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Principles of Data Science

Introduction

Principles of Data ScienceIntroduction
Figure 7.1 Neural networks are central to modern AI, especially in the fields of deep learning and machine learning. Inspired by the human brain's structure and functioning, neural networks consist of interconnected layers of artificial neurons designed to recognize patterns, learn from data, and make decisions or predictions. (credit: modification of work "Machine Learning & Artificial Intelligence" by www.vpnsrus.com/mikemacmarketing, CC BY 2.0)

Chapter Outline

Introduction

How does the human brain learn new information? This question has puzzled scientists for ages. The field of neuroscience is dedicated to finding out how the brain functions, but there are still so many things that we do not know. One major advance was the discovery of how neurons link up with one another inside the brain to make vast networks. Unlike manufactured electronic circuits that either send electronic signals or not, neurons in the brain fire at various strengths. The receiving neurons then transmit information by converting stimuli into electrical impulses, thereby deciding how important the incoming signals are and sending out signals in turn. It is thought that information is carried by the pattern of impulses in time over many groups of neurons. The net result is a complex “machine” whose function relies on trillions of connections, each with the ability to send signals along a continuum of intensity and with the capability of changing its structure when new information is encountered! This chapter will delve into the topics of deep learning and artificial intelligence.

Artificial intelligence (AI) is a branch of computer science and data science that aims to create intelligent systems capable of simulating humanlike cognitive abilities, including learning, reasoning, perception, and decision-making. Neural networks lie at the heart of many sophisticated AI systems, using models such as deep learning (covered in Introduction to Deep Learning). Neural networks and related AI algorithms use this inspiration to solve real-world problems, including image recognition and language generation.

In the early days of artificial intelligence research, efforts were significantly hindered by limited computational power. For example, in the 1950s and 1960s, early AI programs like the General Problem Solver (GPS) and the Logic Theorist could only solve relatively simple problems due to the constraints of the hardware available at the time. These programs ran on machines that had limited memory and processing speed, which meant that even basic tasks could take an inordinate amount of time to complete. The inability to process large datasets or perform complex calculations in a reasonable time frame greatly restricted the potential of AI.

Fast forward to the present—advancements in computational power and technology have dramatically transformed the landscape of AI. One prominent example of this transformation is the development of deep learning models, such as those used in natural language processing (NLP). Modern AI systems, like ChatGPT developed by OpenAI, are powered by state-of-the-art hardware, which enables the training of large neural networks on vast datasets.

We discuss many of these applications of neural networks and AI as well as some of the issues surrounding the ethics of artificial intelligence and machine learning, especially regarding fair use of artistic materials.

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