pwc-archive/papers-with-abstracts huggingface dataset:
The potential functions of an international institution for AI safety. Insights from adjacent policy areas and recent trends
Authors: A. Leone De Castris, C. Thomas
Date: 2024-08-31 00:00:00

Governments, industry, and other actors involved in governing AI technologies around the world agree that, while AI offers tremendous promise to benefit the world, appropriate guardrails are required to mitigate risks. Global institutions, including the OECD, the G7, the G20, UNESCO, and the Council of Europe, have already started developing frameworks for ethical and responsible AI governance. While these are important initial steps, they alone fall short of addressing the need for institutionalised international processes to identify and assess potentially harmful AI capabilities. Contributing to the relevant conversation on how to address this gap, this chapter reflects on what functions an international AI safety institute could perform. Based on the analysis of both existing international governance models addressing safety considerations in adjacent policy areas and the newly established national AI safety institutes in the UK and US, the chapter identifies a list of concrete functions that could be performed at the international level. While creating a new international body is not the only way forward, understanding the structure of these bodies from a modular perspective can help us to identify the tools at our disposal. These, we suggest, can be categorised under three functional domains: a) technical research and cooperation, b) safeguards and evaluations, c) policymaking and governance support.
Effective Monitoring of Online Decision-Making Algorithms in Digital Intervention Implementation
Authors: Anna L. Trella, Susobhan Ghosh, Erin E. Bonar, Lara Coughlin, Finale Doshi-Velez, Yongyi Guo, Pei-Yao Hung, Inbal Nahum-Shani, Vivek Shetty, Maureen Walton, Iris Yan, Kelly W. Zhang, Susan A. Murphy
Date: 2024-08-30 00:00:00
Tasks: Decision Making, scientific discovery

Online AI decision-making algorithms are increasingly used by digital interventions to dynamically personalize treatment to individuals. These algorithms determine, in real-time, the delivery of treatment based on accruing data. The objective of this paper is to provide guidelines for enabling effective monitoring of online decision-making algorithms with the goal of (1) safeguarding individuals and (2) ensuring data quality. We elucidate guidelines and discuss our experience in monitoring online decision-making algorithms in two digital intervention clinical trials (Oralytics and MiWaves). Our guidelines include (1) developing fallback methods, pre-specified procedures executed when an issue occurs, and (2) identifying potential issues categorizing them by severity (red, yellow, and green). Across both trials, the monitoring systems detected real-time issues such as out-of-memory issues, database timeout, and failed communication with an external source. Fallback methods prevented participants from not receiving any treatment during the trial and also prevented the use of incorrect data in statistical analyses. These trials provide case studies for how health scientists can build monitoring systems for their digital intervention. Without these algorithm monitoring systems, critical issues would have gone undetected and unresolved. Instead, these monitoring systems safeguarded participants and ensured the quality of the resulting data for updating the intervention and facilitating scientific discovery. These monitoring guidelines and findings give digital intervention teams the confidence to include online decision-making algorithms in digital interventions.
Achieving Responsible AI through ESG: Insights and Recommendations from Industry Engagement
Authors: Harsha Perera, Sung Une Lee, Yue Liu, Boming Xia, Qinghua Lu, Liming Zhu, Jessica Cairns, Moana Nottage
Date: 2024-08-30 00:00:00

As Artificial Intelligence (AI) becomes integral to business operations, integrating Responsible AI (RAI) within Environmental, Social, and Governance (ESG) frameworks is essential for ethical and sustainable AI deployment. This study examines how leading companies align RAI with their ESG goals. Through interviews with 28 industry leaders, we identified a strong link between RAI and ESG practices. However, a significant gap exists between internal RAI policies and public disclosures, highlighting the need for greater board-level expertise, robust governance, and employee engagement. We provide key recommendations to strengthen RAI strategies, focusing on transparency, cross-functional collaboration, and seamless integration into existing ESG frameworks.
ISO: Overlap of Computation and Communication within Seqenence For LLM Inference
Authors: Bin Xiao, Lei Su
Date: 2024-09-04 00:00:00
Tasks: GPU, Language Modeling, Language Modelling, Large Language Model

In the realm of Large Language Model (LLM) inference, the inherent structure of transformer models coupled with the multi-GPU tensor parallelism strategy leads to a sequential execution of computation and communication. This results in substantial underutilization of computing resources during the communication phase. To mitigate this inefficiency, various techniques have been developed to optimize the use of computational power throughout the communication process. These strategies primarily involve overlapping matrix computations and communications, as well as interleaving micro-batches across different requests. Nonetheless, these approaches either fall short of achieving ideal overlap or impose certain limitations on their application. To overcome these challenges, this paper introduces a novel strategy for computation-communication overlap that operates at the sequence level. This method not only enhances the degree of overlap but also minimizes the constraints on its applicability. Experimental evaluations conducted using 30b/70b models have demonstrated significant improvements in efficiency. Specifically, the proposed technique has been shown to reduce time consumption by approximately 35% on 4090 GPU and by roughly 15% on A800 GPU during the prefill stage of LLM inference.
Three Pillars Towards Next-Generation Routing System
Authors: Lei LI, Mengxuan Zhang, Zizhuo Xu, Yehong Xu, Xiaofang Zhou
Date: 2024-09-03 00:00:00
Tasks: Management

