šŸ¤” A moment to consider Toys 'R' Us

And more on GenAi for visualization

ā€œNostalgia is a seductive liar.ā€ - George Ball

In today’s newsletter:

  • A moment to consider Toys 'R' Us

  • Paper

  • Learn

Pense

AI isn’t creative. It’s an engine to mimic the creativity of others at scale.

After Toys ā€˜R’ Us’ defeat at the hands of Amazon, the brand is surprisingly still kicking. It just launched an AI ad that it made with Open AI’s Sora hoping to drive up some buzz.

It certainly did that but there’s many questions around AI’s potential in the marketing industry. And not everyone is excited about it. Many think it’s trash. It’s not the best ad but that’s a concept issue not the AI’s fault. Those who know it’s not an AI issue are concerned with the employment outlook of the marketing industry.

Here is a classic Toys ā€˜R’ Us ad from the 1990s:

The new ad the new crew made with AI:

There’s no comparison in terms of creativity. The first 1 wins. The 2nd reminds me of sad puppy ads which I guess the new Toys ā€˜R’ us is.

Creativity can’t be faked. AI’s role was never to be creative. AI’s role is to reduce the cost of production across a large swath of the visual arts space through replicating existing materials in a slightly changed way.

Jobs will be eliminated. But competition will increase since the cost of creation will be driven down. The behemoths at the top of industry just may tumble over the next 50 years. Kind like Toys ā€˜R’ us did.

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Paper

Generative AI for Visualization: State of the Art and Future Directions

  • Data inference with GenAI enables the interpolation and augmentation of data for visualization tasks, improving data quality and completeness

  • The use of sequence generation methods like RNN and NL2VIS Transformer allows for the automatic generation of visualization code from input data, simplifying the visualization creation process

  • Spatial generation techniques such as VAE and GAN facilitate the generation of imagery and volumetric data for visualization, enhancing visualization quality and detail.

  • Challenges in data embedding include accurate data restoration and the trade-off between data capacity and image quality

  • The integration of user interactions and improved evaluation metrics can enhance the usability and effectiveness of GenAI for visualization tasks

Read more here . Summarize by AI.

Learn

It’s coming to games too

A cool two minute paper

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