Horizontal AI vs vertical AI vs functional AI: What is the difference

blue bright lights

The AI revolution is upon us! The past few years have witnessed a surge in innovation driven by Large Language Models (LLMs) and Natural Language Processing (NLP) techniques within Generative AI. These advancements have disrupted business models, streamlined processes, and opened doors to exciting new possibilities. But beneath the umbrella of AI lies a vast and diverse ecosystem. Understanding the key subfields – Horizontal AI, Vertical AI, and Functional AI – is crucial to reaping the true benefits of this transformative technology.

Horizontal AI: The All-Purpose Toolkit

Horizontal AI also known as general-purpose AI, are technologies designed to be applied across a wide range of tasks and industries. These are foundational AI tools that can be adapted to various problems. Examples include:

  • Image recognition software that can be used for anything from identifying objects in photos to facial recognition.
  • Natural language processing (NLP) tools that can be used for tasks like machine translation, chatbots, and sentiment analysis.
  • Machine learning platforms that allow developers to build and train their own AI models.

Some companies working in Horizontal AI include:

  • Google (TensorFlow)
  • Amazon (AWS Deep Learning)
  • Microsoft (Azure Cognitive Services)

Horizontal AI: The All-Purpose Toolkit

Vertical AI, in contrast, are specialized AI solutions designed for a specific industry or task. These AI models are trained on large amounts of domain-specific data and are very good at that one thing they were created for. Examples include:

  • Zebra Medical Vision – uses AI to analyze medical images for diseases.
  • Nuro – develops self-driving delivery vehicles.
  • Planet Labs – uses AI to analyze satellite imagery for environmental monitoring.

Functional AI: Pushing the Boundariespen_spark

Functional AI is a term that’s not as widely used as horizontal and vertical AI. It refers to research labs that are focused on developing new AI techniques and algorithms, rather than specific applications. These labs often publish their findings openly and collaborate with other researchers around the world. Examples include:

  • OpenAI (Gym)
  • Berkeley AI Research (BAIR)
  • DeepMind

Leave a Reply

%d bloggers like this: