53 research outputs found
Antagonising explanation and revealing bias directly through sequencing and multimodal inference
Deep generative models produce data according to a learned representation,
e.g. diffusion models, through a process of approximation computing possible
samples. Approximation can be understood as reconstruction and the large
datasets used to train models as sets of records in which we represent the
physical world with some data structure (photographs, audio recordings,
manuscripts). During the process of reconstruction, e.g., image frames develop
each timestep towards a textual input description. While moving forward in
time, frame sets are shaped according to learned bias and their production, we
argue here, can be considered as going back in time; not by inspiration on the
backward diffusion process but acknowledging culture is specifically marked in
the records. Futures of generative modelling, namely in film and audiovisual
arts, can benefit by dealing with diffusion systems as a process to compute the
future by inevitably being tied to the past, if acknowledging the records as to
capture fields of view at a specific time, and to correlate with our own finite
memory ideals. Models generating new data distributions can target video
production as signal processors and by developing sequences through timelines
we ourselves also go back to decade-old algorithmic and multi-track
methodologies revealing the actual predictive failure of contemporary
approaches to synthesis in moving image, both as relevant to composition and
not explanatory.Comment: 3 pages, no figures. ACM C&C 23 Workshop pape
Antagonising explanation and revealing bias directly through sequencing and multimodal inference.
Deep generative models produce data according to a learned representation, e.g. diffusion models, through a process of approximation computing possible samples. Approximation can be understood as reconstruction and the large datasets used to train models as sets of records in which we represent the physical world with some data structure (photographs, audio recordings, manuscripts). During the process of reconstruction, e.g., image frames develop each timestep towards a textual input description. While moving forward in time, frame sets are shaped according to learned bias and their production, we argue here, can be considered as going back in time; not by inspiration on the backward diffusion process but acknowledging culture is specifically marked in the records. Futures of generative modelling, namely in film and audiovisual arts, can benefit by dealing with diffusion systems as a process to compute the future by inevitably being tied to the past, if acknowledging the records as to capture fields of view at a specific time, and to correlate with our own finite memory ideals. Models generating new data distributions can target video production as signal processors and by developing sequences through timelines we ourselves also go back to decade-old algorithmic and multi-track methodologies revealing the actual predictive failure of contemporary approaches to synthesis in moving image, both as relevant to composition and not explanator
Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges
Artificial General Intelligence (AGI), possessing the capacity to comprehend,
learn, and execute tasks with human cognitive abilities, engenders significant
anticipation and intrigue across scientific, commercial, and societal arenas.
This fascination extends particularly to the Internet of Things (IoT), a
landscape characterized by the interconnection of countless devices, sensors,
and systems, collectively gathering and sharing data to enable intelligent
decision-making and automation. This research embarks on an exploration of the
opportunities and challenges towards achieving AGI in the context of the IoT.
Specifically, it starts by outlining the fundamental principles of IoT and the
critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it
delves into AGI fundamentals, culminating in the formulation of a conceptual
framework for AGI's seamless integration within IoT. The application spectrum
for AGI-infused IoT is broad, encompassing domains ranging from smart grids,
residential environments, manufacturing, and transportation to environmental
monitoring, agriculture, healthcare, and education. However, adapting AGI to
resource-constrained IoT settings necessitates dedicated research efforts.
Furthermore, the paper addresses constraints imposed by limited computing
resources, intricacies associated with large-scale IoT communication, as well
as the critical concerns pertaining to security and privacy
On learning spatial sequences with the movement of attention
In this paper we start with a simple question, how is it possible that humans
can recognize different movements over skin with only a prior visual experience
of them? Or in general, what is the representation of spatial sequences that
are invariant to scale, rotation, and translation across different modalities?
