Planning in textual environments have been shown to be a long-standing challenge even for current models. A recent, promising line of work uses LLMs to generate a formal representation of the environment that can be solved by a symbolic planner. However, existing methods rely on a fully-observed environment where all entity states are initially known, so a one-off representation can be constructed, leading to a complete plan. In contrast, we tackle partially-observed environments where there is initially no sufficient information to plan for the end-goal. We propose PDDLEGO that iteratively construct a planning representation that can lead to a partial plan for a given sub-goal. By accomplishing the sub-goal, more information is acquired to augment the representation, eventually achieving the end-goal. We show that plans produced by few-shot PDDLEGO are 43% more efficient than generating plans end-to-end on the Coin Collector simulation, with strong performance (98%) on the more complex Cooking World simulation where end-to-end LLMs fail to generate coherent plans (4%).

Rydberg atom array experiments have demonstrated the ability to act as powerful quantum simulators, preparing strongly-correlated phases of matter which are challenging to study for conventional computer simulations. A key direction has been the implementation of interactions on frustrated geometries, in an effort to prepare exotic many-body states such as spin liquids and glasses. In this paper, we apply two-dimensional recurrent neural network (RNN) wave functions to study the ground states of Rydberg atom arrays on the kagome lattice. We implement an annealing scheme to find the RNN variational parameters in regions of the phase diagram where exotic phases may occur, corresponding to rough optimization landscapes. For Rydberg atom array Hamiltonians studied previously on the kagome lattice, our RNN ground states show no evidence of exotic spin liquid or emergent glassy behavior. In the latter case, we argue that the presence of a non-zero Edwards-Anderson order parameter is an artifact of the long autocorrelations times experienced with quantum Monte Carlo simulations. This result emphasizes the utility of autoregressive models, such as RNNs, to explore Rydberg atom array physics on frustrated lattices and beyond.

In recent years, deep generative models have been successfully adopted for various molecular design tasks, particularly in the life and material sciences. A critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks aimed at optimizing specific molecular properties. However, redesigning and training an existing effective generative model from scratch for each new design task is impractical. Furthermore, the black-box nature of typical downstream tasks$\unicode{x2013}$such as property prediction$\unicode{x2013}$makes it nontrivial to optimize the generative model in a task-specific manner. In this work, we propose a novel approach for a model uncertainty-guided fine-tuning of a pre-trained variational autoencoder (VAE)-based GMD model through performance feedback in an active learning setting. The main idea is to quantify model uncertainty in the generative model, which is made efficient by working within a low-dimensional active subspace of the high-dimensional VAE parameters explaining most of the variability in the model's output. The inclusion of model uncertainty expands the space of viable molecules through decoder diversity. We then explore the resulting model uncertainty class via black-box optimization made tractable by low-dimensionality of the active subspace. This enables us to identify and leverage a diverse set of high-performing models to generate enhanced molecules. Empirical results across six target molecular properties, using multiple VAE-based generative models, demonstrate that our uncertainty-guided fine-tuning approach consistently outperforms the original pre-trained models.

Humans have an innate drive to seek out causality. Whether fuelled by curiosity or specific goals, we constantly question why things happen, how they are interconnected, and many other related phenomena. To develop AI agents capable of addressing this natural human quest for causality, we urgently need a comprehensive dataset of natural causal questions. Unfortunately, existing datasets either contain only artificially-crafted questions that do not reflect real AI usage scenarios or have limited coverage of questions from specific sources. To address this gap, we present CausalQuest, a dataset of 13,500 naturally occurring questions sourced from social networks, search engines, and AI assistants. We formalize the definition of causal questions and establish a taxonomy for finer-grained classification. Through a combined effort of human annotators and large language models (LLMs), we carefully label the dataset. We find that 42% of the questions humans ask are indeed causal, with the majority seeking to understand the causes behind given effects. Using this dataset, we train efficient classifiers (up to 2.85B parameters) for the binary task of identifying causal questions, achieving high performance with F1 scores of up to 0.877. We conclude with a rich set of future research directions that can build upon our data and models.

VPN adoption has seen steady growth over the past decade due to increased public awareness of privacy and surveillance threats. In response, certain governments are attempting to restrict VPN access by identifying connections using "dual use" DPI technology. To investigate the potential for VPN blocking, we develop mechanisms for accurately fingerprinting connections using OpenVPN, the most popular protocol for commercial VPN services. We identify three fingerprints based on protocol features such as byte pattern, packet size, and server response. Playing the role of an attacker who controls the network, we design a two-phase framework that performs passive fingerprinting and active probing in sequence.

