For decades, artificial intelligence (AI) has been a laymen’s lingo.
Current technological breakthroughs and advancements have created a surge of interest in a specific type of AI known as generative AI. With its unprecedented ability to generate new and distinctive content that aids human creativity, generative AI revolves around analysis, automation and generation of content.
It is intriguing to learn how generative AI fits into the vernacular of all pragmatic applications. As per a BCG blog, the generative AI sector will gain an estimated 30% share of the whole AI market by 2025, which is equal to $60 billion of the total addressable AI market.
Harnessing the power of AI: Generative AI
Generative AI is a subset of machine learning that uses neural networks to generate new content. Unlike other AI systems programmed to perform specific tasks, Generative AI functions on large datasets and produces content that is new, unique and sometimes unpredictably informative.
One of the most popular types of generative AI is generative adversarial networks (GANs). GANs consist of two neural networks: a generator and a discriminator. The generator creates new content, and the discriminator evaluates whether the content is real or fake. These networks continuously learn from each other, improving the quality of the generated content over time.
Generative AI has the potential to transform how we utilize AI, from producing realistic synthetic data for training AI models, to curating tailored content for customers. The quality of the content produced by GANs has subsequently increased over time. Today, GANs produce pictures and videos that can be nearly indistinguishable from the originals.
For instance, to speed up and lower the cost of the design process, businesses like H&M and Nike have employed generative AI to produce new apparel designs. Designers can now display their collections in a virtual setting thanks to AI technology used to create virtual fashion shows. According to a 2022 McKinsey survey, usage of AI has nearly doubled over the last five years, and investment in AI is expanding rapidly. Generative AI tools like ChatGPT and DALL-E (a tool for AI-generated art) can alter a wide range of job roles.
Defining ChatGPT and DALL-E
Chat Generative Pre-trained Transformer (ChatGPT) is the latest robust innovation in the rapidly developing AI industry. It is an effective generative AI language model developed by OpenAI that can produce unique content in response to a user command. ChatGPT is based on the Reinforcement Learning from Human Feedback (RLHF) technique and runs on the GPT-3.5 language model (a model created using a large quantity of data collected from numerous sources) at the time of writing.
DALL-E, on the other hand, is an AI model developed by OpenAI, that utilizes a combination of advanced deep learning techniques, such as transformer networks and Generative Adversarial Networks (GANs), to generate images based on textual descriptions. This innovative technology can comprehend and interpret natural language inputs and produce unique visual representations accordingly.
Pragmatic instances of leveraging AI
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The application of ChatGPT and DALL-E in real-life scenarios has increased efficiency and creativity. Major companies like Microsoft and Google have incorporated ChatGPT into their customer support systems, providing customers with immediate assistance.
Furniture retailer IKEA has utilized AI to create 3D models of their products, allowing customers to preview furniture in their homes. Furthermore, the car manufacturer Lexus made use of AI to produce surreal car designs based on textual descriptions, demonstrating the technology’s ability to facilitate innovative design. This helps highlight the potential of generative AI technologies such as ChatGPT and DALL-E.
How is generative AI empowering the Web3 industry?
Generative AI is empowering Web3 via NFTs (such as with branding and media with NFT arts), blockchain gaming (such as with asset generation, narrative and story designs as well as avatar modeling creation), the metaverse (with 3D ecosystems, multiple assets and texture generation) and Web3 development (such as with code generation, audits debugging and workflow automation).
Different generative AI tools in Web3 intuitively innovate online search. For instance, ChatGPT’s latest integration with Microsoft’s Bing offers an enhanced and user-friendly chat interface. Furthermore, Generative AI fits into the Web3 domain through its AI cloud. It helps people filter data on the web and mitigates the complexities of SEO content while making a query on web search.
By implementing Generative AI text tools, you can streamline and innovate dynamic game elements like dialogues and avatars.
Generative AI also supports NFT art generation such as with CryptoPunks, Lost Poets, Ringers and Chromie Squiggle. The AI tools input a set of rules (such as color range and patterns) along with multiple iterations and levels of randomness to produce artwork within the stipulated framework.
What are the potential risks of generative AI in Web3 and how can you combat them?
Like every coin has two sides, generative AI too has some risks which you need to be careful of while leveraging the technology. These are some of the potential risks of generative AI in Web3:
- Intellectual property infringement and content copyright issues
- Quality and correctness of content generated via AI
- Architectural blockers in new blockchain runtime generation
- Privacy issues via content based on sensitive data
- Malicious implementation of generative AI
- Biased algorithm data outputs
You can combat these risks by:
- AI-based content moderation tools like Perspective API by Google or Two Hat’s Community Sift
- Data privacy-preserving techniques like federated learning, homomorphic encryption and anonymization
- Representative datasets to train the generative AI algorithms for credibility like ImageNet, MNIST
- AI-based fraud detection tools like Fraud.Net, Kount, NICE Actimize
- AI content analysis metrics like fairness and accountability metrics
- Chalk out standards and practices for use of generative AI in Web3
Generative AI’s automation powers the data computation aiding Web3 organizations to integrate machine learning into their operations. Consequently, individuals are voluntarily adopting forthcoming advancements in AI.
Generative AI is a revolutionary space wherein the leaders are innovating industries like fintech, climate tech, fantasy sports, digital gaming, interoperable trading, healthcare, art space and hospitality. It could potentially uplift the generative AI implementation in Web3 as well.
As AI technology evolves with time, we could expect a disruptive future in the Web3 industry.
Vinita Rathi is the Founder and Chief Executive Officer of Systango, specialising in Web3, Data and Blockchain.
This article was published through Cointelegraph Innovation Circle, a vetted organization of senior executives and experts in the blockchain technology industry who are building the future through the power of connections, collaboration and thought leadership. Opinions expressed do not necessarily reflect those of Cointelegraph.
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