Apr 2, 2016

Fields of Artificial Intelligence

I've come across a number of great landscapes covering companies operating in different areas of artificial intelligence, but those frameworks never quite covered the actual structure of AI for me. So I decided to put together a framework showing what I was looking for!

*Note*: This framework is a work in progress. Hit me up if you have feedback, or just check back soon for an update.

One of the biggest challenges I've seen in trying to lay out the AI landscape is confusion around taxonomy. Is a company a machine learning startup, a computer vision startup, or a driverless car startup? Well, it can be all three at once. Machine learning is a category of AI techniques. Computer vision is an application of a variety of AI technologies (including machine learning). And driverless cars make use of "smart" video analysis, a use case that involves computer vision.

To try to make more sense of the landscape, I want to break artificial intelligence nomenclature into three groups: use cases, applications, and technologies.

To that end, I want to start with a (who am I kidding?) comprehensive list for each of the three groups:
Use cases

  • Chat bot / Personal assistant
  • "Smart" image search
  • "Smart" video analysis
  • Writing
  • Anomaly detection
  • Recommendation/comparison engine
  • Robotics

  • Computer vision
  • Pattern/anomaly detection
  • Natural Language Processing (NLP)
  • Natural Language Querying (NLQ)
  • Natural Language Generation (NLG)
  • Recommendation/comparison engine
  • Speech-to-text

  • Methods/Techniques
    • Machine learning
      • Artificial neural networks
        • Deep learning
        • Convolutional neural networks
        • Recurrent neural networks
      • Cluster analysis/categorization
      • Decision trees
        • Random forests
    • Logic
      • Semantic graph
  • Models/Algorithms
    • Stochastic gradient descent
    • Evolutionary algorithms
    • Learning types
      • Supervised Learning
      • Unsupervised Learning
      • Reinforcement Learning

Thank you to Sonia Nagar, Ablorde Ashigbi, Konstantine Buhler, and Melissa Caldwell for their help in thinking through this