What Happens When You Train an AI with 10,000 Memes

A video walkthrough by Coding with Lewis on training an AI with over 10,000 memes to generate funny captions, including data collection, model training, and challenges faced.


  • – AI trained with 10,000 memes to automatically generate captions for images.
  • Initial AI attempts at humor were largely unsuccessful and not funny.
  • Memes evolved from user-generated content to complex cultural expressions.
  • The project involved scraping meme templates and captions for training data.
  • Large language models fine-tuned with meme data to produce relevant captions.
  • Incorporating current events into memes for timely and relevant humor.
  • The challenge of making AI understand and generate humor in memes.
  • Using a multi-modal large language model for generating memes from images.
  • Fine-tuning AI models requires adjusting parameters for specific outcomes.
  • The difficulty of explaining why something is funny complicates AI humor.
  • Six humor types identified for training AI: unexpected, exaggeration, absurdity, wordplay, juxtaposition, incongruity.
  • The context of memes plays a significant role in their humor and virality.
  • Using current events as context for generating timely and funny memes.
  • Custom code developed to label objects within scenes for meme creation.
  • Challenges in identifying objects accurately with AI for meme generation.
  •  Streamlit used to create a user interface for the meme-generating application.
  •  The project’s open-source code allows others to experiment with AI-generated memes.
  • The developer reflects on the challenges and learning experiences from the project.
  • AI’s limitations in replicating human humor highlight the complexity of comedy.
  • The project demonstrates the potential and challenges of creative AI applications.

Insights Learned:

– Training AI to understand humor reveals the nuanced nature of comedy.
– The evolution of memes mirrors the development of internet culture and technology.
– Fine-tuning AI models for specific tasks like humor generation is both art and science.
– The intersection of current events and memes showcases the dynamic nature of humor.
– Humor’s subjective nature makes it a challenging domain for artificial intelligence.
– The project highlights the iterative process of developing and refining AI applications.
– AI-generated humor’s effectiveness is heavily influenced by cultural and social contexts.
– The use of multi-modal models underscores the complexity of understanding visual humor.
– The project’s challenges underscore the gap between AI capabilities and human creativity.
– Open-source contributions to AI projects foster community engagement and innovation.


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