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- Herausgeber: epubli
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
- Veröffentlichungsjahr: 2023
In today's world, where Artificial Intelligence (AI) is an indispensable part of our daily lives, "Building a side hustle: The AI Path to Financial Freedom" opens up innovative paths to economic success. This book is a comprehensive guide that walks you through the process of making AI technologies work for you. It provides practical tips on how to boost your income with innovative AI applications and achieve long-term financial security. From creating automated e-books and AI-supported investment strategies to efficient social media management, the book explores various ways to generate income with AI on the side. Readers will gain insights into the diverse applications of AI and learn how to use these technologies to tap into new sources of income. No prior knowledge in programming or complex technical areas is needed. With practical examples, easy-to-understand explanations, and concrete application guides, this book is your navigator into the world of AI-based income streams. Building a side hustle: The AI Path to Financial Freedom" is not just a book – it is a guidepost to a future where financial freedom is not a utopia but a realistic option for anyone willing to seize the opportunities of AI.
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Disclaimer
The authors and publishers of this book accept no liability for any damage or loss that may arise from the use of the information and examples in this book. Each user is responsible for ensuring compliance with copyright, citation rules and other legal requirements.
It is the responsibility of each individual to familiarize themselves with the respective rules and regulations of their university, college or school and to clarify which procedures are legally permissible and which are considered plagiarism. The contents of this book are for informational and educational purposes only and should in no way be construed as direct recommendations for writing academic texts. The examples shown are merely intended to offer suggestions and illustrate the various aspects of academic writing with ChatGPT.
The authors and publishers assume no liability for the accuracy, completeness and timeliness of the information and examples contained in this book. It is the responsibility of each user to carry out additional research and to critically scrutinize the information presented in this book.
I
What is Artificial Intelligence?5
Weak AI (Narrow AI)8
Strong AI (General AI)9
Super intelligent AI (Super AI)9
History12
Early beginnings12
1950s: The hour of birth12
1960s and 1970s: Initial successes and setbacks12
1990s: The Internet and more data13
Today: AI in everyday life13
ChatGPT14
GPT14
Excursus: Technical insight into the Transformer architecture15
History and development of ChatGPT17
CodeInterpreter (Advanced Data Analysis)26
Plugins:27
Excursus: Instructions for installing ChatGPT plugins:27
Instructions for activating the plugins/code interpreter29
How plugins work:33
Custom Instructions:35
Areas of application of ChatGPT at pioneering companies38
Possible uses of ChatGPT for you!41
Top up your salary41
Guidelines and specific examples48
1. become an e-book author48
The actual writing process54
Excursus: Plugin stories56
Revision and proofreading61
Publication and marketing63
Summary: Earning money with books65
2. assistance with investment advice67
Excursus: The VoxScript plugin70
Summary: Earning money with investments74
3 Media design76
Logos78
Excursus: DALLE-3 integration80
Videos85
Tips89
Summary: Earning money with media design91
4 Fitness trainers92
Step-by-step instructions94
Tips95
Excursus: Plugin GO98
Summary: Earning money as a fitness trainer104
5 Social media management105
Integration of ChatGPT107
Tips108
Summary: Earning money with social media management111
Summary and outlook112
Our thanks for your trust115
Image sources117
Imprint118
What is Artificial Intelligence?
Artificial Intelligence is not just a buzzword you hear in the news; it is a revolutionary technology that has already changed our lives in many areas and will continue to do so. From medicine to mobility, from communication to entertainment, AI has the potential to fundamentally improve our world. But what exactly is AI, and why are there so many different types of it?
Artificial Intelligence is an area of computer science that deals with developing machines or software so that they can perform tasks that normally require human intelligence. This includes things like speech recognition, decision making, visual perception and even creative activities such as composing music.
One of the fundamental characteristics of Artificial Intelligence is its ability to learn. Similar to a student who gets better and better at a subject through constant practice, an AI system can also learn from experience or data and thus continuously improve. Imagine you have an AI-controlled lawnmower that initially has difficulty reaching all corners of your garden. Over time, however, it learns from its mistakes and adjusts its route so that it can mow the lawn more efficiently.
This brings us to the next point, adaptability. AI systems are not just programmed to perform a single task; they have the ability to adapt to new or unexpected situations. If we stick with the lawnmower example, imagine planting a new tree in your garden. An adaptive AI system would notice this change and adjust its route accordingly without you having to intervene manually.
Last but not least, autonomy. Some advanced AI systems are able to make decisions independently without the need for human intervention. This is particularly useful for complex tasks where human intervention could be inefficient or even dangerous. An example of this would be an AI-controlled car that is able to react to sudden obstacles such as a fallen tree and make a decision on its own to avoid an accident.
Now, to better understand the concept and scope of Artificial Intelligence, it can be helpful to look at some famous quotes that shed light on this fascinating and sometimes unsettling field.
Stephen Hawking:
Elon Musk (Tesla, SpaceX):
Larry Page (co-founder of Google):
Alan Kay (computer scientist and pioneer of object-oriented programming):
Claude Shannon (mathematician, founder of information theory):
Ray Kurzweil (author, computer scientist and futurist):
Ginni Rometty (former CEO of IBM):
Nick Bilton (technology columnist):
Sebastian Thrun (computer scientist and expert in robotic learning):
These quotes reflect the diverse perspectives and potential that Artificial Intelligence brings, both positive and negative. They also emphasize the ethical and moral considerations that come with the development and application of AI. They help to gain perspective on the profound impact that AI can have on our society and our lives.
