Domain models enable autonomous agents to solve long-horizon tasks by producing interpretable plans. However, in open-world environments, a single general domain model cannot capture the variety of tasks, so agents must generate suitable task-specific models on the fly. Large Language Models (LLMs), with their implicit common knowledge, can generate such domains, but suffer from high error rates that limit their applicability. Hence, related work relies on extensive human feed-back or prior knowledge, which undermines autonomous, open-world deployment. In this work, we propose LODGE, a framework for autonomous domain learning from LLMs and environment grounding. LODGE builds on hierarchical abstractions and automated simulations to identify and correct inconsistencies between abstraction layers and between the model and environment. Our framework is task-agnostic, as it generates predicates, operators, and their preconditions and effects, while only assuming access to a simulator and a set of generic, executable low-level skills. Experiments on two International Planning Competition ( IPC) domains and a robotic assembly domain show that LODGE yields more accurate domain models and higher task success than existing methods, requiring remarkably few environment interactions and no human feedback or demonstrations.

In Ari Aster's movies, the price of understanding how the world really works is your sanity, if not your life. His first three movies--Hereditary, Midsommar, and Beau Is Afraid--center on characters whose feeling that there's something sinister going on beneath the surface of their existence is eventually proved to be correct, but it's as if their bodies aren't equipped to contain that knowledge. One way or another, their minds are gone. The people in Aster's polarizing fourth movie, Eddington, a Western-inflected psychodrama set during the early days of the COVID-19 pandemic, don't get off so easy. The stress test of a rapidly spreading virus with no known treatment exposes innumerable cracks in society's facade: the gap between remote workers and people forced to risk their lives in order to earn a living; between people who breathe a sigh of relief when they see a police car approaching and people who have to be sure to keep their hands in plain sight.

At the recent QCon London conference, Mathew Lodge, CEO of DiffBlue, gave a presentation on the advancements in artificial intelligence (AI) for writing code. Lodge highlighted the differences between Large Language Models and Reinforcement Learning approaches, emphasizing what both approaches can and can't do. The session gave an overview of the state of the current state of AI-powered code generation and its future trajectory. In his presentation, Lodge delved into the differences between AI-powered code generation tools and unit test writing tools. Code generation tools like GitHub Copilot, TabNine, and ChatGPT primarily focus on completing code snippets or suggesting code based on the context provided.

The three of us were in a 1957 de Havilland Beaver, floating in the middle of a crater lake in the southwest quadrant of Alaska. The pilot was recounting the toll that the Vietnam War had taken on him, while, over in the right seat, my boyfriend, Karl, listened. Thanks to proximity, I was listening as well, though chances are they'd forgotten I was there. Outside, water sloshed against the pontoons, rocking the plane gently from side to side. No one had asked this man to tell his story in a long time, but Karl had asked, and so the pilot put the plane down on the lake, turned off the ignition, and began.

Developers are critically important to every business, and they're not cheap. According to the Bureau of Labor Statistics, the median annual salary for a software developer in the US is close to $110,000. In San Francisco, it's closer to $145,000, where entry-level developers can command $100,000. Meanwhile, developer tools are typically priced using the size of development team (seats) as a proxy. Production platforms, on the other hand, are typically charged by the size of the environment.

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Anaconda Inc. announced today it is leveraging Docker containers and Kubernetes clusters to accelerate the development of AI applications built and deployed using graphical processor units (GPUs) from NVIDIA. Previously, Anaconda added support for Docker and Kubernetes to version 5.0 of Anaconda Enterprise, a commercially supported instance of an open source platform for developing, governing and automating data science and AI pipelines on Intel processors. A version 5.2 of Anaconda Enterprise extends that platform to add support for GPUs. Matthew Lodge, senior vice president of products and marketing at Anaconda, says that training AI applications has been proven to be significantly faster on GPUs. But over time, developers of AI applications will be employing a broad range of algorithms across Intel processors, GPUs, field programmable gate arrays and new classes of processors such as the TPU processors developed by Google, which are designed specifically for AI applications.

I recently spent a few days with my family at a ski lodge in the Sierras. While we were there, we noticed a young French couple who spent the vast majority of their time sitting by the fire, staring at a tiny Android tablet, flipping through a guidebook, and making notes in a paper calendar. For two or three days, while we were out skiing through the sublimely silent woods, learning how to snowboard on the bunny slope, and sitting in the hot tub underneath the misty evening skies, these two sad, beautiful foreigners were sitting in the lodge, trying to plan their trip. A similar experience motivated Travis Katz to create Gogobot, a social travel recommendation engine that helps you plan your trip quickly and efficiently by using the expertise of your Facebook and Twitter networks. Katz, while working for MySpace in London a few years ago, spent so much time researching and planning European trips that he wound up missing many of the actual trips.

The consumer sentiment analysis of this one's pretty easy, but will they be compensated? When a person feels sufficiently wronged to lodge a complaint with the Consumer Financial Protection Bureau (CFPB), there's likely to be some negative sentiment involved. But is there a connection between the language they use and the likelihood they will be compensated by the offending company? At the upcoming Sentiment Analysis Symposium, I will discuss how machine learning and rule-based sentiment analysis can support each other in a complementary analysis, and produce actionable information from large amounts of free form text. In this case, machine learning and sentiment analysis could improve and evolve the CFPB's ability to assess consumer complaints.

To them that had had, more would be given (Lodge 1986, p. 172). "Morris read through the letter. Was it a shade too fulsome? No, that was another law of academic life: it is impossible to be excessive in the flattery of one's peers." There we met Morris That book was made by Mr. Mark I read these lines as a new truth." I haven't even gotten my Who is talking floor, and stepped out on to his regular on the British version of the here? More importantly, whom balcony to inhale the air, scented Discovery Channel), and womanizer should I believe? Messenger, as his wife Twain" disguised as Huck?