Non-fungible tokens (NFTs) are becoming more popular by the day. According to DappRadar, the trade volume of NFTs in 2021 was $24.9 billion–over $95 million more than in 2020.
One of the most significant developments in the cryptocurrency ecosystem is the rise of non-fungible tokens. The initial generation of NFTs concentrates on developing the fundamental components of the NFT market’s infrastructure, including ownership representation, transfer, and automation.
Even the most basic kind of NFTs capture great value, but the hype in the industry makes it difficult to tell the difference between signal and noise. As the market develops, the value of NFTs should shift from static photos or text to more intelligent and dynamic collectables. The upcoming wave of NFTs is going to be heavily impacted by artificial intelligence (AI).
We need to know what AI disciplines cross with the current generation of NFTs to comprehend how intelligent NFTs can be created. NFTs are represented virtually using digital media, including photos, videos, text, and audio. These representations translate to several AI sub-disciplines amazingly well.
The “deep learning” branch of AI uses deep neural networks to generalize information from datasets. The concepts underpinning deep learning have been known since the 1970s. Still, in the last ten years, they have experienced a new boom thanks to various platforms and frameworks that have accelerated its widespread use. Deep learning can significantly impact a few critical areas, enabling the intelligence of NFTs.
NFTs are mostly about pictures and videos nowadays, making them the ideal platforms for utilizing the latest developments in computer vision. Convolutional neural networks (CNN), generative adversarial neural networks (GAN), and transformers are approaches that have advanced computer vision in recent years.
The next wave of NFT technologies can use image production, object identification, and scene understanding, amongst other computer vision techniques. It appears obvious to integrate computer vision with NFTs in the field of generative art.
James Allison, a Nobel Prize-winning cancer researcher, was the subject of an NFT that the University of California, Berkeley auctioned off on June 8 for more than US$50,000. Designers scanned faxes, handwritten notes, and legal documents related to Allison’s important findings filed with the university. Everyone may view this piece of art, titled The Fourth Pillar, online, and the team created an NFT to prove ownership.
Language is the primary means through which cognition, including forms of ownership, may be expressed. Over the past ten years, some of the most significant advances in deep learning have been in natural language understanding (NLU).
In NLU, methods like transformer powering models, or GPT-3s, have achieved new milestones. New versions of NFTs could benefit from research in fields like sentiment analysis, question answering, and summarization. The concept of adding language comprehension to NFTs in their current forms feels like a simple way to improve their usability and engagement.
For instance, Eponym, a program that enables the translation of words into art and the direct development of NFTs, was recently released by Art AI.
Speech intelligence is the third branch of deep learning that can immediately affect NFTs. The field of voice intelligence has recently evolved because of techniques like CNNs and Recurrent Neural Networks (RNNs). Attractive NFT designs may be powered by features like voice recognition or tone analysis. It should be no surprise that audio-NFTs appear to be the ideal application for speech intelligence techniques.
NFTs need voice AI because it enables people to connect with their digital collectables naturally. Voice AI, for instance, may be used to query an NFT or issue commands to it. In the future, NFTs will be even more important since they are now more dynamic and engaging. Platforms such as Enjin allow users to create music industry NFTs, which could be game-changing.
The potential of NFTs is increased by language, vision, and voice intelligence improvements. The value unleashed at the point when AI and NFTs converge will influence several aspects of the NFT ecosystem. Three essential categories in the current NFT environment may be immediately reinvented by introducing AI capabilities.
This aspect of the NFT ecosystem stands to gain the most from recent developments in AI technology. The experience for NFT creators may be enhanced to heights we haven’t seen before by utilizing deep learning techniques in areas like computer vision, language, and voice. Today, we can see this tendency in fields like generative art, but they are still very limited in terms of the AI techniques they employ and the use cases they address.
We should soon observe the usefulness of AI-generated NFTs to spread beyond generative art into other general NFT utility categories.
Digital artists like Refik Anadol, who are experimenting with cutting-edge deep learning techniques to develop NFTs, illustrate this value proposition. To produce astounding graphics, Anadol’s company trained models utilizing hundreds of millions of photos and audio snippets using techniques like GANs and quantum computing.
Even if we can create NFTs using AI, they won’t necessarily be clever. But imagine if they were? Another commercial opportunity presented by the convergence of these two exciting technological phenomena is the native integration of AI capabilities into NFT. Imagine NFTs with language and speech skills that can interact with a specific environment, engage in a conversation with people, or respond to queries regarding their meaning. Here, platforms like Alethea AIand Fetch.ai are beginning to make headway.
Building blocks like NFT markets, oracles, or NFT data platforms incorporating AI capabilities can provide the groundwork for gradually enabling intelligence across the whole ecosystem of NFTs. Consider NFT markets that utilize computer vision techniques to give consumers intelligent suggestions or NFT data APIs or oracles that provide intelligent indications from on-chain statistics. The market for NFTs will increasingly depend on data and intelligence APIs.
AI is reshaping nearly every industry. By combining with AI, NFTs can go from simple, rudimentary forms of ownership to intelligent, self-evolving versions that allow for richer digital experiences and much greater forms of value for NFT creators and users.
Smart NFT technology does not require any far-fetched technological innovation. The flexibility of NFT technologies combined with recent developments in computer vision, natural language comprehension, and voice analysis already provide an excellent environment for launching new innovations in the ever-growing digital asset space.
Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.
The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.
The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com.
The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.
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