Now that we have developed a basic understanding of what Artificial Intelligence actually is - namely the imitation of human intelligence by machines - it makes sense to delve deeper into this fascinating topic. You'll be surprised at how diverse and complex the world of AI can be. It's not just a uniform block of technology, but a dynamic field that encompasses many different approaches and methods. Just as there are different professions and talents among humans, there are also different "specialties" in AI. To better understand the full range and fascinating possibilities of AI, let's take a closer look at the different types of Artificial Intelligence.
In research, the different types of Artificial Intelligence are often categorized according to their capabilities. There are usually three main categories: weak, strong and super-intelligent AI:
Weak AI (Narrow AI)
What is this? Weak AI is specialized in a specific task and can only act in this specific area. It has no general intelligence or consciousness.
Think of weak AI as a talented waiter in a restaurant. The waiter is excellent at taking orders, serving the right dishes and making sure the customers are happy. But if you put this waiter in a garage, he would be completely out of his depth. His expertise and skills are specialized and limited.
Examples:
Strong AI (General AI)
What is that?
Strong AI is a theoretical concept of a machine that has the ability to perform any intellectual task that a human can perform. It would have its own consciousness, emotions and the ability to learn and think independently.
Strong AI would be like an all-rounder that could work as a waiter, repair a car or write a book. It could learn new skills and adapt to different situations, almost like a human.
Examples:
Super intelligent AI (Super AI)
What is that?
The concept of superintelligent AI goes one step further than strong AI. While strong AI aims to emulate human intelligence in various areas and abilities, superintelligent AI is intended to surpass it. This means that it would be able to perform tasks and solve problems that are unimaginably complex for humans. It could think faster, have access to a huge pool of data and could theoretically be better than humans in every respect, be it in scientific research, art or social understanding.
Imagine the super-intelligent AI as a scientist who has not only read all the books, but also has the ability to generate new knowledge in a matter of seconds. This scientist would be able to solve complex equations in their head, find the cause of previously incurable diseases and even solve social or political problems that have plagued humanity for centuries.
There are no concrete examples of this yet, as the concept is only intended to provide a broad view of the future.
Now that we understand the different types of Artificial Intelligence - from weak and strong AI to the fascinating idea of superintelligent AI - it's time to dive a little deeper into the technologies that bring these concepts to life. It's important to emphasize that while the terms 'Artificial Intelligence', 'machine learning' and 'deep learning' are often used interchangeably, they each cover specific aspects of this complex field. So let's clarify how they differ from each other and how they relate to each other.
Artificial Intelligence is the generic term for the development of computer technologies that can perform tasks that normally require human intelligence. This encompasses a wide range of capabilities, as we have just seen with the characteristics of learning ability, adaptability and autonomy.
Machine learning is a sub-area of AI and could be regarded as its "learning department". It specifically deals with the development of algorithms and models that enable computers to learn from data. If you use an email application that recognizes and sorts out spam messages, it usually does this through machine learning. The application has learned from millions of emails which characteristics classify an email as spam.
Deep learning is in turn a sub-area of machine learning. It could be seen as the specialized unit for complicated learning tasks. It attempts to imitate the human brain by using neural networks that are able to recognize very complex patterns in large amounts of data. An example of this would be facial recognition in photos. A deep learning model can learn from a large number of faces and then recognize a specific face in a new photo, even if the person is wearing glasses or has changed their hairstyle.
To really make the connections clear: imagine AI as a car company. Machine learning is the department that specializes in building particularly efficient engines. Deep learning would then be the team within this department that works on a special, very powerful engine that runs optimally under various extreme conditions.
History
Early beginnings
The history of Artificial Intelligence is a fascinating kaleidoscope of theory, practical application and ever-emerging visions. It does not begin, as one might assume, in the age of computers, but can be traced back as far as the ancient Greeks. Even then, there were myths of artificially created creatures such as Talos, the bronze giant, or the mechanical servants of Hephaestus. But although these stories date back to a time when AI was still pure fantasy, they lay the foundations for humanity's dream of creating machines that can think.
1950s: The hour of birth
Modern AI research began in the 1950s. An important milestone was the year 1956, when the term "Artificial Intelligence" was used for the first time at a conference at Dartmouth College in the USA. Researchers such as Alan Turing, who had already laid the foundations for computers in the 1940s, were pioneers in this field.
AI research really took off in the modern era, especially after the Second World War when the first computers were developed. One of the pioneers of this field was Alan Turing, a British mathematician and computer scientist. With the Turing test, he posed the question of whether a machine could think in such a way that it could no longer be distinguished from a human being. At the time, no one could have imagined how far we have come in AI research today, but Turing laid the foundations for what was to follow.
1960s and 1970s: First successes and setbacks
The late 20th century was characterized by a mixture of progress and setbacks. In the 1960s and 1970s, there were great waves of optimism. Researchers such as Marvin Minsky and John McCarthy, who coined the term "Artificial Intelligence", were convinced that machines would soon achieve human intelligence. This phase was later often referred to as the "AI spring". However, the hoped-for breakthrough failed to materialize. The machines were not able to even come close to the complexity of human thought, and research came up against technical and financial limits. This led to phases of the "AI winter", during which enthusiasm waned and research funding flowed more sparingly.
1990s: The Internet and more data
In the 1990s, however, AI experienced a renaissance, mainly due to advances in the field of machine learning and data analysis. Computers became more powerful and the internet became a global information superhighway. The combination of improved hardware and huge amounts of data made it possible to train algorithms that could perform complex tasks. Search engines such as Google, voice-controlled assistants such as Siri and autonomous vehicles became possible.
