Intelligence (AI) is a topic that is being widely covered by the media. These initial findings present a few examples:

Artificial Intelligence (Broader category):

  •  Google has added various "smart" functions to their email and G-Suite productivity apps. On email, these include suggesting ways to complete your sentences while drafting an email and advising responses to incoming messages.
  •  Amazon uses artificial intelligence to predict demand: The company collects significant quantities of data about customer's buying habits, enabling them to know what items to recommend and predict what items to ship.

Machine Learning (ML):

  • Since 2017, JP Morgan Chase introduced Contract Intelligence (COIN) technology. COIN uses machine learning to review and interpret commercial loan agreements. This software can scan documents in seconds and is less prone to human error.
  • Intuit, the software giant behind QuickBooks and TurboTax, implemented a Machine Learning platform and made it available to the users of their products. For example, one of the models implemented for TurboTax help users decide between standard and itemized deductions.

Deep Learning:

  •  NVIDIA has developed advanced platforms that allow researchers and data scientists to prototype, train and deploy advanced computer vision applications. These include advanced image classification, object detection, image restoration and segmentation.
  •  Facebook implemented its translation system in the deep learning framework Caffe2. The system allows Facebook to do billions of translations every day. It is used for short translations, such as status updates posted by people to their Facebook profiles.
There are many lists articles that list AI case studies and examples worth exploring, for example: