Do you have an image idea in mind and propose to use one of these trendy Generative Artificial Intelligences to create it? Whether it’s MidJourney or DALL·E 3, they all promise to generate your idea within seconds. You input a phrase, and voila! Before your eyes appears a spectacular image, supposedly entirely original and tailored to your request. Or is it?
The Rise of Generative AI
Since the arrival of DALL·E 2 in April 2022, the debate surrounding Generative AI has been fueled by criticisms from design and art professionals. They accuse tech giants of using their published works without permission for massive generative model training. These models now compete in their own market, raising legal questions about whether these companies are offering identical plagiarized content without proper attribution.
Today, I aim to answer this critical question: should we consider Generative AI a source of plagiarism? To answer, we must first define generative idea. When we speak of Generative AI, you might intuitively think of text, images, audio, video, or 3D generation—modalities where AI excels in creating content.
Let’s start by understanding the concept of learning. Many believe AI algorithms memorize data, selecting interesting parts to regurgitate as patterns. This misconception is illustrated in infographics portraying machines as mere copycats. However, Generative AI operates differently. It’s about learning probability distributions, not memorizing data.
Imagine you’re fixated on a bear’s weight. Since bears vary in weight, answering this question requires real-world data. We collect measurements, forming a dataset. Now, analyzing this data, we grasp the probability distribution—indicating the likelihood of finding a bear with certain characteristics.
The crux here is understanding the concept of probability distribution and transitioning from raw data to its probability distribution. This is key—Generative AI learns these distributions, not the data itself.
Modeling Images with Generative AI
Images pose a greater challenge, as they contain billions of pixels, each with RGB color intensities. Learning their distribution means modeling millions of numbers in high-dimensional spaces. Simplifying this, imagine a world with 2×2 grayscale images. Each pixel ranges from 0 to 1, representing shades between black and white. We divide pixels into blue and pink groups, simplifying our variables for visualization.
Similar to the bear example, we analyze data to learn probability distributions. From these distributions, we can generate new data points—images—similar to those in our dataset but not identical. Generative AI learns to create new data based on learned distributions, not specific data points.
Addressing Plagiarism Concerns
Generative AI’s task isn’t random content generation but learning data distributions. However, two problems arise, leading to potential plagiarism: overrepresentation and specific prompts.
Generative AI doesn’t generate random content. Users often guide it with specific prompts, conditioning its output. This can lead to situations where the model produces near-identical copies of overrepresented data from the original dataset.
Additionally, when prompts become highly specific, such as requesting an image identical to a well-known artwork, the model might reproduce it, leading to accusations of plagiarism.
Ultimately, Generative AI’s potential for creating entirely novel content is vast. However, ethical and legal concerns persist, especially regarding copyright infringement. As society grapples with these issues, debates and legal frameworks will shape the future of Generative AI.
In conclusion, Generative AI represents a powerful yet nascent technology. Understanding its nuances is essential as we navigate its ethical and legal implications in the digital age.
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