Recent developments in digital platforms have highlighted the prevalence of open systems, where agents can arrive and depart over time. While bandit learning in open systems has recently received initial attention, existing work imposes structural assumptions that are frequently violated in practice. A learning paradigm for general open systems creates fresh challenges: newly arriving agents induce endogenous non-stationarity; agent patterns determine how quickly information accumulates; and new agents make regret scale further with the time horizon. To this end, we formulate a unified open-system bandit problem with general dynamics, including heterogeneous rewards and general agent patterns. We introduce new concepts to capture the inherent complexities: the \emph{pre-training degree} of new agents quantifies how much information an agent carries upon entry, \emph{stability} measures the impact of new agents on the system, and \emph{global dynamic regret} compares the cumulative expected reward of all active agents with that of the varying optimal arms. We develop certified global-UCB learning methodologies with provable guarantees. Our regret bounds reveal that entry uncertainty enters linearly via the pre-training degree, while in stable regimes, regret is governed by the time needed to identify a persistent optimal arm, as well as by the agent patterns. We further show that these dependencies are tight via lower bounds in hard instances.

The increasing demand for efficient last-mile delivery in smart logistics underscores the role of autonomous robots in enhancing operational efficiency and reducing costs. Traditional navigation methods, which depend on high-precision maps, are resource-intensive, while learning-based approaches often struggle with generalization in real-world scenarios. To address these challenges, this work proposes the Openstreetmap-enhanced oPen-air sEmantic Navigation (OPEN) system that combines foundation models with classic algorithms for scalable outdoor navigation. The system uses off-the-shelf OpenStreetMap (OSM) for flexible map representation, thereby eliminating the need for extensive pre-mapping efforts. It also employs Large Language Models (LLMs) to comprehend delivery instructions and Vision-Language Models (VLMs) for global localization, map updates, and house number recognition. To compensate the limitations of existing benchmarks that are inadequate for assessing last-mile delivery, this work introduces a new benchmark specifically designed for outdoor navigation in residential areas, reflecting the real-world challenges faced by autonomous delivery systems. Extensive experiments in simulated and real-world environments demonstrate the proposed system's efficacy in enhancing navigation efficiency and reliability. To facilitate further research, our code and benchmark are publicly available.

The growing interest in human-robot collaboration (HRC), where humans and robots cooperate towards shared goals, has seen significant advancements over the past decade. While previous research has addressed various challenges, several key issues remain unresolved. Many domains within HRC involve activities that do not necessarily require human presence throughout the entire task. Existing literature typically models HRC as a closed system, where all agents are present for the entire duration of the task. In contrast, an open model offers flexibility by allowing an agent to enter and exit the collaboration as needed, enabling them to concurrently manage other tasks. In this paper, we introduce a novel multiagent framework called oDec-MDP, designed specifically to model open HRC scenarios where agents can join or leave tasks flexibly during execution. We generalize a recent multiagent inverse reinforcement learning method - Dec-AIRL to learn from open systems modeled using the oDec-MDP. Our method is validated through experiments conducted in both a simplified toy firefighting domain and a realistic dyadic human-robot collaborative assembly. Results show that our framework and learning method improves upon its closed system counterpart.

Recently, a leaked document, allegedly from Google, claimed that open-source AI will outcompete Google and OpenAI. The leak brought to the fore ongoing conversations in the AI community about how an AI system and its many components should be shared with researchers and the public. Even with the slew of recent generative AI system releases, this issue remains unresolved. Irene Solaiman is policy director at Hugging Face, where she leads policy and conducts social impact research. Many people think of this as a binary question: Systems can either be open source or closed source.

Artificial intelligence needs no introduction, although it might need a definition. Amazon is as good a place as any to start. It defines AI as "the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition." Amazon's definition also categorises machine learning and deep learning as "derived from the discipline of AI" which synchs nicely with IBM's framing of AI as the "general concept that machines can be'taught' to mimic human decision-making". Other subsets of AI include logic, natural language processing and robotics.

Microsoft Corp. co-founder Bill Gates spoke out against protectionism in technological research around topics like artificial intelligence, arguing that open systems will inevitably win out over closed ones. In conversation with Bloomberg News editor-in-chief John Micklethwait at the New Economy Forum in Beijing on Thursday, Gates was skeptical about the idea that ongoing U.S.-China trade tensions could ever lead to a bifurcated system of two internets and two mutually exclusive strands of tech research and development. "It just doesn't work that way," said the software pioneer. "AI is very hard to put back in the bottle," Gates said, and "whoever has an open system will get massively ahead" by virtue of being able to integrate more insights from more sources. Citing Microsoft's AI research in Beijing, Gates pondered the rhetorical question of whether it was producing Chinese AI or American AI.

An informal workshop on concurrent logic programming, metaprogramming, and open systems was held at Xerox Palo Alto Research Center (PARC) on 8-9 September 1987 with support from the American Association for Artificial Intelligence. The 50 workshop participants came from the Japanese Fifth Generation Project (ICOT), the Weizmann Institute of Science in Israel, Imperial College in London, the Swedish Institute of Computer Science, Stanford University, the Massachusetts Institute of Technology (MIT), Carnegie-Mellon University (CMU), Cal Tech, Science University of Tokyo, Melbourne University, Calgary University, University of Wisconsin, Case Western Reserve, University of Oregon, Korea Advanced Institute of Science and Technology (KAIST), Quintus, Symbolics, IBM, and Xerox PARC. No proceedings were generated; instead, participants distributed copies of drafts, slides, and recent papers. A shared vision emerged from the morning session with concurrent logic programming fulfilling the same role that C and Assembler do now. Languages such as Flat Concurrent Prolog and Guarded Horn Clauses are seen as general-purpose, parallel machine languages and interface languages between hardware and software and not, as a newcomer to this field might expect, as high-level, AI, problemsolving languages.

François Chollet argues about the Impossibility of an Intelligence Explosion. It is a strong article with the exception of the conclusion. Chollet is accurate in describing the many of the obstacles that we expect to encounter in creating an advanced artificial general intelligence (AGI). These obstacles are as follows ( I use my own categorization, but its mapping with Chollet's should be straightforward): The flaw in Chollet's article is that he believes the pace to be linear. There is little evidence that this is true.

François Chollet argues about the Impossibility of an Intelligence Explosion. It is a strong article with the exception of the conclusion. Chollet is accurate in describing the many of the obstacles that we expect to encounter in creating an advanced artificial general intelligence (AGI). These obstacles are as follows ( I use my own categorization, but its mapping with Chollet's should be straightforward): The flaw in Collet's article is that he believes the pace to be linear. There is little evidence that this is true.

Why do you think that could help you regain your edge? LI: First, every company has its own DNA. Baidu is a technology company. During the desktop age, in order to serve the users better, you just needed to come out with the best technology to rank contents on the internet. I think that's what we are good at.