The routing results are playing an increasingly important role in transportation efficiency, but they could generate traffic congestion unintentionally. This is because the traffic condition and routing system are disconnected components in the current routing paradigm. In this paper, we propose a next-generation routing paradigm that could reduce traffic congestion by considering the influence of the routing results in real-time. Specifically, we regard the routing results as the root cause of the future traffic flow, which at the same time is identified as the root cause of traffic conditions. To implement such a system, we identify three essential components: 1) the traffic condition simulation that establishes the relation between traffic flow and traffic condition with guaranteed accuracy; 2) the future route management that supports efficient simulation with dynamic route update; 3) the global routing optimization that improves the overall transportation system efficiency. Preliminary design and experimental results will be presented, and the corresponding challenges and research directions will also be discussed.
Biological arrow of time: Emergence of tangled information hierarchies and self-modelling dynamics
Authors: Mikhail Prokopenko, Paul C. W. Davies, Michael Harré, Marcus Heisler, Zdenka Kuncic, Geraint F. Lewis, Ori Livson, Joseph T. Lizier, Fernando E. Rosas
Date: 2024-09-18 00:00:00

We study open-ended evolution by focusing on computational and information-processing dynamics underlying major evolutionary transitions. In doing so, we consider biological organisms as hierarchical dynamical systems that generate regularities in their phase-spaces through interactions with their environment. These emergent information patterns can then be encoded within the organism's components, leading to self-modelling "tangled hierarchies". Our main conjecture is that when macro-scale patterns are encoded within micro-scale components, it creates fundamental tensions (computational inconsistencies) between what is encodable at a particular evolutionary stage and what is potentially realisable in the environment. A resolution of these tensions triggers an evolutionary transition which expands the problem-space, at the cost of generating new tensions in the expanded space, in a continual process. We argue that biological complexification can be interpreted computation-theoretically, within the G\"odel--Turing--Post recursion-theoretic framework, as open-ended generation of computational novelty. In general, this process can be viewed as a meta-simulation performed by higher-order systems that successively simulate the computation carried out by lower-order systems. This computation-theoretic argument provides a basis for hypothesising the biological arrow of time.
Personalized Speech Emotion Recognition in Human-Robot Interaction using Vision Transformers
Authors: Ruchik Mishra, Andrew Frye, Madan Mohan Rayguru, Dan O. Popa
Date: 2024-09-16 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

Emotions are an essential element in verbal communication, so understanding individuals' affect during a human-robot interaction (HRI) becomes imperative. This paper investigates the application of vision transformer models, namely ViT (Vision Transformers) and BEiT (BERT Pre-Training of Image Transformers) pipelines, for Speech Emotion Recognition (SER) in HRI. The focus is to generalize the SER models for individual speech characteristics by fine-tuning these models on benchmark datasets and exploiting ensemble methods. For this purpose, we collected audio data from different human subjects having pseudo-naturalistic conversations with the NAO robot. We then fine-tuned our ViT and BEiT-based models and tested these models on unseen speech samples from the participants. In the results, we show that fine-tuning vision transformers on benchmark datasets and and then using either these already fine-tuned models or ensembling ViT/BEiT models gets us the highest classification accuracies per individual when it comes to identifying four primary emotions from their speech: neutral, happy, sad, and angry, as compared to fine-tuning vanilla-ViTs or BEiTs.
Meijer-G Function with Continued Product and Integer Exponent: Performance of Multi-Aperture UOWC System over EGG Turbulence
Authors: Arvind Kumar, Nikumani Choudhury, Jayendra N. Bandyopadhyay, S. M. Zafaruddin
Date: 2024-09-17 00:00:00

