AI UX 3. What Is Generative AI?

• Generative AI is a subset of deep learning that creates new content based on learned data. It operates by training on existing data to produce new, similar outputs.
• It is focused on generating new, unique content, including images, text, music, and more, based on data it has been trained on.
• Primarily uses neural networks, inspired by the human brain, which consist of interconnected nodes that process and analyze information.

Primary Applications:

• Language Models: These models predict and generate text based on patterns learned from training data. Capabilities include writing, translation, summarization, question answering, and grammar correction.
• Image Models: Use techniques like diffusion to generate new images. This involves adding noise to training data and learning to reverse this process to produce clear images.

Industry Influence

• The adoption of generative AI is likened to the early days of the internet or the mobile phone revolution, indicating a fundamental and irreversible change in how we work and live.

Applications in Design

• Visual Content Creation: visual mock-ups, icons, product illustrations, and photography
• Text Generation: UX copy, product descriptions, emails, and documentation
• Data Augmentation: In scenarios with limited research data, generative AI can create synthetic data to supplement real data.

Current Landscape and Future Outlook

• Prominent Tools: include ChatGPT, Bard, Microsoft's Copilot, Midjourney, DALL-e, and Stable Diffusion.
• Market Dynamics: The generative AI tool landscape is rapidly evolving, with new tools emerging and others phasing out quickly. Some tools are expected to remain stable and continue influencing the industry.
• Potential for Growth: As technology and understanding of generative AI advance, more applications and use cases are expected to develop.