Introduction Codenames is a word association game where two teams guess secret words based on one-word clues. The game involves a 25-word grid, with each team identifying their words while avoiding the opposing team’s words and the “assassin” word. I knew that word embeddings could be used to group words based on their semantic similarity. This seemed like a good way to cluster words on the board and generate clues. I was largely successful in getting this to work along with few surprises and learnings along the way.

Introduction By now, many of us may be familiar with text-to-image models like Midjourney, DALL·E 3, StableDiffusion etc., Recently, I came across an interesting project called Visual Anagrams that utilizes text-to-image model to generate picture illusions. This project enables us to input two different text prompts, and the model generates pictures that match the prompts under various transformations, such as flips, rotations, or pixel permutations. Growing up, I had a nerdy fascination with illusions and ambigrams, so I was thrilled to give this a try.

Introduction While chatbots have grown common in applications like customer service, they have several shortcomings which disrupts user experience. Traditional chatbots rely on pattern matching and database lookups, which are ineffective when a user’s question deviates from what was expected. Responses may feel impersonal and fail to address the true intent when questions deviate slightly from pattern matching rules. This is where large language models (LLMs) can provide value. LLMs are better equipped to handle out-of-scope questions due to their ability to understand context and previous exchanges.

Introduction When it comes to creating artwork, there are many Generative AI tools, but my favorite one is the vanilla Stable Diffusion. Since it is open source, an ecosystem of tools and techniques have sprouted around it. With it, you can train your own model, fine-tune existing models, or use countless other models trained and hosted by others. But one of my favorite use case is to render rough sketches into much prettier artwork.

In 2020, I wrote about GPT-3 model. Late last year, OpenAI released ChatGPT which was based on GPT-3 but trained using Reinforcement Learning from Human Feedback (RLHF). And now GPT-4 has been released. It has only been out for a few days, but it is already seeing incredible applications such as creating office documents, turning sketches into functional apps, creating personal tutors, and more. And not just GPT-based models, StableDiffusion and Dall-E are also pushing the boundaries of art, creating stunning visuals from mere textual descriptions.