For many decades, researchers have been trying to simulate human brain activity through the field known as artificial intelligence, or AI for short. In 1956, a group of people met at the Dartmouth Summer Research Project on Artificial Intelligence, an event that is widely accepted as the first group discussion about AI as it’s known today. Researchers were trying to prove that many aspects of the learning process could be precisely described and, therefore, automated and replicated by a machine. Today, you know they were right!
Many other terms appeared in this field, such as machine learning (ML) and deep learning (DL). These sub-areas of AI have also been evolving for many decades (granted, nothing here is new to the science). However, with the natural advance of the information society and, more recently, the advent of big data platforms, AI applications have been reborn with much more applicability – power (because now there are more computational resources to simulate and implement them) and applicability (because now information is everywhere).
Even more recently, cloud service providers have put AI in the cloud. This helps all sizes of companies to reduce their operational costs and even lets them sample AI applications, considering that it could be too costly for a small company to maintain its own data center to scale an AI application.
An incredible journey of building cutting-edge AI applications has emerged with the popularization of big data and cloud services. In June 2020, one specific technology gained significant attention and put AI on the list of the most discussed topics across the technology industry – its name is ChatGPT.
ChatGPT is a popular AI application that uses large language models (more specifically, generative pre-trained transformers) trained on massive amounts of text data to understand and generate human-like language. These models are designed to process and comprehend the complexities of human language, including grammar, context, and semantics.
Large language models utilize DL techniques (for example, deep neural networks based on transformer architecture) to learn patterns and relationships within textual data. They consist of millions of parameters, making them highly complex and capable of capturing very specific language structures.
Such mixing of terms and different classes of use cases might get one stuck on understanding the practical steps of implementing AI applications. That brings you to the goal of this chapter: being able to describe what the terms AI, ML, and DL mean, as well as understanding all the nuances of an ML pipeline. Avoiding confusion about these terms and knowing what exactly an ML pipeline is will allow you to properly select your services, develop your applications, and master the AWS Machine Learning Specialty exam.
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