The substance of the symposium addressed the challenges in creating synergistic human and AI-based autonomous systems-of-systems. Recent advances in generative AI techniques (e.g., LLMs) have exacerbated the growing concerns associated with AI, held by researchers and the public alike, such as the risk, trust, ethics, and safety to the users and to the public from the operations of autonomous machines/AI alone in open situations. These concerns present major hurdles in the development of verified and validated engineered systems involving bi-directional pathways across the human-machine barrier; in this context, bi-directionality means understanding the design and operational consequences that the human may have on machine agents and the effects that machine or AI agents may have on humans. Current discussions on human-AI/machine interactions are unresolved or fragmented, focusing either on the impact that AI or machines may have on human stakeholders (including the relevant human factor considerations) or potential ways of involving humans or machines in computational or physical interventions (e.g., data annotations, human-machine behavior interpretations, operator-machine interventions). We believe the challenges associated with human-AI/machine collaborative systems cannot be adequately addressed if the underlying challenges associated with bi-directionality are not fully identified and taken into consideration.

Moreover, the program committee comprised researchers from 12 countries across five continents. The workshop featured six keynote speakers, oral sessions, poster sessions, a panel discussion, and a networking lunch. Of the 20 submitted papers, six were selected for oral and poster presentation, and an additional nine were selected for poster presentation only. The acceptance rate was, therefore, 75%. All accepted papers are published in the open-access workshop's proceedings at https://ceur-ws.org/Vol-3649/.

Predicting future values in multivariate time series is vital across various domains. This work explores the use of large language models (LLMs) for this task. However, LLMs typically handle one-dimensional data. We introduce MultiCast, a zero-shot LLM-based approach for multivariate time series forecasting. It allows LLMs to receive multivariate time series as input, through three novel token multiplexing solutions that effectively reduce dimensionality while preserving key repetitive patterns. Additionally, a quantization scheme helps LLMs to better learn these patterns, while significantly reducing token use for practical applications. We showcase the performance of our approach in terms of RMSE and execution time against state-of-the-art approaches on three real-world datasets.

In the era of deep learning (DL), convolutional neural networks (CNNs), and large language models (LLMs), machine learning (ML) models are becoming increasingly complex, demanding significant computational resources for both inference and training stages. To address this challenge, distributed learning has emerged as a crucial approach, employing parallelization across various devices and environments. This survey explores the landscape of distributed learning, encompassing cloud and edge settings. We delve into the core concepts of data and model parallelism, examining how models are partitioned across different dimensions and layers to optimize resource utilization and performance. We analyze various partitioning schemes for different layer types, including fully connected, convolutional, and recurrent layers, highlighting the trade-offs between computational efficiency, communication overhead, and memory constraints. This survey provides valuable insights for future research and development in this rapidly evolving field by comparing and contrasting distributed learning approaches across diverse contexts.

Food production is a vital global concern and the potential for an agritech revolution through artificial intelligence (AI) remains largely unexplored. This paper presents a comprehensive review focused on the application of machine learning (ML) in agriculture, aiming to explore its transformative potential in farming practices and efficiency enhancement. To understand the extent of research activity in this field, statistical data have been gathered, revealing a substantial growth trend in recent years. This indicates that it stands out as one of the most dynamic and vibrant research domains. By introducing the concept of ML and delving into the realm of smart agriculture, including Precision Agriculture, Smart Farming, Digital Agriculture, and Agriculture 4.0, we investigate how AI can optimize crop output and minimize environmental impact. We highlight the capacity of ML to analyze and classify agricultural data, providing examples of improved productivity and profitability on farms. Furthermore, we discuss prominent ML models and their unique features that have shown promising results in agricultural applications. Through a systematic review of the literature, this paper addresses the existing literature gap on AI in agriculture and offers valuable information to newcomers and researchers. By shedding light on unexplored areas within this emerging field, our objective is to facilitate a deeper understanding of the significant contributions and potential of AI in agriculture, ultimately benefiting the research community.

