Although the full concept of the NFT market seems to escape us, the exponential growth of this market – peaking at $11.6 billion in 2021 – has brought widespread attention to the subject. This, in turn, has opened the floodgates for new innovation and technology to power the creation of these virtual works of art.
One form of innovation includes AI-driven and machine learning technologies that use data attributes from existing NFTs listed on publicly accessible websites to randomly generate thousands of new, unique NFTs a second that could pass off as originals. Experts are projecting that we will see the emergence of these AI-driven NFTs this year. This development could be a positive step in the direction of inclusivity but could also have some negative effects on the valuation of the current NFT population.
An example of one of these creative innovators is Yannic Kilcher, a machine learning researcher and engineer. He developed a method for generating infinite amounts of Bored Ape NFTs by training his own AI using readily available public web data. Kilcher has a YouTube channel with 133K subscribers, where he makes “videos about machine learning research papers, programming, and issues of the AI community, and the broader impact of AI in society.” Through his Bored Ape project, Kilcher wanted to show that anyone could create their own one-of-a-kind Bored Ape NFT, free of charge.
For background, Bored Apes are a popular collection of 10,000 unique NFTs created by the Bored Ape Yacht Club. Each Bored Ape is “programmatically generated” from a selection of 170 traits, with some being rarer than others, ranging in price from 60 Ethereum to 100 trillion Ethereum. The latter amount, at the moment, would make just under a $197-quadrillion dent in your pocket. How and why the prices of NFTs are set has little to do with AI and data and is a topic for another discussion. However, the recent incremental increase in the price of Bored Ape NFTs has been driven up by celebrities, like Jimmy Fallon, Justin Bieber, Paris Hilton, and others, who have recently purchased Bored Apes from the collection.
Kilcher described the process of creating the AI model in a joint video he produced on his YouTube channel with Bright Data. Among other methods, he used public web data collection and open-sourced code and materials found on GitHub to power and design the AI for public use. To feed the Bored Ape AI model, Kilcher collected web data from opensea.io, which houses the official collection of Bored Ape NFTs for sale on its website. He used web data collection tools to automate the collection of images, attributes, prices, when the NFTs were last downloaded or sold, and other information from all 10,000 of the listed Bored Ape NFTs available on the website.
Kilcher used this data and some open-sourced code from Nvidia to train his model to create Bored Ape tokens. He did so by applying a generative adversarial network (GAN) machine learning framework, which is effectively a complex set of checks and balances to create unique images. According to Kilcher, data is crucial in building GAN frameworks. They automatically discover and learn from the patterns in the input data collected from the original NFTs – enough to where they eventually possess the ability to generate new NFTs that could plausibly appear as part of the original collection.
GAN frameworks fundamentally train two models at the same time, one being the generator and the other being the discriminator. The generator’s role is to attempt to fool the discriminator by introducing new data into the images outside the originally collected data set. Meanwhile, the discriminator’s job is to determine the authenticity of the image by estimating the probability that the image came from “training data” rather than original data from the generative model. Through this process, the generator and the discriminator are inevitably improved and end up balancing each other out over time. Eventually, a properly trained model will have the ability to generate new images that can pass as legitimate, which Kilcher realized could be applied to create an infinite amount of NFTs.
Using the Nvidia StyleGAN2–ADA code based on the GAN framework, Kilcher trained his model step-by-step using the web data from original NFTs to feed the AI. Every couple hundred steps, the AI produced a new batch of images that allowed him to track its progress. The images were blurry at first, but eventually, the AI began learning and creating apes as it correlated the data to achieve its designed purpose. And as the model became more advanced, it generated more diverse apes.
While the exclusivity and artificial scarcity of NFTs is typically what drives up their cost, Kilcher decided to publish his AI-powered application for public use – offering up infinitely free Bored Apes to everyone. While this is bad news for Bored Ape collectors, it is great news for the development and advancement of new innovation.
Kilcher’s Bored Ape generator can be found on Hugging Face.
About the Author
Or Lenchner, CEO, Bright Data. Ever since his appointment as CEO of Bright Data (formerly Luminati Networks), Or Lenchner has continued to expand the company’s market base as an online data collection platform dedicated to delivering complete web transparency. For the past three years, under Lenchner’s leadership, the company has advanced its product offerings to include first-of-its-kind automated solutions, enabling its customers to collect and receive data in a matter of minutes. Among Bright Data’s thousands of customers are Fortune 500 companies, major e-commerce firms and sites, prominent finance firms, leading security operators, travel sites, academic and public sector organizations.
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