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.

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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.

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PaLM 2 is Google's next generation large language model that builds on Google’s legacy of breakthrough research in machine learning and responsible AI. It excels at advanced reasoning tasks, including code and math, classification and question answering, translation and multilingual proficiency, and natural language generation better than previous state-of-the-art LLMs. It can accomplish these tasks because of the way it was built – bringing together compute-optimal scaling, an improved dataset mixture, and model architecture improvements. This article offers a quick and straightforward method for leveraging the PaLM 2 API to extract knowledge and ask questions from text.

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.

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