Compositionality has long been considered a key explanatory property underlying human intelligence: arbitrary concepts can be composed into novel complex combinations, permitting the acquisition of an open ended, potentially infinite expressive capacity from finite learning experiences. Influential arguments have held that neural networks fail to explain this aspect of behavior, leading many to dismiss them as viable models of human cognition. Over the last decade, however, modern deep neural networks (DNNs), which share the same fundamental design principles as their predecessors, have come to dominate artificial intelligence, exhibiting the most advanced cognitive behaviors ever demonstrated in machines. In particular, large language models (LLMs), DNNs trained to predict the next word on a large corpus of text, have proven capable of sophisticated behaviors such as writing syntactically complex sentences without grammatical errors, producing cogent chains of reasoning, and even writing original computer programs -- all behaviors thought to require compositional processing. In this chapter, we survey recent empirical work from machine learning for a broad audience in philosophy, cognitive science, and neuroscience, situating recent breakthroughs within the broader context of philosophical arguments about compositionality. In particular, our review emphasizes two approaches to endowing neural networks with compositional generalization capabilities: (1) architectural inductive biases, and (2) metalearning, or learning to learn. We also present findings suggesting that LLM pretraining can be understood as a kind of metalearning, and can thereby equip DNNs with compositional generalization abilities in a similar way. We conclude by discussing the implications that these findings may have for the study of compositionality in human cognition and by suggesting avenues for future research.

Between 2021 and 2023, AI-Olympics, a series of online AI competitions was hosted by the online evaluation platform Jidi in collaboration with the IJCAI committee. In these competitions, an agent is required to accomplish diverse sports tasks in a two-dimensional continuous world, while competing against an opponent. This paper provides a brief overview of the competition series and highlights notable findings. We aim to contribute insights to the field of multi-agent decision-making and explore the generalization of agents through engineering efforts.

Federated continual learning (FCL) has received increasing attention due to its potential in handling real-world streaming data, characterized by evolving data distributions and varying client classes over time. The constraints of storage limitations and privacy concerns confine local models to exclusively access the present data within each learning cycle. Consequently, this restriction induces performance degradation in model training on previous data, termed "catastrophic forgetting". However, existing FCL approaches need to identify or know changes in data distribution, which is difficult in the real world. To release these limitations, this paper directs attention to a broader continuous framework. Within this framework, we introduce Federated Bayesian Neural Network (FedBNN), a versatile and efficacious framework employing a variational Bayesian neural network across all clients. Our method continually integrates knowledge from local and historical data distributions into a single model, adeptly learning from new data distributions while retaining performance on historical distributions. We rigorously evaluate FedBNN's performance against prevalent methods in federated learning and continual learning using various metrics. Experimental analyses across diverse datasets demonstrate that FedBNN achieves state-of-the-art results in mitigating forgetting.

Advances in Large Language Models (LLMs) have led to significant interest in their potential to support human experts across a range of domains, including public health. In this work we present automated evaluations of LLMs for public health tasks involving the classification and extraction of free text. We combine six externally annotated datasets with seven new internally annotated datasets to evaluate LLMs for processing text related to: health burden, epidemiological risk factors, and public health interventions. We initially evaluate five open-weight LLMs (7-70 billion parameters) across all tasks using zero-shot in-context learning. We find that Llama-3-70B-Instruct is the highest performing model, achieving the best results on 15/17 tasks (using micro-F1 scores). We see significant variation across tasks with all open-weight LLMs scoring below 60% micro-F1 on some challenging tasks, such as Contact Classification, while all LLMs achieve greater than 80% micro-F1 on others, such as GI Illness Classification. For a subset of 12 tasks, we also evaluate GPT-4 and find comparable results to Llama-3-70B-Instruct, which scores equally or outperforms GPT-4 on 6 of the 12 tasks. Overall, based on these initial results we find promising signs that LLMs may be useful tools for public health experts to extract information from a wide variety of free text sources, and support public health surveillance, research, and interventions.

The shift from a linear to a circular economy has the potential to simultaneously reduce uncertainties of material supplies and waste generation. To date, the development of robotic and, more generally, autonomous systems have been rarely integrated into circular economy implementation strategies. In this review, we merge deep-learning vision, compartmental dynamical thermodynamics, and robotic manipulation into a theoretically-coherent physics-based research framework to lay the foundations of circular flow designs of materials, and hence, to speed-up the transition from linearity to circularity. Then, we discuss opportunities for robotics in circular economy.