'Surveillance pricing': Why you might be paying more than your neighbour You go into a store to buy a two-litre bottle of milk at your local supermarket and pay $3. But the person before you in the queue paid $3.50. And the person after you paid $2. What if those prices were based on your personal data or circumstances, or even the battery power on your phone? This may sound like science fiction, but it's not as far-fetched as you might think. In July, US group Delta Air Lines revealed that approximately 3 percent of its domestic fare pricing is determined using artificial intelligence (AI) - although it has not elaborated on how this happens. The company said it aims to increase this figure to 20 percent by the end of this year.

We propose a simple yet effective use of LLM-powered AI tools to improve causal estimation. In double machine learning, the accuracy of causal estimates of the effect of a treatment on an outcome in the presence of a high-dimensional confounder depends on the performance of estimators of conditional expectation functions. We show that predictions made by generative models trained on historical data can be used to improve the performance of these estimators relative to approaches that solely rely on adjusting for embeddings extracted from these models. We argue that the historical knowledge and reasoning capacities associated with these generative models can help overcome curse-of-dimensionality problems in causal inference problems. We consider a case study using a small dataset of online jewelry auctions, and demonstrate that inclusion of LLM-generated guesses as predictors can improve efficiency in estimation.

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While significant progress has been made in developing autonomous agents for shape assembly, existing datasets have not yet tackled the 4D grounding of assembly instructions in videos, essential for a holistic understanding of assembly in 3D space over time.

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This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome the limitations of previous models by effectively processing semantic data and offering versatility in modeling various tasks.

LLMs can now act as autonomous agents that interact with digital environments and complete specific objectives ( e.g., arranging an online meeting).

Pay attention to the edges and boundaries of the part to create a clean and precise mask.

LLMs excel at few-shot in-context learning (ICL) - learning from a few input-output examples ("shots") provided in context at inference, without any weight updates.