Artificial Intelligence: Inference for Machine Learning

pchan — Sun, 27 Feb 2022 20:13:53 GMT

Current Revision posted to Documents by pchan on 2/27/2022 8:13:53 PM

Artificial Intelligence III: Inference for Machine Learning

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1. Introduction

We have all seen films where humankind implements an artificial intelligence (AI) system, something goes wrong, and it turns against its creators. 2001: A Space Odyssey and the Terminator films are classic examples. But what is artificial intelligence, really, and how do we use it in our applications, especially at the edge?

When it comes to working with artificial intelligence, we really are standing on the shoulders of giants. The term AI itself was first used by John McCarthy in 1956, when the earliest conference on artificial intelligence was held. Even before the AI term had been coined, however, there was considerable research being undertaken on the question of whether or not machines think. One early leading researcher was Alan Turing, the Bletchley Park code breaker, who posed the Turing Test. The test revolves around the question of whether we will ever be able to build a computer that can sufficiently imitate a human, to the point where a suspicious judge can't tell the difference between what is human and what is machine.

AI research has been a hot topic over the years. The late 1990s saw IBM's Deep Blue supercomputer beat chess grandmasters. More recently, we have seen significant interest in artificial intelligence and machine learning at the edge, deployed within embedded systems.

But what is machine learning? According to the book Machine Learning by Tom M. Mitchell, Professor at Carnegie Mellon University, "Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience."

A simple example of this is providing a machine learning algorithm with a data set of pictures with labels. The machine learning (ML) algorithm will be able to identify features and elements of the pictures, so that when a new unlabeled picture is input, the ML algorithm is able to determine how likely the image is to contain any of the learned features and elements. The classic example of this is the machine learning algorithm being able to identify pictures of animals, fruits, and so on.

These machine learning algorithms can, therefore, be said to have two distinct elements: training and inference. Training is process in which the features and elements that need to be identified are learned, while inference is using the trained behavior to examine unlabeled inputs to identify the elements and features once deployed.

2. Objectives

This Essentials learning module will introduce you to the basics of artificial intelligence (AI) and machine learning (ML) inference. The learning objectives are as follows:

Gain an understanding of inference and its role in AI and ML systems.

Learn about neural networks and what they consist of.

Become familiar with the different network types that can be used for AI inference.

See examples of different methodologies and frameworks which can be used for creation, training, and inference of AI and ML.

3. Neural Networks

Artificial Intelligence: Inference for Machine Learning

Artificial Intelligence: Inference for Machine Learning

Artificial Intelligence Inference for Machine Learning

Artificial Intelligence Inference for Machine Learning

Artificial Intelligence III Inference for Machine Learning

Artificial Intelligence III Inference for Machine Learning

element14 Essentials: Artificial Intelligence III

element14 Essentials: Artificial Intelligence III