Creative process: Machine learning vs Human

 Artificial intelligence (AI) has come a long way in recent years, and one area in which it has made significant strides is in the realm of image generation. AI image generators like DALL-E, Stable Difusion are able to create images from text prompts, and the results can be quite impressive. Although both machine learning and artists rely on images made before them, it's important to note that the creative process behind images constructed by image generators is quite different from that of an artist.

NighCafe Studio (25/01/2023)

Artists often look to the work of other artists for inspiration. They might study the techniques and styles of their predecessors and contemporaries, and use this knowledge to inform their own creative process. Artists might also look to the natural world, as well as their own experiences and emotions, for inspiration. The goal is to find new ways to express themselves and create something that is unique and original in light of the traditions.

In contrast, AI image generators like DALL-E use a different approach. They are trained on a vast dataset of images, and they use this data to learn patterns and relationships between different elements. When presented with a text prompt, the AI uses these patterns to create an image that best matches the prompt. The AI does not have the ability to look at an image and find inspiration in the way an artist would. Instead, it finds common denominators based on the text prompt and creates an image accordingly.

This difference in approach is reflected in the images produced by the two methods. Images generated by AI tend to be highly detailed, but they can also be somewhat formulaic. They often lack the sense of spontaneity and individuality that is often present in the work of an artist. In contrast, images created by an artist tend to be more expressive and unique, reflecting the artist's personal vision and creative process.

NightCafe Studio (25/01/2023)

It's worth noting that AI image generators can be a useful tool for artists, and can be used to create images that might not be possible with traditional techniques. For example, an artist might use AI to generate a complex pattern that they can then incorporate into their own work. Additionally, AI can be used to create variations on a theme, which can be a useful starting point for an artist to create their own unique work.

NightCafe Studio (25/01/2023)

In conclusion, while AI image generators like DALL-E can create impressive images, the creative process behind them is quite different from that of an artist “even if both processes rely on previous traditions”. Artists look for inspiration in the work of other artists, the natural world and their own experiences, and emotions, whereas AI image generators rely on patterns learned from a vast dataset of images. The resulting images are highly detailed and formulaic, lacking the sense of spontaneity and individuality that is often present in the work of an artist. However, AI can be a useful tool for artists to generate variations on a theme and create images that might not be possible with traditional techniques.

PS Similar to the previous post, I kept the words I wrote and those of the OpenAI chatbot separate when I was writing this one. As it is visible, in this post, my words are almost insignificantly few. This is true, but not because the chatbot has evolved between posts so quickly that it no longer needs my input. This post differs from the previous one in that I provided a more detailed prompt this time, outlining the genre, the required length, the claim, as well as the supporting evidence and applications. This raises even another issue, namely how to conceptually and visually separate the various voices and determine what is my work and what is that of OpenAIchat. It is very likely that in the forthcoming posts I will explore this problem in more detail.

ChatOpenAI, Copyright, Editorial work

In the previous post I used quotation marks to indicate the text has been generated by chat OpenAI. This practice seems to be somewhat disorienting, so I am going to use different colours for the two voices: my words will be in the standard green of this blog and those of chat OpenAI in purple. This distinction in voices is particularly crucial here, as in this blog post I am going to start meditating about copyright issues.

Nightcafe (19/01/2023)

When generating the text below, first I asked questions from OpenAI, requested clarifications, further explanations. Then I put the responses into a prompt before requesting OpenAI to create a blog post. The outcome wasn't terrible, but it wasn't perfect either, so I made OpenAI rewrite the post with a little shift in emphasis. Although the outcome was already better, there was still need for improvement, so I copy-pasted into it an answer to a previous question, rearranged the order of the paragraphs, united and divided paragraphs when necessary, and deleted sentences. This is what I do as an editor to journals, too, so I treated the texts as one written by a human being. And below is the result.

With AI, it is possible to generate high-quality content quickly and efficiently, making it an attractive tool for businesses and individuals alike. However, there are some concerns about copyright when it comes to AI-generated text. In this blog post, I will explore the importance of properly acknowledging sources and avoiding plagiarism when using AI for text generation.

First and foremost, it is important to ensure that the sources that the AI is using are properly acknowledged and cited. This means that any content that is generated by the AI should include proper attribution to the sources that were used. This can typically be done by including in-text citations and a list of references at the end of the generated content, just as you would with any other type of written work. It is also important to ensure that you have the necessary permissions to use the sources in your content, and to give credit to the original authors in a way that is consistent with copyright laws and best practices. This is important for two reasons: first, it ensures that the content is not plagiarized, and second, it allows readers to easily verify the accuracy and reliability of the information presented.

When, however, it comes to training the OpenAI model, it draws on patterns and structures that it learned from vast amounts of text data that were fed into it during its training process. This data can include a wide range of text, such as news articles, books, websites, and other types of written content. The model processes this data and transforms it into a numerical representation that it can use to generate responses. However, it's important to remember that because the AI model does not have access to the specific texts that were used to train it, it is not possible for the model to identify specific texts or sources that were used in its training process. That's why it is a good idea to perform a thorough check of your content before publishing it to ensure that it does not contain any potentially infringing material. This can include running your content through a plagiarism checker or reviewing it manually to ensure that all sources are properly cited and acknowledged.

