arXiv:2404.09932v2 »Full PDF »This work identifies 18 foundational challenges in assuring the alignment and
safety of large language models (LLMs). These challenges are organized into
three different categories: scientific understanding of LLMs, development and
deployment methods, and sociotechnical challenges. Based on the identified
challenges, we pose 200+ concrete research questions.Abstract
arXiv:2407.21783v2 »Full PDF »Modern artificial intelligence (AI) systems are powered by foundation models.
This paper presents a new set of foundation models, called Llama 3. It is a
herd of language models that natively support multilinguality, coding,
reasoning, and tool usage. Our largest model is a dense Transformer with 405B
parameters and a context window of up to 128K tokens. This paper presents an
extensive empirical evaluation of Llama 3. We find that Llama 3 delivers
comparable quality to leading language models such as GPT-4 on a plethora of
tasks. We publicly release Llama 3, including pre-trained and post-trained
versions of the 405B parameter language model and our Llama Guard 3 model for
input and output safety. The paper also presents the results of experiments in
which we integrate image, video, and speech capabilities into Llama 3 via a
compositional approach. We observe this approach performs competitively with
the state-of-the-art on image, video, and speech recognition tasks. The
resulting models are not yet being broadly released as they are still under
development.Abstract
Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level
Traffic Accident Prediction
arXiv:2309.05072v4 »Full PDF »Traffic accidents present substantial challenges to human safety and
socio-economic development in urban areas. Developing a reliable and
responsible traffic accident prediction model is crucial to addressing growing
public safety concerns and enhancing the safety of urban mobility systems.
Traditional methods face limitations at fine spatiotemporal scales due to the
sporadic nature of highrisk accidents and the predominance of non-accident
characteristics. Furthermore, while most current models show promising
occurrence prediction, they overlook the uncertainties arising from the
inherent nature of accidents, and then fail to adequately map the hierarchical
ranking of accident risk values for more precise insights. To address these
issues, we introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural
Network STZITDGNN -- the first uncertainty-aware probabilistic graph deep
learning model in roadlevel traffic accident prediction for multisteps. This
model integrates the interpretability of the statistical Tweedie family model
and the expressive power of graph neural networks. Its decoder innovatively
employs a compound Tweedie model,a Poisson distribution to model the frequency
of accident occurrences and a Gamma distribution to assess injury severity,
supplemented by a zeroinflated component to effectively identify exessive
nonincident instances. Empirical tests using realworld traffic data from
London, UK, demonstrate that the STZITDGNN surpasses other baseline models
across multiple benchmarks and metrics, including accident risk value
prediction, uncertainty minimisation, non-accident road identification and
accident occurrence accuracy. Our study demonstrates that STZTIDGNN can
effectively inform targeted road monitoring, thereby improving urban road
safety strategies.Abstract
SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for
Traffic Accident Prediction
arXiv:2407.17642v1 »Full PDF »Predicting traffic accidents is the key to sustainable city management, which
requires effective address of the dynamic and complex spatiotemporal
characteristics of cities. Current data-driven models often struggle with data
sparsity and typically overlook the integration of diverse urban data sources
and the high-order dependencies within them. Additionally, they frequently rely
on predefined topologies or weights, limiting their adaptability in
spatiotemporal predictions. To address these issues, we introduce the
Spatiotemporal Multiview Adaptive HyperGraph Learning (SMA-Hyper) model, a
dynamic deep learning framework designed for traffic accident prediction.
Building on previous research, this innovative model incorporates dual adaptive
spatiotemporal graph learning mechanisms that enable high-order cross-regional
learning through hypergraphs and dynamic adaptation to evolving urban data. It
also utilises contrastive learning to enhance global and local data
representations in sparse datasets and employs an advance attention mechanism
to fuse multiple views of accident data and urban functional features, thereby
enriching the contextual understanding of risk factors. Extensive testing on
the London traffic accident dataset demonstrates that the SMA-Hyper model
significantly outperforms baseline models across various temporal horizons and
multistep outputs, affirming the effectiveness of its multiview fusion and
adaptive learning strategies. The interpretability of the results further
underscores its potential to improve urban traffic management and safety by
leveraging complex spatiotemporal urban data, offering a scalable framework
adaptable to diverse urban environments.Abstract
Continually updated, including weak-to-strong generalization and
socio-technical thinking. 58 page...
AI alignment aims to make AI systems behave in line with human intentions and
values. As AI systems grow more capable, so do risks from misalignment. To
provide a comprehensive and up-to-date overview of the alignment field, in this
survey, we delve into the core concepts, methodology, and practice of
alignment. First, we identify four principles as the key objectives of AI
alignment: Robustness, Interpretability, Controllability, and Ethicality
(RICE). Guided by these four principles, we outline the landscape of current
alignment research and decompose them into two key components: forward
alignment and backward alignment. The former aims to make AI systems aligned
via alignment training, while the latter aims to gain evidence about the
systems' alignment and govern them appropriately to avoid exacerbating
misalignment risks. On forward alignment, we discuss techniques for learning
from feedback and learning under distribution shift. On backward alignment, we
discuss assurance techniques and governance practices.
