The rise of large language models (LLMs) has significantly transformed both the construction and application of information retrieval (IR) systems. However, current interactions between IR systems and LLMs remain limited, with LLMs merely serving as part of components within IR systems, and IR systems being constructed independently of LLMs. This separated architecture restricts knowledge sharing and deep collaboration between them. In this paper, we introduce Self-Retrieval, a novel end-to-end LLM-driven information retrieval architecture.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Workers move a decorative replica Oscar statue as preparations are made on the red carpet arrivals area ahead of the 98th Academy Awards in Hollywood on Friday. This is read by an automated voice. Please report any issues or inconsistencies here . It's been more than two decades since the Oscars were celebrated as the United States was launching a war in the Middle East.

We propose stochastic ensemble value expansion (STEVE), a novel model-based technique that addresses this issue. By dynamically interpolating between model rollouts of various horizon lengths for each individual example, STEVE ensures that the model is only utilized when doing so does not introduce significant errors.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

The design of neural network architectures is an important component for achieving state-of-the-art performance with machine learning systems across a broad array of tasks. Much work has endeavored to design and build architectures automatically through clever construction of a search space paired with simple learning algorithms. Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks. An open question is the degree to which such methods may generalize to new domains. In this work we explore the construction of meta-learning techniques for dense image prediction focused on the tasks of scene parsing, person-part segmentation, and semantic image segmentation. Constructing viable search spaces in this domain is challenging because of the multi-scale representation of visual information and the necessity to operate on high resolution imagery. Based on a survey of techniques in dense image prediction, we construct a recursive search space and demonstrate that even with efficient random search, we can identify architectures that outperform human-invented architectures and achieve state-of-the-art performance on three dense prediction tasks including 82.7% on Cityscapes (street scene parsing), 71.3% on PASCAL-Person-Part (person-part segmentation), and 87.9% on PASCAL VOC 2012 (semantic image segmentation). Additionally, the resulting architecture is more computationally efficient, requiring half the parameters and half the computational cost as previous state of the art systems.

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This month we tackle the topic of young people and what AI tools mean for their future. Joining the conversation this time are: Sanmay Das (Virginia Tech), Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Michael Littman (Brown University), and Ella Scallan (AIhub). As AI tools have become ubiquitous, we've seen growing concern and increasing coverage about how the use of such tools from a formative age might affect children. What do you think the impact will be and what skills might young people need to navigate this AI world? I met up with a bunch of high school friends when I was last in Switzerland and they were all wondering what their kids should study. They were wondering if they should do social science, seeing as AI tools have become adept at many tasks, such as coding, writing, art, etc. I think that we need social sciences, but that we also need people who know the technology and who can continue developing it. I say they should continue doing whatever they're interested in and those jobs will evolve and they'll look different, but there will still be a whole wealth of different types of jobs.