We're in the middle of an AI Revolution, and I think I can fairly compare it to the early days of the internet or the beginning of the mobile phone industry. The world is changing fundamentally and irreversibly. And the way we work and live is tremendously impacted, whether we want it or not. Why now, when AI has been around for so long? Well, it probably has to do with the impressive popularity and spectacular adoption rates
of recently launched AI tools. What this adoption story tells us is that people are extremely curious, interested and open to this new world of tools and capabilities; interest that's most probably stemming from a mix of enthusiasm, fear, reluctance and excitement. And most of these tools are based on *generative AI*. So, what is generative AI? We know that machine learning is a subset of AI, deep learning is a subset of machine learning,
and – you guessed it – generative AI is a subset of deep learning. To define it, putting it simply, generative AI is a type of artificial intelligence that creates new content based on what it has learned from existing content. Its main applications are in generating text, images or other media in response to prompts. How it works is that the model gets trained on data by applying machine learning techniques and is then able to create something entirely new, but with similar characteristics.
The main applications of generative AI are *language models* and *image models*. Language models learn about patterns in language through training data and then, given some text, they predict what comes next. Using text, these tools can generate new text, translations, summarizations, question answering, grammar correction, can generate images, videos, audio, speech
or decisions, tasks, play games. *Image models* produce new images using techniques such as *diffusion*, which works by destroying training data, by adding noise, and then learning to recover the data by reversing this noising process. Then, given a prompt, they transform random noise into images. By inputting images in generative AI tools, you can get text, image captioning, image search, visual question answering; *images*, image completion, super resolution, video, like animations.
The generative AI application landscape is already pretty vast, and we'll see more use cases and scenarios that these tools will address as technology and our understanding of it advance. Narrowing our scope to the design industry, some of the applications that help us most right now are *image generation* visual, mockups, icons, product illustrations, photography; *text generation* – UX copy, product descriptions, emails, documentation,
data augmentation. In cases where we have scarce research data, AI can help create synthetic data to supplement the real data. Not ideal – real data is the best, though. *Chatbots* for customer experience, and so on. The most prominent tools in generative AI are ChatGPT, Bard, Microsoft's Copilot, Midjourney, DALL-e, Stable Diffusion, to name a few. The landscape of tools is ever-changing with new tools emerging every day
and others being sunset after a short-lived life. The speed at which the market is transforming makes it hard to keep up with all the tool opportunities we can leverage in our work, but some of them are stable enough to be a safe bet for sticking around for a while; Bard, GPT, Copilot, probably here to stay. And I'm excited to see how this prediction ages.