arXiv:2410.20141v1 »Full PDF »The increasing concern for data privacy has driven the rapid development of
federated learning (FL), a privacy-preserving collaborative paradigm. However,
the statistical heterogeneity among clients in FL results in inconsistent
performance of the server model across various clients. Server model may show
favoritism towards certain clients while performing poorly for others,
heightening the challenge of fairness. In this paper, we reconsider the
inconsistency in client performance distribution and introduce the concept of
adversarial multi-armed bandit to optimize the proposed objective with explicit
constraints on performance disparities. Practically, we propose a novel
multi-armed bandit-based allocation FL algorithm (FedMABA) to mitigate
performance unfairness among diverse clients with different data distributions.
Extensive experiments, in different Non-I.I.D. scenarios, demonstrate the
exceptional performance of FedMABA in enhancing fairness.Abstract
arXiv:2410.21276v1 »Full PDF »GPT-4o is an autoregressive omni model that accepts as input any combination
of text, audio, image, and video, and generates any combination of text, audio,
and image outputs. It's trained end-to-end across text, vision, and audio,
meaning all inputs and outputs are processed by the same neural network. GPT-4o
can respond to audio inputs in as little as 232 milliseconds, with an average
of 320 milliseconds, which is similar to human response time in conversation.
It matches GPT-4 Turbo performance on text in English and code, with
significant improvement on text in non-English languages, while also being much
faster and 50\% cheaper in the API. GPT-4o is especially better at vision and
audio understanding compared to existing models. In line with our commitment to
building AI safely and consistent with our voluntary commitments to the White
House, we are sharing the GPT-4o System Card, which includes our Preparedness
Framework evaluations. In this System Card, we provide a detailed look at
GPT-4o's capabilities, limitations, and safety evaluations across multiple
categories, focusing on speech-to-speech while also evaluating text and image
capabilities, and measures we've implemented to ensure the model is safe and
aligned. We also include third-party assessments on dangerous capabilities, as
well as discussion of potential societal impacts of GPT-4o's text and vision
capabilities.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
Learning to Generate Training Datasets for Robust Semantic Segmentation
Semantic segmentation methods have advanced significantly. Still, their
robustness to real-world perturbations and object types not seen during
training remains a challenge, particularly in safety-critical applications. We
propose a novel approach to improve the robustness of semantic segmentation
techniques by leveraging the synergy between label-to-image generators and
image-to-label segmentation models. Specifically, we design Robusta, a novel
robust conditional generative adversarial network to generate realistic and
plausible perturbed images that can be used to train reliable segmentation
models. We conduct in-depth studies of the proposed generative model, assess
the performance and robustness of the downstream segmentation network, and
demonstrate that our approach can significantly enhance the robustness in the
face of real-world perturbations, distribution shifts, and out-of-distribution
samples. Our results suggest that this approach could be valuable in
safety-critical applications, where the reliability of perception modules such
as semantic segmentation is of utmost importance and comes with a limited
computational budget in inference. We release our code at
https://github.com/ENSTA-U2IS-AI/robusta.Abstract
Generative AI and Copyright: A Dynamic Perspective
arXiv:2402.17801v1 »Full PDF »The rapid advancement of generative AI is poised to disrupt the creative
industry. Amidst the immense excitement for this new technology, its future
development and applications in the creative industry hinge crucially upon two
copyright issues: 1) the compensation to creators whose content has been used
to train generative AI models (the fair use standard); and 2) the eligibility
of AI-generated content for copyright protection (AI-copyrightability). While
both issues have ignited heated debates among academics and practitioners, most
analysis has focused on their challenges posed to existing copyright doctrines.
In this paper, we aim to better understand the economic implications of these
two regulatory issues and their interactions. By constructing a dynamic model
with endogenous content creation and AI model development, we unravel the
impacts of the fair use standard and AI-copyrightability on AI development, AI
company profit, creators income, and consumer welfare, and how these impacts
are influenced by various economic and operational factors. For example, while
generous fair use (use data for AI training without compensating the creator)
benefits all parties when abundant training data exists, it can hurt creators
and consumers when such data is scarce. Similarly, stronger AI-copyrightability
(AI content enjoys more copyright protection) could hinder AI development and
reduce social welfare. Our analysis also highlights the complex interplay
between these two copyright issues. For instance, when existing training data
is scarce, generous fair use may be preferred only when AI-copyrightability is
weak. Our findings underscore the need for policymakers to embrace a dynamic,
context-specific approach in making regulatory decisions and provide insights
for business leaders navigating the complexities of the global regulatory
environment.Abstract
Llama 2: Open Foundation and Fine-Tuned Chat Models
arXiv:2307.09288v2 »Full PDF »In this work, we develop and release Llama 2, a collection of pretrained and
fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70
billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for
dialogue use cases. Our models outperform open-source chat models on most
benchmarks we tested, and based on our human evaluations for helpfulness and
safety, may be a suitable substitute for closed-source models. We provide a
detailed description of our approach to fine-tuning and safety improvements of
Llama 2-Chat in order to enable the community to build on our work and
contribute to the responsible development of LLMs.Abstract
Trans4Trans: Efficient Transformer for Transparent Object Segmentation
to Help Visually Impaired People Navigate in the Real World
Accepted to ICCV 2021 ACVR Workshop. Code will be made publicly
available at https://github.com/ja...