Signal transmission over underwater optical wireless communication (UOWC) experiences the combined effect of oceanic turbulence and pointing errors statistically modeled using the sum of two Meijer-G functions. There is a research gap in the exact statistical analysis of multi-aperture UOWC systems that use selection combining diversity techniques to enhance performance compared to single-aperture systems. In this paper, we develop a general framework for the continued product and positive integer exponent for the sum of Meijer-G functions to analyze the exact statistical performance of the UOWC system in terms of multivariate Fox-H function for both independent and non-identically distributed (i.ni.d.) and independent and identically distributed (i.i.d.) channels. We also approximate the performance of a multi-aperture UOWC system with i.i.d. channels using the single-variate Fox-H function. Using the generalized approach, we present analytical expressions for average bit-error rate (BER) and ergodic capacity for the considered system operating over exponential generalized gamma (EGG) oceanic turbulence combined with zero-boresight pointing errors. We also develop asymptotic expressions for the average BER at a high signal-to-noise (SNR) to capture insights into the system's performance. Our simulation findings confirm the accuracy of our derived expressions and illustrate the impact of turbulence parameters for i.ni.d. and i.i.d. models for the average BER and ergodic capacity, which may provide a better estimate for the efficient deployment of UOWC.
Stimulus Modality Matters: Impact of Perceptual Evaluations from Different Modalities on Speech Emotion Recognition System Performance
Authors: Huang-Cheng Chou, Haibin Wu, Chi-Chun Lee
Date: 2024-09-16 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

Speech Emotion Recognition (SER) systems rely on speech input and emotional labels annotated by humans. However, various emotion databases collect perceptional evaluations in different ways. For instance, the IEMOCAP dataset uses video clips with sounds for annotators to provide their emotional perceptions. However, the most significant English emotion dataset, the MSP-PODCAST, only provides speech for raters to choose the emotional ratings. Nevertheless, using speech as input is the standard approach to training SER systems. Therefore, the open question is the emotional labels elicited by which scenarios are the most effective for training SER systems. We comprehensively compare the effectiveness of SER systems trained with labels elicited by different modality stimuli and evaluate the SER systems on various testing conditions. Also, we introduce an all-inclusive label that combines all labels elicited by various modalities. We show that using labels elicited by voice-only stimuli for training yields better performance on the test set, whereas labels elicited by voice-only stimuli.
"Is This It?": Towards Ecologically Valid Benchmarks for Situated Collaboration
Authors: Dan Bohus, Sean Andrist, Yuwei Bao, Eric Horvitz, Ann Paradiso
Date: 2024-08-30 00:00:00
Tasks: Embodied Question Answering, Question Answering, valid

We report initial work towards constructing ecologically valid benchmarks to assess the capabilities of large multimodal models for engaging in situated collaboration. In contrast to existing benchmarks, in which question-answer pairs are generated post hoc over preexisting or synthetic datasets via templates, human annotators, or large language models (LLMs), we propose and investigate an interactive system-driven approach, where the questions are generated by users in context, during their interactions with an end-to-end situated AI system. We illustrate how the questions that arise are different in form and content from questions typically found in existing embodied question answering (EQA) benchmarks and discuss new real-world challenge problems brought to the fore.
Distributed Perception Aware Safe Leader Follower System via Control Barrier Methods
Authors: Richie R. Suganda, Tony Tran, Miao Pan, Lei Fan, Qin Lin, Bin Hu
Date: 2024-09-17 00:00:00
Tasks: State Estimation

This paper addresses a distributed leader-follower formation control problem for a group of agents, each using a body-fixed camera with a limited field of view (FOV) for state estimation. The main challenge arises from the need to coordinate the agents' movements with their cameras' FOV to maintain visibility of the leader for accurate and reliable state estimation. To address this challenge, we propose a novel perception-aware distributed leader-follower safe control scheme that incorporates FOV limits as state constraints. A Control Barrier Function (CBF) based quadratic program is employed to ensure the forward invariance of a safety set defined by these constraints. Furthermore, new neural network based and double bounding boxes based estimators, combined with temporal filters, are developed to estimate system states directly from real-time image data, providing consistent performance across various environments. Comparison results in the Gazebo simulator demonstrate the effectiveness and robustness of the proposed framework in two distinct environments.
Safety Verification and Navigation for Autonomous Vehicles based on Signal Temporal Logic Constraints
Authors: Aditya Parameshwaran, Yue Wang
Date: 2024-09-16 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control, Navigate

The software architecture behind modern autonomous vehicles (AV) is becoming more complex steadily. Safety verification is now an imminent task prior to the large-scale deployment of such convoluted models. For safety-critical tasks in navigation, it becomes imperative to perform a verification procedure on the trajectories proposed by the planning algorithm prior to deployment. Signal Temporal Logic (STL) constraints can dictate the safety requirements for an AV. A combination of STL constraints is called a specification. A key difference between STL and other logic constraints is that STL allows us to work on continuous signals. We verify the satisfaction of the STL specifications by calculating the robustness value for each signal within the specification. Higher robustness values indicate a safer system. Model Predictive Control (MPC) is one of the most widely used methods to control the navigation of an AV, with an underlying set of state and input constraints. Our research aims to formulate and test an MPC controller, with STL specifications as constraints, that can safely navigate an AV. The primary goal of the cost function is to minimize the control inputs. STL constraints will act as an additional layer of constraints that would change based on the scenario and task on hand. We propose using sTaliro, a MATLAB-based robustness calculator for STL specifications, formulated in a receding horizon control fashion for an AV navigation task. It inputs a simplified AV state space model and a set of STL specifications, for which it constructs a closed-loop controller. We test out our controller for different test cases/scenarios and verify the safe navigation of our AV model.
Conformal Prediction for Manifold-based Source Localization with Gaussian Processes
Authors: Vadim Rozenfeld, Bracha Laufer Goldshtein
Date: 2024-09-18 00:00:00
Tasks: Conformal Prediction, Decision Making, Gaussian Processes, GPR, Prediction Intervals, Uncertainty Quantification, valid