Nightcafe (19/01/2023)

In addition to acknowledging the sources that the AI uses, it is also important to acknowledge the work of the AI itself. This can be done by including a disclaimer or statement in the content that is generated, indicating that it was produced using AI. This ensures that the work of the AI is recognized and credited, and helps to avoid any potential confusion about the authorship of the content.

In conclusion, AI can be a powerful and efficient tool for text generation, but it is important to navigate copyright issues in a responsible and ethical manner. This includes properly acknowledging the sources that the AI uses, acknowledging the work of the AI itself and avoiding plagiarism. By following these best practices, we can ensure that the benefits of AI are realized while also respecting the rights of others.”

It should also be emphasized though that it is rather challenging to accurately credit OpenAI’s efforts because I was unable to colour-code the editorial efforts and prompts that went into the creation of this particular text. Therefore, in terms of execution of the task and drawing of conclusions, this article may be considered a failure in and of itself. Even if this is the case, I hope it has at least provided some food for thought. If you have suggestions for how to properly attribute OpenAI’s work, please leave them in the comment area below. You may also do this if you think there is something in the text that needs more explanation.

Artificial Intelligence and Academic Work – introductory thoughts

 “As someone who is interested in the intersection of artificial intelligence and academia, I have been closely following the development of AI text and image generation tools and the ways in which they are beginning to influence the way we work and create” in academia. And also as 2023  will be clearly about AI. “In this series of blog posts, I want to delve into the potential impacts these tools may have on academia, specifically in terms of academic work and workflow.” What is going to be, however, more interesting is that I am going to give prompts to these tools about what they should write and what images they should generate, and this way we’ll also see how effective the tools are. This post has been written mostly by OpenAI chatGPT, and the images have been generated exclusively by NighCafe Studio. To distinguish the different voices, I'll put the words that Open AI chatGPT generated between quotation marks and my own lines without them.

NightCafe Studio (07/01/2023)

“What are AI text and image generation tools?

AI text generation refers to the use of artificial intelligence to produce written content. These tools utilize machine learning algorithms to analyse and understand language patterns, allowing them to generate coherent and often convincing text on a variety of topics. This can be something as simple as a social media post or as complex as a research paper.

AI image generation involves the use of AI to create original images or manipulate existing ones. This can range from simple tasks like adding a watermark to a photo to more complex tasks like generating entirely new images based on a set of specified characteristics.

How are these tools being used in academia?

One area where I have seen AI text generation being utilized in academia is through the use of automated abstract and summary writing “and also in higher education potentially by students to generate home assignments. These tools can analyse a research paper and produce a concise summary of its key points, saving researchers time and effort in the process.” Furthermore, as I teach at a university in Hungary, I know students will start using it for their written assignments. Instead of being afraid of this, I will teach them how to use it, and show that even to be able to appreciate it we have to learn much together. As I teach essay writing, I will introduce this to my students. Also I will thematise its use, its fair use.

“AI image generation is also being used in academia, particularly in fields such as biology and medicine. For example, AI can be used to create realistic 3D models of biological structures, which can be useful for education and research purposes.” Also for the sake of creating illustrations, one may well use these tools. And also, I will use the images as a model to chart cultural, visual biases, as the images are related closely to the corpus that machine has been trained on. For example, it is interesting to explore the image of this post: the colour scheme, the human hand (why a man and not a woman – the prompt was gender neutral), why a wooden desk, no ring on the fingers, why a pullover and not a shirt, why a laptop and not a PC, the perspective?

“What are the potential impacts of these tools on academic work and workflow?

As someone who has seen first hand the demands placed on academics to publish research and teach, I can see the potential for AI text and image generation tools to increase efficiency and productivity. By automating certain tasks, researchers can save time and focus on more important and complex aspects of their work.

Another potential impact is the democratization of information. AI text generation tools have the potential to make research more accessible by generating summaries and abstracts that can be easily understood by a wider audience. Similarly, AI image generation tools can help to make scientific concepts more visual and easier to understand for a wider audience.

However, it's important to note that these tools also have the potential to be misused. For example, there have been instances of AI-generated text being used to spread misinformation or propaganda.” And it is also possible that papers, assignments will be written with the help of AI. “In order to mitigate these risks, it will be important for researchers and university educators to establish guidelines for the ethical use of these tools.”

“Conclusion:

As AI text and image generation tools continue to develop and improve, it's crucial that we consider their potential impacts on academia. While these tools have the potential to increase efficiency and productivity, as well as democratize information, it's important to also consider the potential risks and establish guidelines for their ethical use. In this series of blog posts, I plan to explore these topics in greater depth and examine the ways in which AI text and image generation tools are shaping the future of academia.” If interested in these cooperative meditations, please, read and maybe comment on the posts!