4,139 research outputs found
Visual art inspired by the collective feeding behavior of sand-bubbler crabs
Sand--bubblers are crabs of the genera Dotilla and Scopimera which are known
to produce remarkable patterns and structures at tropical beaches. From these
pattern-making abilities, we may draw inspiration for digital visual art. A
simple mathematical model is proposed and an algorithm is designed that may
create such sand-bubbler patterns artificially. In addition, design parameters
to modify the patterns are identified and analyzed by computational aesthetic
measures. Finally, an extension of the algorithm is discussed that may enable
controlling and guiding generative evolution of the art-making process
Stochastic Parrots Looking for Stochastic Parrots: LLMs are Easy to Fine-Tune and Hard to Detect with other LLMs
The self-attention revolution allowed generative language models to scale and
achieve increasingly impressive abilities. Such models - commonly referred to
as Large Language Models (LLMs) - have recently gained prominence with the
general public, thanks to conversational fine-tuning, putting their behavior in
line with public expectations regarding AI. This prominence amplified prior
concerns regarding the misuse of LLMs and led to the emergence of numerous
tools to detect LLMs in the wild.
Unfortunately, most such tools are critically flawed. While major
publications in the LLM detectability field suggested that LLMs were easy to
detect with fine-tuned autoencoders, the limitations of their results are easy
to overlook. Specifically, they assumed publicly available generative models
without fine-tunes or non-trivial prompts. While the importance of these
assumptions has been demonstrated, until now, it remained unclear how well such
detection could be countered.
Here, we show that an attacker with access to such detectors' reference human
texts and output not only evades detection but can fully frustrate the detector
training - with a reasonable budget and all its outputs labeled as such.
Achieving it required combining common "reinforcement from critic" loss
function modification and AdamW optimizer, which led to surprisingly good
fine-tuning generalization. Finally, we warn against the temptation to
transpose the conclusions obtained in RNN-driven text GANs to LLMs due to their
better representative ability.
These results have critical implications for the detection and prevention of
malicious use of generative language models, and we hope they will aid the
designers of generative models and detectors.Comment: 15 pages, 6 figures; 10 pages, 7 figures Supplementary Materials;
under review at ECML 202
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis
Each year, expert-level performance is attained in increasingly-complex
multiagent domains, notable examples including Go, Poker, and StarCraft II.
This rapid progression is accompanied by a commensurate need to better
understand how such agents attain this performance, to enable their safe
deployment, identify limitations, and reveal potential means of improving them.
In this paper we take a step back from performance-focused multiagent learning,
and instead turn our attention towards agent behavior analysis. We introduce a
model-agnostic method for discovery of behavior clusters in multiagent domains,
using variational inference to learn a hierarchy of behaviors at the joint and
local agent levels. Our framework makes no assumption about agents' underlying
learning algorithms, does not require access to their latent states or
policies, and is trained using only offline observational data. We illustrate
the effectiveness of our method for enabling the coupled understanding of
behaviors at the joint and local agent level, detection of behavior
changepoints throughout training, discovery of core behavioral concepts,
demonstrate the approach's scalability to a high-dimensional multiagent MuJoCo
control domain, and also illustrate that the approach can disentangle
previously-trained policies in OpenAI's hide-and-seek domain
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
Learning to Speak and Act in a Fantasy Text Adventure Game
We introduce a large scale crowdsourced text adventure game as a research
platform for studying grounded dialogue. In it, agents can perceive, emote, and
act whilst conducting dialogue with other agents. Models and humans can both
act as characters within the game. We describe the results of training
state-of-the-art generative and retrieval models in this setting. We show that
in addition to using past dialogue, these models are able to effectively use
the state of the underlying world to condition their predictions. In
particular, we show that grounding on the details of the local environment,
including location descriptions, and the objects (and their affordances) and
characters (and their previous actions) present within it allows better
predictions of agent behavior and dialogue. We analyze the ingredients
necessary for successful grounding in this setting, and how each of these
factors relate to agents that can talk and act successfully
From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI
This paper reviews the field of Game AI, which not only deals with creating
agents that can play a certain game, but also with areas as diverse as creating
game content automatically, game analytics, or player modelling. While Game AI
was for a long time not very well recognized by the larger scientific
community, it has established itself as a research area for developing and
testing the most advanced forms of AI algorithms and articles covering advances
in mastering video games such as StarCraft 2 and Quake III appear in the most
prestigious journals. Because of the growth of the field, a single review
cannot cover it completely. Therefore, we put a focus on important recent
developments, including that advances in Game AI are starting to be extended to
areas outside of games, such as robotics or the synthesis of chemicals. In this
article, we review the algorithms and methods that have paved the way for these
breakthroughs, report on the other important areas of Game AI research, and
also point out exciting directions for the future of Game AI
Privacy in crowdsourcing:a systematic review
The advent of crowdsourcing has brought with it multiple privacy challenges. For example, essential monitoring activities, while necessary and unavoidable, also potentially compromise contributor privacy. We conducted an extensive literature review of the research related to the privacy aspects of crowdsourcing. Our investigation revealed interesting gender differences and also differences in terms of individual perceptions. We conclude by suggesting a number of future research directions.</p
Methods for Interpreting and Understanding Deep Neural Networks
This paper provides an entry point to the problem of interpreting a deep
neural network model and explaining its predictions. It is based on a tutorial
given at ICASSP 2017. It introduces some recently proposed techniques of
interpretation, along with theory, tricks and recommendations, to make most
efficient use of these techniques on real data. It also discusses a number of
practical applications.Comment: 14 pages, 10 figure
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