Spending long hours in front of a computer screen trying to plan vacations could soon become a thing of the past. The travel industry has enthusiastically embraced generative AI in recent years. Expedia, for example, integrated a ChatGPT-powered customer service chatbot into its app in early 2023. Kayak did something similar one year later, launching an AI platform trained on customer data and designed to provide personalized, real-time travel recommendations. Also: Are AI subscriptions worth it?

The growing adoption of artificial intelligence (AI) has been accelerating across all parts of society, boosting productivity and addressing pressing global challenges such as climate change. Nonetheless, the technological advancement of AI relies on computationally intensive calculations and thus has led to a surge in resource usage and energy consumption. Even putting aside the environmental toll of server manufacturing and supply chains, AI systems can create a huge environmental cost to communities and regions where they are deployed, including air/thermal pollution due to fossil fuel-based electricity generation and further stressed water resources due to AI's staggering water footprint.12,25 To make AI more environmentally friendly and ensure that its overall impacts on climate change are positive, recent studies have pursued multifaceted approaches, including efficient training and inference,5 energy-efficient GPU and accelerator designs,19 carbon forecasting,14 carbon-aware task scheduling,1,21 green cloud infrastructures,2 sustainable AI policies,10,18 and more. Additionally, datacenter operators have also increasingly adopted carbon-free energy (such as solar and wind power) and climate-conscious cooling systems, lowering carbon footprint and direct water consumption.8

We study the sampling-based planning problem in Markov decision processes (MDPs) that we can access only through a generative model, usually referred to as Monte-Carlo planning. Our objective is to return a good estimate of the optimal value function at any state while minimizing the number of calls to the generative model, i.e. the sample complexity. We propose a new algorithm, TrailBlazer, able to handle MDPs with a finite or an infinite number of transitions from state-action to next states. TrailBlazer is an adaptive algorithm that exploits possible structures of the MDP by exploring only a subset of states reachable by following near-optimal policies. We provide bounds on its sample complexity that depend on a measure of the quantity of near-optimal states.

A.1 Classical Planning Problem Formulation Classical Planning Problems can be mathematically represented by using the tuple P = D, I, G . D is referred to as the problem domain, I is the initial state and G is the goal specification. The possible truth assignment over the predicates defines the state space for the planning problem. The domain is again defined by the tuple D = F, O . F corresponds to the set of fluents, i.e., the state variable used to define the state space and each fluent corresponds to a predicate with some arity, and A correspond to the set of actions that can be performed as part of the planning problem.

In this special episode of Slate Money, Felix Salmon, Elizabeth Spiers, and Emily Peck discuss how long is too long for a vacation. They dig into how time zones should factor into travel plans, how much recovery time you need on the back end of a vacation, and why you should sometimes plan a trip to do absolutely nothing. Want to hear that discussion and hear more Slate Money? Join Slate Plus to unlock weekly bonus episodes. You can subscribe directly from the Slate Money show page on Apple Podcasts and Spotify.

Human-designed visual manuals are crucial components in shape assembly activities. They provide step-by-step guidance on how we should move and connect different parts in a convenient and physically-realizable way. While there has been an ongoing effort in building agents that perform assembly tasks, the information in human-design manuals has been largely overlooked. We identify that this is due to 1) a lack of realistic 3D assembly objects that have paired manuals and 2) the difficulty of extracting structured information from purely image-based manuals. Motivated by this observation, we present IKEA-Manual, a dataset consisting of 102 IKEA objects paired with assembly manuals. We provide fine-grained annotations on the IKEA objects and assembly manuals, including decomposed assembly parts, assembly plans, manual segmentation, and 2D-3D correspondence between 3D parts and visual manuals. We illustrate the broad application of our dataset on four tasks related to shape assembly: assembly plan generation, part segmentation, pose estimation and 3D part assembly.

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