We also release and continually update the website (www.alignmentsurvey.com)
which features tutorials, collections of papers, blog posts, and other
resources.Abstract
BlenderBot 3: a deployed conversational agent that continually learns to
responsibly engage
arXiv:2208.03188v3 »Full PDF »We present BlenderBot 3, a 175B parameter dialogue model capable of
open-domain conversation with access to the internet and a long-term memory,
and having been trained on a large number of user defined tasks. We release
both the model weights and code, and have also deployed the model on a public
web page to interact with organic users. This technical report describes how
the model was built (architecture, model and training scheme), and details of
its deployment, including safety mechanisms. Human evaluations show its
superiority to existing open-domain dialogue agents, including its predecessors
(Roller et al., 2021; Komeili et al., 2022). Finally, we detail our plan for
continual learning using the data collected from deployment, which will also be
publicly released. The goal of this research program is thus to enable the
community to study ever-improving responsible agents that learn through
interaction.Abstract
Nteasee: A mixed methods study of expert and general population
perspectives on deploying AI for health in African countries
Artificial Intelligence (AI) for health has the potential to significantly
change and improve healthcare. However in most African countries, identifying
culturally and contextually attuned approaches for deploying these solutions is
not well understood. To bridge this gap, we conduct a qualitative study to
investigate the best practices, fairness indicators, and potential biases to
mitigate when deploying AI for health in African countries, as well as explore
opportunities where artificial intelligence could make a positive impact in
health. We used a mixed methods approach combining in-depth interviews (IDIs)
and surveys. We conduct 1.5-2 hour long IDIs with 50 experts in health, policy,
and AI across 17 countries, and through an inductive approach we conduct a
qualitative thematic analysis on expert IDI responses. We administer a blinded
30-minute survey with case studies to 672 general population participants
across 5 countries in Africa and analyze responses on quantitative scales,
statistically comparing responses by country, age, gender, and level of
familiarity with AI. We thematically summarize open-ended responses from
surveys. Our results find generally positive attitudes, high levels of trust,
accompanied by moderate levels of concern among general population participants
for AI usage for health in Africa. This contrasts with expert responses, where
major themes revolved around trust/mistrust, ethical concerns, and systemic
barriers to integration, among others. This work presents the first-of-its-kind
qualitative research study of the potential of AI for health in Africa from an
algorithmic fairness angle, with perspectives from both experts and the general
population. We hope that this work guides policymakers and drives home the need
for further research and the inclusion of general population perspectives in
decision-making around AI usage.Abstract
BehaviorGPT: Smart Agent Simulation for Autonomous Driving with
Next-Patch Prediction
Simulating realistic behaviors of traffic agents is pivotal for efficiently
validating the safety of autonomous driving systems. Existing data-driven
simulators primarily use an encoder-decoder architecture to encode the
historical trajectories before decoding the future. However, the heterogeneity
between encoders and decoders complicates the models, and the manual separation
of historical and future trajectories leads to low data utilization. Given
these limitations, we propose BehaviorGPT, a homogeneous and fully
autoregressive Transformer designed to simulate the sequential behavior of
multiple agents. Crucially, our approach discards the traditional separation
between "history" and "future" by modeling each time step as the "current" one
for motion generation, leading to a simpler, more parameter- and data-efficient
agent simulator. We further introduce the Next-Patch Prediction Paradigm (NP3)
to mitigate the negative effects of autoregressive modeling, in which models
are trained to reason at the patch level of trajectories and capture long-range
spatial-temporal interactions. Despite having merely 3M model parameters,
BehaviorGPT won first place in the 2024 Waymo Open Sim Agents Challenge with a
realism score of 0.7473 and a minADE score of 1.4147, demonstrating its
exceptional performance in traffic agent simulation.Abstract
SIESEF-FusionNet: Spatial Inter-correlation Enhancement and
Spatially-Embedded Feature Fusion Network for LiDAR Point Cloud Semantic
Segmentation
The ambiguity at the boundaries of different semantic classes in point cloud
semantic segmentation often leads to incorrect decisions in intelligent
perception systems, such as autonomous driving. Hence, accurate delineation of
the boundaries is crucial for improving safety in autonomous driving. A novel
spatial inter-correlation enhancement and spatially-embedded feature fusion
network (SIESEF-FusionNet) is proposed in this paper, enhancing spatial
inter-correlation by combining inverse distance weighting and angular
compensation to extract more beneficial spatial information without causing
redundancy. Meanwhile, a new spatial adaptive pooling module is also designed,
embedding enhanced spatial information into semantic features for strengthening
the context-awareness of semantic features. Experimental results demonstrate
that 83.7% mIoU and 97.8% OA are achieved by SIESEF-FusionNet on the Toronto3D
dataset, with performance superior to other baseline methods. A value of 61.1%
mIoU is reached on the semanticKITTI dataset, where a marked improvement in
segmentation performance is observed. In addition, the effectiveness and
plug-and-play capability of the proposed modules are further verified through
ablation studies.Abstract
Federated Learning (FL) employs a training approach to address scenarios
where users' data cannot be shared across clients. Achieving fairness in FL is
imperative since training data in FL is inherently geographically distributed
among diverse user groups. Existing research on fairness predominantly assumes
access to the entire training data, making direct transfer to FL challenging.
However, the limited existing research on fairness in FL does not effectively
address two key challenges, i.e., (CH1) Current methods fail to deal with the
inconsistency between fair optimization results obtained with surrogate
functions and fair classification results. (CH2) Directly aggregating local
fair models does not always yield a globally fair model due to non Identical
and Independent data Distributions (non-IID) among clients. To address these
challenges, we propose a Wasserstein Fair Federated Learning framework, namely
WassFFed. To tackle CH1, we ensure that the outputs of local models, rather
than the loss calculated with surrogate functions or classification results
with a threshold, remain independent of various user groups. To resolve CH2, we
employ a Wasserstein barycenter calculation of all local models' outputs for
each user group, bringing local model outputs closer to the global output
distribution to ensure consistency between the global model and local models.
We conduct extensive experiments on three real-world datasets, demonstrating
that WassFFed outperforms existing approaches in striking a balance between
accuracy and fairness.Abstract
16737 latest Fairness/Ethics + ML/AI papers
