Generative AI covers a wide field, from image generation, video, audio, and more. But the most widely known example that received larger awareness among the wider population in recent years is, if we talk about conversational AI specifically, what you see Claude, ChatGPT and all other chat-based AI apps do when prompted. Based on the vast amount of text data these models were trained on, much of which is sourced from publicly available online content, up to a certain point in time, they generate the most statistically likely response and present it in the format and through the lens you asked for.

What was groundbreaking about the large language models in generative AI, and why it became suddenly so widely known, is how simple it became to interact with AI in everyday use. Imagine how wide suddenly the world can get for anyone with such a tool, especially if prompted simply by voice, in all 7,000+ languages of our globe. But we will let the engineers and governments work that out properly, since it does have significant environmental and other potentially negative impacts, unfortunately.

For now, thanks to automation and AI we can already think about online solo-entrepreneurship; and that is what we’re here to do. This rather technical topic is likely what a beginner has the biggest room to grow in understanding, and that’s normal. We do believe that by learning about automation in such a tangible manner, through online income techniques, you have the advantages of “learning by doing” and “doing for a tangible purpose,” which often stick in people’s memories better. And to be fair, you do not have to know the technical details of automation and AI to endeavor into an online activity. It is, however, always interesting to learn about.

The relationship between these 4 smaller areas of computer sciences — which encompasses so much more — we illustrated in Figure 4.

Frequently asked questions

What is generative AI?

AI that creates new output rather than just analysing existing data. This includes text, images, audio, video, code and more. The most widely known example is conversational AI, like Claude or ChatGPT.

How do chat-based AI tools actually generate answers?

They generate the most statistically likely response based on the vast amount of text data they were trained on, then present it in the format and through the lens you asked for.

Why did generative AI become so widely known?

Because interacting with AI became suddenly simple in everyday use, including by voice in over 7,000 languages. That shift made powerful tools accessible to anyone, not only engineers.

Are there downsides to generative AI?

Yes. It has significant environmental and social costs, and its outputs are not always reliable. Lynnaider mentions this to keep the picture honest, not to discourage use.

Do I need to understand AI technically to use it in my business?

No. You can use these tools well without knowing the technical details. Learning by doing through a specific online income technique tends to stick better than studying the theory in isolation.

How do automation, AI, Machine Learning and Deep Learning fit together?

Automation is the largest and oldest circle. AI overlaps with automation but is a separate field. Machine Learning is a subset of AI, and Deep Learning is a subset of Machine Learning. Figure 4 in the book maps them together.

Disclaimer: This article is for general informational purposes only and should not be regarded as legal, tax, or business advice. Pursuing an online business does not guarantee income; results depend on many factors including the business environment, individual effort, skills, and consistency. Some links on this site may allow Lynnaider to earn a commission at no additional cost to the reader.

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