To answer, we rethink the mathematical representation of spatial sequences,
argue against the minimum description length principle, and focus on the
movements of attention. We advance the idea that spatial sequences must be
represented on different levels of abstraction, this adds redundancy but is
necessary for recognition and generalization. To address the open question of
how these abstractions are formed we propose two hypotheses: the first invites
exploring selectionism learning, instead of finding parameters in some models;
the second proposes to find new data structures, not neural network
architectures, to efficiently store and operate over redundant features to be
further selected. Movements of attention are central to human cognition and
lessons should be applied to new better learning algorithms.Comment: 10 pages, 3 figure
Artificial Superintelligence: Coordination & Strategy
Attention in the AI safety community has increasingly started to include strategic considerations of coordination between relevant actors in the field of AI and AI safety, in addition to the steadily growing work on the technical considerations of building safe AI systems. This shift has several reasons: Multiplier effects, pragmatism, and urgency. Given the benefits of coordination between those working towards safe superintelligence, this book surveys promising research in this emerging field regarding AI safety. On a meta-level, the hope is that this book can serve as a map to inform those working in the field of AI coordination about other promising efforts. While this book focuses on AI safety coordination, coordination is important to most other known existential risks (e.g., biotechnology risks), and future, human-made existential risks. Thus, while most coordination strategies in this book are specific to superintelligence, we hope that some insights yield “collateral benefits” for the reduction of other existential risks, by creating an overall civilizational framework that increases robustness, resiliency, and antifragility
Responsible AI in Africa
This open access book contributes to the discourse of Responsible Artificial Intelligence (AI) from an African perspective. It is a unique collection that brings together prominent AI scholars to discuss AI ethics from theoretical and practical African perspectives and makes a case for African values, interests, expectations and principles to underpin the design, development and deployment (DDD) of AI in Africa. The book is a first in that it pays attention to the socio-cultural contexts of Responsible AI that is sensitive to African cultures and societies. It makes an important contribution to the global AI ethics discourse that often neglects AI narratives from Africa despite growing evidence of DDD in many domains. Nine original contributions provide useful insights to advance the understanding and implementation of Responsible AI in Africa, including discussions on epistemic injustice of global AI ethics, opportunities and challenges, an examination of AI co-bots and chatbots in an African work space, gender and AI, a consideration of African philosophies such as Ubuntu in the application of AI, African AI policy, and a look towards a future of Responsible AI in Africa. This is an open access book
Combating Misinformation in the Age of LLMs: Opportunities and Challenges
Misinformation such as fake news and rumors is a serious threat on
information ecosystems and public trust. The emergence of Large Language Models
(LLMs) has great potential to reshape the landscape of combating
misinformation. Generally, LLMs can be a double-edged sword in the fight. On
the one hand, LLMs bring promising opportunities for combating misinformation
due to their profound world knowledge and strong reasoning abilities. Thus, one
emergent question is: how to utilize LLMs to combat misinformation? On the
other hand, the critical challenge is that LLMs can be easily leveraged to
generate deceptive misinformation at scale. Then, another important question
is: how to combat LLM-generated misinformation? In this paper, we first
systematically review the history of combating misinformation before the advent
of LLMs. Then we illustrate the current efforts and present an outlook for
these two fundamental questions respectively. The goal of this survey paper is
to facilitate the progress of utilizing LLMs for fighting misinformation and
call for interdisciplinary efforts from different stakeholders for combating
LLM-generated misinformation.Comment: 9 pages for the main paper, 35 pages including 656 references, more
resources on "LLMs Meet Misinformation" are on the website:
https://llm-misinformation.github.io
Responsible AI in Africa
This open access book contributes to the discourse of Responsible Artificial Intelligence (AI) from an African perspective. It is a unique collection that brings together prominent AI scholars to discuss AI ethics from theoretical and practical African perspectives and makes a case for African values, interests, expectations and principles to underpin the design, development and deployment (DDD) of AI in Africa. The book is a first in that it pays attention to the socio-cultural contexts of Responsible AI that is sensitive to African cultures and societies. It makes an important contribution to the global AI ethics discourse that often neglects AI narratives from Africa despite growing evidence of DDD in many domains. Nine original contributions provide useful insights to advance the understanding and implementation of Responsible AI in Africa, including discussions on epistemic injustice of global AI ethics, opportunities and challenges, an examination of AI co-bots and chatbots in an African work space, gender and AI, a consideration of African philosophies such as Ubuntu in the application of AI, African AI policy, and a look towards a future of Responsible AI in Africa. This is an open access book
Responsible AI in Africa: Challenges and Opportunities
This open access book contributes to the discourse of Responsible Artificial Intelligence (AI) from an African perspective. It is a unique collection that brings together prominent AI scholars to discuss AI ethics from theoretical and practical African perspectives and makes a case for African values, interests, expectations and principles to underpin the design, development and deployment (DDD) of AI in Africa. The book is a first in that it pays attention to the socio-cultural contexts of Responsible AI that is sensitive to African cultures and societies. It makes an important contribution to the global AI ethics discourse that often neglects AI narratives from Africa despite growing evidence of DDD in many domains. Nine original contributions provide useful insights to advance the understanding and implementation of Responsible AI in Africa, including discussions on epistemic injustice of global AI ethics, opportunities and challenges, an examination of AI co-bots and chatbots in an African work space, gender and AI, a consideration of African philosophies such as Ubuntu in the application of AI, African AI policy, and a look towards a future of Responsible AI in Africa
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