113,272 research outputs found
NewsMe: A case study for adaptive news systems with open user model
Adaptive news systems have become important in recent years. A lot of work has been put into developing these adaptation processes. We describe here an adaptive news system application, which uses an open user model and allow users to manipulate their interest profiles. We also present a study of the system. Our results showed that user profile manipulation should be used with caution. Ā© 2007 IEEE
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Language is increasingly being used to define rich visual recognition
problems with supporting image collections sourced from the web. Structured
prediction models are used in these tasks to take advantage of correlations
between co-occurring labels and visual input but risk inadvertently encoding
social biases found in web corpora. In this work, we study data and models
associated with multilabel object classification and visual semantic role
labeling. We find that (a) datasets for these tasks contain significant gender
bias and (b) models trained on these datasets further amplify existing bias.
For example, the activity cooking is over 33% more likely to involve females
than males in a training set, and a trained model further amplifies the
disparity to 68% at test time. We propose to inject corpus-level constraints
for calibrating existing structured prediction models and design an algorithm
based on Lagrangian relaxation for collective inference. Our method results in
almost no performance loss for the underlying recognition task but decreases
the magnitude of bias amplification by 47.5% and 40.5% for multilabel
classification and visual semantic role labeling, respectively.Comment: 11 pages, published in EMNLP 201
Time-Contrastive Networks: Self-Supervised Learning from Video
We propose a self-supervised approach for learning representations and
robotic behaviors entirely from unlabeled videos recorded from multiple
viewpoints, and study how this representation can be used in two robotic
imitation settings: imitating object interactions from videos of humans, and
imitating human poses. Imitation of human behavior requires a
viewpoint-invariant representation that captures the relationships between
end-effectors (hands or robot grippers) and the environment, object attributes,
and body pose. We train our representations using a metric learning loss, where
multiple simultaneous viewpoints of the same observation are attracted in the
embedding space, while being repelled from temporal neighbors which are often
visually similar but functionally different. In other words, the model
simultaneously learns to recognize what is common between different-looking
images, and what is different between similar-looking images. This signal
causes our model to discover attributes that do not change across viewpoint,
but do change across time, while ignoring nuisance variables such as
occlusions, motion blur, lighting and background. We demonstrate that this
representation can be used by a robot to directly mimic human poses without an
explicit correspondence, and that it can be used as a reward function within a
reinforcement learning algorithm. While representations are learned from an
unlabeled collection of task-related videos, robot behaviors such as pouring
are learned by watching a single 3rd-person demonstration by a human. Reward
functions obtained by following the human demonstrations under the learned
representation enable efficient reinforcement learning that is practical for
real-world robotic systems. Video results, open-source code and dataset are
available at https://sermanet.github.io/imitat
Quantitative abstraction theory
A quantitative theory of abstraction is presented. The central feature of this is a growth formula defining the number of abstractions which may be formed by an individual agent in a given context. Implications of the theory for artificial intelligence and cognitive psychology are explored. Its possible applications to the issue of implicit v. explicit learning are also discussed
Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence
An important challenge for safety in machine learning and artificial
intelligence systems is a~set of related failures involving specification
gaming, reward hacking, fragility to distributional shifts, and Goodhart's or
Campbell's law. This paper presents additional failure modes for interactions
within multi-agent systems that are closely related. These multi-agent failure
modes are more complex, more problematic, and less well understood than the
single-agent case, and are also already occurring, largely unnoticed. After
motivating the discussion with examples from poker-playing artificial
intelligence (AI), the paper explains why these failure modes are in some
senses unavoidable. Following this, the paper categorizes failure modes,
provides definitions, and cites examples for each of the modes: accidental
steering, coordination failures, adversarial misalignment, input spoofing and
filtering, and goal co-option or direct hacking. The paper then discusses how
extant literature on multi-agent AI fails to address these failure modes, and
identifies work which may be useful for the mitigation of these failure modes.Comment: 12 Pages, This version re-submitted to Big Data and Cognitive
Computing, Special Issue "Artificial Superintelligence: Coordination &
Strategy
- ā¦