Elon Musk, owner of Tesla and the X (formerly Twitter) platform, attends a symposium on fighting antisemitism titled'Never Again: Lip Service or Deep Conversation' in Krakow, Poland, on Jan. 22, 2024. SHOW ME THE MONEY: Billionaire entrepreneur Elon Musk reiterated his stance this week that artificial intelligence will eventually eliminate the need for humans to work, giving his vision for how the future will look as the technology continues to rapidly advance. AI IN POLITICAL ADS: The Federal Communications Commission last week proposed a new regulation that would require the use of artificial intelligence in political advertisements to be disclosed, which has one commissioner slamming the move as regulatory overreach ahead of the election. The Eastern Command of the Indian Army is currently showcasing the latest defense artillery robot at a stall during'East Tech 2023' in Guwahati, Assam, India, on October 10, 2023. HI-TECH WAR PLANNING: India, a country blessed with a strong high-tech industry, is applying its brains not just to commercial artificial intelligence but also to its military, as its neighbor and regional rival China continues to pour billions into AI research.

Red Arrows pay tribute to Spitfire crash pilot 2 hours agoEleanor Maslin,BBC NewsShareMODSqn Ldr Mark Long was described as a "passionate aviator" in a tribute by the RAF The Red Arrows have shared their "heartfelt condolences" after the death of a pilot when his Spitfire crashed into a Lincolnshire field. Emergency crews were called shortly before 13:20 BST on 25 May to the site near RAF Coningsby where Sqn Ldr Mark Long crashed. Red Arrows team leader Sqn Ldr Jon Bond said he and fellow pilots were supporting Sqn Ldr Long's family "as much as we can". The display team is getting ready to start its 60th anniversary season after returning from winter training in Greece on Saturday.Sqn Ldr Jon Bond said "things can change quickly" when flying The RAF said a "comprehensive investigation" was now under way to determine the cause of the Spitfire crash. Speaking to BBC Radio Lincolnshire, Sqn Ldr Bond said: "Awful news to come back to on Saturday. "Our absolute heartfelt condolences go to Mark's family, all at the BBMF (Battle of Britain Memorial Flight) and all at RAF Coningsby.

Police seized ketamine hidden inside life-size Transformer robots in Thailand. A woman who was previously caught trying to ship meth hidden in a food processing machine was trying to send the robots to Taiwan. Thai prosecutors said Wednesday former Prime Minister Thaksin Shinawatra will be indicted for defaming the monarchy, three months after he was freed on parole on other charges. Thaksin will not yet be indicted because he had filed a request to postpone his original appointment on Wednesday with proof that he has COVID-19, Prayuth Bejraguna, a spokesperson for the Office of the Attorney General, said at a news conference. The attorney general's office scheduled a new appointment for Thaksin's indictment on June 18, Prayuth said, adding that Thaksin will also be indicted for violating the Computer Crime Act.

Ahead of Microsoft finishing up its acquisition of Activision Blizzard, it said bringing that publisher's titles to Xbox Game Pass might take some time. We've only seen one so far(Diablo IV), but the company now has added a notable one. Call of Duty: Black Ops 6 will be available on Game Pass on its release day, later this year. Almost every year in recent memory, the latest Call of Duty release has been the best-selling game. Microsoft is relying on the debut of a new Call of Duty game to draw players to its Game Pass. 10 a month is cheaper than the price of the game, but it's also a subscription.

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. Yet, addressing complex urban and environmental management problems normally requires in-depth domain science and informatics expertise. This expertise is essential for deriving data and simulation-driven for informed decision support. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs). By adopting ChatGPT API as the reasoning core, we outline an integrated workflow that encompasses natural language processing, methontology-based prompt tuning, and transformers. This workflow automates the creation of scenario-based ontology using existing research articles and technical manuals of urban datasets and simulations. The outcomes of our methodology are knowledge graphs in widely adopted ontology languages (e.g., OWL, RDF, SPARQL). These facilitate the development of urban decision support systems by enhancing the data and metadata modeling, the integration of complex datasets, the coupling of multi-domain simulation models, and the formulation of decision-making metrics and workflow. The feasibility of our methodology is evaluated through a comparative analysis that juxtaposes our AI-generated ontology with the well-known Pizza Ontology employed in tutorials for popular ontology software (e.g., prot\'eg\'e). We close with a real-world case study of optimizing the complex urban system of multi-modal freight transportation by generating anthologies of various domain data and simulations to support informed decision-making.