What are the Major Types of Artificial Intelligence?

Inputs & Outputs

Artificial intelligence is a tool that allows you to intuitively discover the relationship between inputs and outputs. What goes into an AI model can be translated, converted, and put to work via a variety of methodologies.

For example, an input may be drone footage of a disaster area and its output could be a damage estimate.

In this guide, we’ll explore all of the different kinds of inputs and outputs you can work with. These possibilities, when combined with your existing data, can give you the building blocks you need to string together an AI solution for your organization.

GET THE GUIDE ▸

Explore Our Other Guides

We've written up lots of articles to help business professionals orient themselves around AI. Learn how Artificial intelligence can meaningfully change how your organization does business by exploring the resources below.

Or Get Everything as a Single Ebook.
▾

4197-the-complete-guide-to-bringing-artificial-intelligence-to-your-organization.png

GET THE EBOOK ▸

OUR LATEST RESOURCES

OUR LATEST ARTICLES

DevFest West Coast 2020

Watch videos of some of the world's top AI experts discuss everything from Tensorflow Extended to Kubernetes to AutoML to Coral.

Video: Machine Learning Engineering with Tensorflow Extended

In this talk, Hannes is providing insights into Machine Learning Engineering with TensorFlow Extended (TFX). He introduces how TFX for machine learning pipeline tasks and how to orchestrate entire ML pipelines with TFX. The audience learns how to run ML production pipelines with Kubeflow Pipelines, and therefore, free the data scientist's time from maintaining production machine learning models.

Video: How to Build a Reproducible ML Pipeline

Solving a data science problem usually requires multiple steps. These steps can include extracting and transforming data, training a model, and deploying the model into production. In this session, we'll discuss how to specify those steps with Python into an ML pipeline. We'll show how to create a Kubeflow Pipeline, a component of the Kubeflow open-source project. The audience will learn about how to integrate TensorFlow Extended components into the pipeline, and how to deploy the pipeline to the hosted Cloud AI Pipelines environment on Google Cloud. The key takeaway is how to improve reuse and reproducibility of the machine learning process.

OUR HEADQUARTERS

Headquartered in the heart of Portland, Oregon, our satellite offices span North America, Europe, the Middle East, and Africa.

(503) 922-1164

Our address is
US Custom House
220 NW 8th Ave