When you ask an LLM like ChatGPT or Claude a question, the model goes through its massive amount of training data and guesses the answer by mathematically predicting the word most likely to appear next in a sentence.
This model, experts say, will not work well for technology designed to navigate the physical world. Something like a robot that works in a warehouse will instead require a <a href="https://www.sciencefriday.com/segments/world-models-for-robots-ai-math/?utm_source=wnyc&utm_medium=podcast&utm_campaign=scifri" target="_blank" rel="noopener noreferrer">“world model”</a> that can understand spatial surroundings, like the stuff we walk by or bang into.
But what is a world model, exactly? And how do you train AI to recognize what the real world looks like? Host Ira Flatow checks in with tech journalist Joanna Stern, who’s seen the early days of these models up close, <a href="https://www.youtube.com/watch?v=yfZhpEupz5M&embeds_referring_origin=https://app.notion.com" target="_blank" rel="noopener noreferrer">even in her own home.</a>
Then, we check in on the math world, where frontier AI models have made meaningful progress on decades-old problems. Mathematician Emily Riehl gives us the big picture on how significant these results actually are.
Guests:
Joanna Stern is a tech journalist who writes newsletters and creates videos for New Things Media.
Dr. Emily Riehl is a professor of mathematics at Johns Hopkins University.
Transcript will be available after the show airs on <a href="https://www.sciencefriday.com/segments/world-models-for-robots-ai-math/?utm_source=wnyc&utm_medium=podcast&utm_campaign=scifri" target="_blank" rel="noopener noreferrer">sciencefriday.com.</a>
 
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