The rapid advancement of open-source foundation models has brought transparency and accessibility to this groundbreaking technology. However, this openness has also enabled the development of highly-capable, unsafe models, as exemplified by recent instances such as WormGPT and FraudGPT, which are specifically designed to facilitate criminal activity. As the capabilities of open foundation models continue to grow, potentially outpacing those of closed-source models, the risk of misuse by bad actors poses an increasingly serious threat to society. This paper addresses the critical question of how open foundation model developers should approach model safety in light of these challenges. Our analysis reveals that open-source foundation model companies often provide less restrictive acceptable use policies (AUPs) compared to their closed-source counterparts, likely due to the inherent difficulties in enforcing such policies once the models are released. To tackle this issue, we introduce PRISM, a design framework for open-source foundation model safety that emphasizes Private, Robust, Independent Safety measures, at Minimal marginal cost of compute. The PRISM framework proposes the use of modular functions that moderate prompts and outputs independently of the core language model, offering a more adaptable and resilient approach to safety compared to the brittle reinforcement learning methods currently used for value alignment. By focusing on identifying AUP violations and engaging the developer community in establishing consensus around safety design decisions, PRISM aims to create a safer open-source ecosystem that maximizes the potential of these powerful technologies while minimizing the risks to individuals and society as a whole.

Multi-Task Reinforcement Learning aims at developing agents that are able to continually evolve and adapt to new scenarios. However, this goal is challenging to achieve due to the phenomenon of catastrophic forgetting and the high demand of computational resources. Learning from scratch for each new task is not a viable or sustainable option, and thus agents should be able to collect and exploit prior knowledge while facing new problems. While several methodologies have attempted to address the problem from different perspectives, they lack a common structure. In this work, we propose a new framework, I Know How (IKH), which provides a common formalization. Our methodology focuses on modularity and compositionality of knowledge in order to achieve and enhance agent's ability to learn and adapt efficiently to dynamic environments. To support our framework definition, we present a simple application of it in a simulated driving environment and compare its performance with that of state-of-the-art approaches.

As background to the main story it is important to understand the meaning of artificial intelligence (AI), and more specifically how its subset machine learning (ML) fits into the picture. AI can be generally defined as the theory and development of computer systems that are able to perform tasks that normally require human intelligence. As such AI systems may be adept in discovering new information, making inferences and possessing reasoning capability. ML is a subset of AI focussing on AI methods that are able to learn and adapt. AI includes symbolic computation, such as expert systems, which are not a part of ML, whereas ML builds statistical models of data that may be used for classification and prediction tasks to aid decision-making. Here we focus on ML rather than AI, but will still use the term AI when referring to the more general technology.

A methodology based on deep recurrent models for maritime surveillance, over publicly available Automatic Identification System (AIS) data, is presented in this paper. The setup employs a deep Recurrent Neural Network (RNN)-based model, for encoding and reconstructing the observed ships' motion patterns. Our approach is based on a thresholding mechanism, over the calculated errors between observed and reconstructed motion patterns of maritime vessels. Specifically, a deep-learning framework, i.e. an encoder-decoder architecture, is trained using the observed motion patterns, enabling the models to learn and predict the expected trajectory, which will be compared to the effective ones. Our models, particularly the bidirectional GRU with recurrent dropouts, showcased superior performance in capturing the temporal dynamics of maritime data, illustrating the potential of deep learning to enhance maritime surveillance capabilities. Our work lays a solid foundation for future research in this domain, highlighting a path toward improved maritime safety through the innovative application of technology.

The rise of automation has provided an opportunity to achieve higher efficiency in manufacturing processes, yet it often compromises the flexibility required to promptly respond to evolving market needs and meet the demand for customization. Human-robot collaboration attempts to tackle these challenges by combining the strength and precision of machines with human ingenuity and perceptual understanding. In this paper, we conceptualize and propose an implementation framework for an autonomous, machine learning-based manipulator that incorporates human-in-the-loop principles and leverages Extended Reality (XR) to facilitate intuitive communication and programming between humans and robots. Furthermore, the conceptual framework foresees human involvement directly in the robot learning process, resulting in higher adaptability and task generalization. The paper highlights key technologies enabling the proposed framework, emphasizing the importance of developing the digital ecosystem as a whole. Additionally, we review the existent implementation approaches of XR in human-robot collaboration, showcasing diverse perspectives and methodologies. The challenges and future outlooks are discussed, delving into the major obstacles and potential research avenues of XR for more natural human-robot interaction and integration in the industrial landscape.