Common fully glazed facades and transparent objects present architectural
barriers and impede the mobility of people with low vision or blindness, for
instance, a path detected behind a glass door is inaccessible unless it is
correctly perceived and reacted. However, segmenting these safety-critical
objects is rarely covered by conventional assistive technologies. To tackle
this issue, we construct a wearable system with a novel dual-head Transformer
for Transparency (Trans4Trans) model, which is capable of segmenting general
and transparent objects and performing real-time wayfinding to assist people
walking alone more safely. Especially, both decoders created by our proposed
Transformer Parsing Module (TPM) enable effective joint learning from different
datasets. Besides, the efficient Trans4Trans model composed of symmetric
transformer-based encoder and decoder, requires little computational expenses
and is readily deployed on portable GPUs. Our Trans4Trans model outperforms
state-of-the-art methods on the test sets of Stanford2D3D and Trans10K-v2
datasets and obtains mIoU of 45.13% and 75.14%, respectively. Through various
pre-tests and a user study conducted in indoor and outdoor scenarios, the
usability and reliability of our assistive system have been extensively
verified.Abstract
Trans4Trans: Efficient Transformer for Transparent Object and Semantic
Scene Segmentation in Real-World Navigation Assistance
Transparent objects, such as glass walls and doors, constitute architectural
obstacles hindering the mobility of people with low vision or blindness. For
instance, the open space behind glass doors is inaccessible, unless it is
correctly perceived and interacted with. However, traditional assistive
technologies rarely cover the segmentation of these safety-critical transparent
objects. In this paper, we build a wearable system with a novel dual-head
Transformer for Transparency (Trans4Trans) perception model, which can segment
general- and transparent objects. The two dense segmentation results are
further combined with depth information in the system to help users navigate
safely and assist them to negotiate transparent obstacles. We propose a
lightweight Transformer Parsing Module (TPM) to perform multi-scale feature
interpretation in the transformer-based decoder. Benefiting from TPM, the
double decoders can perform joint learning from corresponding datasets to
pursue robustness, meanwhile maintain efficiency on a portable GPU, with
negligible calculation increase. The entire Trans4Trans model is constructed in
a symmetrical encoder-decoder architecture, which outperforms state-of-the-art
methods on the test sets of Stanford2D3D and Trans10K-v2 datasets, obtaining
mIoU of 45.13% and 75.14%, respectively. Through a user study and various
pre-tests conducted in indoor and outdoor scenes, the usability and reliability
of our assistive system have been extensively verified. Meanwhile, the
Tran4Trans model has outstanding performances on driving scene datasets. On
Cityscapes, ACDC, and DADA-seg datasets corresponding to common environments,
adverse weather, and traffic accident scenarios, mIoU scores of 81.5%, 76.3%,
and 39.2% are obtained, demonstrating its high efficiency and robustness for
real-world transportation applications.Abstract
Ethics and Governance of Artificial Intelligence: Evidence from a Survey
of Machine Learning Researchers
arXiv:2105.02117v1 »Full PDF »Machine learning (ML) and artificial intelligence (AI) researchers play an
important role in the ethics and governance of AI, including taking action
against what they perceive to be unethical uses of AI (Belfield, 2020; Van
Noorden, 2020). Nevertheless, this influential group's attitudes are not well
understood, which undermines our ability to discern consensuses or
disagreements between AI/ML researchers. To examine these researchers' views,
we conducted a survey of those who published in the top AI/ML conferences (N =
524). We compare these results with those from a 2016 survey of AI/ML
researchers (Grace, Salvatier, Dafoe, Zhang, & Evans, 2018) and a 2018 survey
of the US public (Zhang & Dafoe, 2020). We find that AI/ML researchers place
high levels of trust in international organizations and scientific
organizations to shape the development and use of AI in the public interest;
moderate trust in most Western tech companies; and low trust in national
militaries, Chinese tech companies, and Facebook. While the respondents were
overwhelmingly opposed to AI/ML researchers working on lethal autonomous
weapons, they are less opposed to researchers working on other military
applications of AI, particularly logistics algorithms. A strong majority of
respondents think that AI safety research should be prioritized and that ML
institutions should conduct pre-publication review to assess potential harms.
Being closer to the technology itself, AI/ML re-searchers are well placed to
highlight new risks and develop technical solutions, so this novel attempt to
measure their attitudes has broad relevance. The findings should help to
improve how researchers, private sector executives, and policymakers think
about regulations, governance frameworks, guiding principles, and national and
international governance strategies for AI.Abstract
Accurate real time crime prediction is a fundamental issue for public safety,
but remains a challenging problem for the scientific community. Crime
occurrences depend on many complex factors. Compared to many predictable
events, crime is sparse. At different spatio-temporal scales, crime
distributions display dramatically different patterns. These distributions are
of very low regularity in both space and time. In this work, we adapt the
state-of-the-art deep learning spatio-temporal predictor, ST-ResNet [Zhang et
al, AAAI, 2017], to collectively predict crime distribution over the Los
Angeles area. Our models are two staged. First, we preprocess the raw crime
data. This includes regularization in both space and time to enhance
predictable signals. Second, we adapt hierarchical structures of residual
convolutional units to train multi-factor crime prediction models. Experiments
over a half year period in Los Angeles reveal highly accurate predictive power
of our models.Abstract
16737 latest Fairness/Ethics + ML/AI papers