We address the problem of uncertainty quantification (UQ) in the localization of a sound source within adverse acoustic environments. Estimating the position of the source is influenced by various factors, such as noise and reverberation, leading to significant uncertainty. Quantifying this uncertainty is essential, particularly when localization outcomes impact critical decision-making processes, such as in robot audition, where the accuracy of location estimates directly influences subsequent actions. Despite this, common localization methods offer point estimates without quantifying the estimation uncertainty. To address this, we employ conformal prediction (CP)-a framework that delivers statistically valid prediction intervals (PIs) with finite-sample guarantees, independent of the data distribution. However, commonly used Inductive CP (ICP) methods require a large amount of labeled data, which can be difficult to obtain in the localization setting. To mitigate this limitation, we incorporate a semi-supervised manifold-based localization method using Gaussian process regression (GPR), with an efficient Transductive CP (TCP) technique, specifically designed for GPR. We demonstrate that our method generates statistically valid PIs across different acoustic conditions, while producing smaller intervals compared to baselines.
Performance and Robustness of Signal-Dependent vs. Signal-Independent Binaural Signal Matching with Wearable Microphone Arrays
Authors: Ami Berger, Vladimir Tourbabin, Jacob Donley, Zamir Ben-Hur, Boaz Rafaely
Date: 2024-09-18 00:00:00

The increasing popularity of spatial audio in applications such as teleconferencing, entertainment, and virtual reality has led to the recent developments of binaural reproduction methods. However, only a few of these methods are well-suited for wearable and mobile arrays, which typically consist of a small number of microphones. One such method is binaural signal matching (BSM), which has been shown to produce high-quality binaural signals for wearable arrays. However, BSM may be suboptimal in cases of high direct-to-reverberant ratio (DRR) as it is based on the diffuse sound field assumption. To overcome this limitation, previous studies incorporated sound-field models other than diffuse. However, performance may be sensitive to signal estimation errors. This paper aims to provide a systematic and comprehensive analysis of signal-dependent vs. signal-independent BSM, so that the benefits and limitations of the methods become clearer. Two signal-dependent BSM-based methods designed for high DRR scenarios that incorporate a sound field model composed of direct and reverberant components are investigated mathematically, using simulations, and finally validated by a listening test, and compared to the signal-independent BSM. The results show that signal-dependent BSM can significantly improve performance, in particular in the direction of the source, while presenting only a negligible degradation in other directions. Furthermore, when source direction estimation is inaccurate, performance of of the signal-dependent BSM degrade to equal that of the signal-independent BSM, presenting a desired robustness quality.
Dense-TSNet: Dense Connected Two-Stage Structure for Ultra-Lightweight Speech Enhancement
Authors: Zizhen Lin, Yuanle Li, Junyu Wang, Ruili Li
Date: 2024-09-18 00:00:00
Tasks: Mamba, Speech Enhancement

Speech enhancement aims to improve speech quality and intelligibility in noisy environments. Recent advancements have concentrated on deep neural networks, particularly employing the Two-Stage (TS) architecture to enhance feature extraction. However, the complexity and size of these models remain significant, which limits their applicability in resource-constrained scenarios. Designing models suitable for edge devices presents its own set of challenges. Narrow lightweight models often encounter performance bottlenecks due to uneven loss landscapes. Additionally, advanced operators such as Transformers or Mamba may lack the practical adaptability and efficiency that convolutional neural networks (CNNs) offer in real-world deployments. To address these challenges, we propose Dense-TSNet, an innovative ultra-lightweight speech enhancement network. Our approach employs a novel Dense Two-Stage (Dense-TS) architecture, which, compared to the classic Two-Stage architecture, ensures more robust refinement of the objective function in the later training stages. This leads to improved final performance, addressing the early convergence limitations of the baseline model. We also introduce the Multi-View Gaze Block (MVGB), which enhances feature extraction by incorporating global, channel, and local perspectives through convolutional neural networks (CNNs). Furthermore, we discuss how the choice of loss function impacts perceptual quality. Dense-TSNet demonstrates promising performance with a compact model size of around 14K parameters, making it particularly well-suited for deployment in resource-constrained environments.