Semantic Textual Similarity (STS) constitutes a critical research direction in computational linguistics and serves as a key indicator of the encoding capabilities of embedding models. Driven by advances in pre-trained language models and contrastive learning techniques, leading sentence representation methods can already achieved average Spearman's correlation scores of approximately 86 across seven STS benchmarks in SentEval. However, further improvements have become increasingly marginal, with no existing method attaining an average score higher than 87 on these tasks. This paper conducts an in-depth analysis of this phenomenon and concludes that the upper limit for Spearman's correlation scores using contrastive learning is 87.5. To transcend this ceiling, we propose an innovative approach termed Pcc-tuning, which employs Pearson's correlation coefficient as a loss function to refine model performance beyond contrastive learning. Experimental results demonstrate that Pcc-tuning markedly surpasses previous state-of-the-art strategies, raising the Spearman's correlation score to above 90.

Memes, which rapidly disseminate personal opinions and positions across the internet, also pose significant challenges in propagating social bias and prejudice. This study presents a novel approach to detecting harmful memes, particularly within the multicultural and multilingual context of Singapore. Our methodology integrates image captioning, Optical Character Recognition (OCR), and Large Language Model (LLM) analysis to comprehensively understand and classify harmful memes. Utilizing the BLIP model for image captioning, PP-OCR and TrOCR for text recognition across multiple languages, and the Qwen LLM for nuanced language understanding, our system is capable of identifying harmful content in memes created in English, Chinese, Malay, and Tamil. To enhance the system's performance, we fine-tuned our approach by leveraging additional data labeled using GPT-4V, aiming to distill the understanding capability of GPT-4V for harmful memes to our system. Our framework achieves top-1 at the public leaderboard of the Online Safety Prize Challenge hosted by AI Singapore, with the AUROC as 0.7749 and accuracy as 0.7087, significantly ahead of the other teams. Notably, our approach outperforms previous benchmarks, with FLAVA achieving an AUROC of 0.5695 and VisualBERT an AUROC of 0.5561.

Federated learning (FL) offers a compelling framework for training large language models (LLMs) while addressing data privacy and decentralization challenges. This paper surveys recent advancements in the federated learning of large language models, with a particular focus on machine unlearning--a crucial aspect for complying with privacy regulations like the Right to be Forgotten. Machine unlearning in the context of federated LLMs involves systematically and securely removing individual data contributions from the learned model without retraining from scratch. We explore various strategies that enable effective unlearning, such as perturbation techniques, model decomposition, and incremental learning, highlighting their implications for maintaining model performance and data privacy. Furthermore, we examine case studies and experimental results from recent literature to assess the effectiveness and efficiency of these approaches in real-world scenarios. Our survey reveals a growing interest in developing more robust and scalable federated unlearning methods, suggesting a vital area for future research in the intersection of AI ethics and distributed machine learning technologies.

The data augmentation (DA) algorithms are popular Markov chain Monte Carlo (MCMC) algorithms often used for sampling from intractable probability distributions. This review article comprehensively surveys DA MCMC algorithms, highlighting their theoretical foundations, methodological implementations, and diverse applications in frequentist and Bayesian statistics. The article discusses tools for studying the convergence properties of DA algorithms. Furthermore, it contains various strategies for accelerating the speed of convergence of the DA algorithms, different extensions of DA algorithms and outlines promising directions for future research. This paper aims to serve as a resource for researchers and practitioners seeking to leverage data augmentation techniques in MCMC algorithms by providing key insights and synthesizing recent developments.

The differentiable programming paradigm is a cornerstone of modern scientific computing. It refers to numerical methods for computing the gradient of a numerical model's output. Many scientific models are based on differential equations, where differentiable programming plays a crucial role in calculating model sensitivities, inverting model parameters, and training hybrid models that combine differential equations with data-driven approaches. Furthermore, recognizing the strong synergies between inverse methods and machine learning offers the opportunity to establish a coherent framework applicable to both fields. Differentiating functions based on the numerical solution of differential equations is non-trivial. Numerous methods based on a wide variety of paradigms have been proposed in the literature, each with pros and cons specific to the type of problem investigated. Here, we provide a comprehensive review of existing techniques to compute derivatives of numerical solutions of differential equations. We first discuss the importance of gradients of solutions of differential equations in a variety of scientific domains. Second, we lay out the mathematical foundations of the various approaches and compare them with each other. Third, we cover the computational considerations and explore the solutions available in modern scientific software. Last but not least, we provide best-practices and recommendations for practitioners. We hope that this work accelerates the fusion of scientific models and data, and fosters a modern approach to scientific modelling.