306 research outputs found
Achilles Heels for AGI/ASI via Decision Theoretic Adversaries
As progress in AI continues to advance, it is crucial to know how advanced
systems will make choices and in what ways they may fail. Machines can already
outsmart humans in some domains, and understanding how to safely build ones
which may have capabilities at or above the human level is of particular
concern. One might suspect that artificially generally intelligent (AGI) and
artificially superintelligent (ASI) systems should be modeled as as something
which humans, by definition, can't reliably outsmart. As a challenge to this
assumption, this paper presents the Achilles Heel hypothesis which states that
even a potentially superintelligent system may nonetheless have stable
decision-theoretic delusions which cause them to make obviously irrational
decisions in adversarial settings. In a survey of relevant dilemmas and
paradoxes from the decision theory literature, a number of these potential
Achilles Heels are discussed in context of this hypothesis. Several novel
contributions are made toward understanding the ways in which these weaknesses
might be implanted into a system.Comment: Contact info for author at stephencasper.co
Smart City Analytics: Ensemble-Learned Prediction of Citizen Home Care
We present an ensemble learning method that predicts large increases in the
hours of home care received by citizens. The method is supervised, and uses
different ensembles of either linear (logistic regression) or non-linear
(random forests) classifiers. Experiments with data available from 2013 to 2017
for every citizen in Copenhagen receiving home care (27,775 citizens) show that
prediction can achieve state of the art performance as reported in similar
health related domains (AUC=0.715). We further find that competitive results
can be obtained by using limited information for training, which is very useful
when full records are not accessible or available. Smart city analytics does
not necessarily require full city records.
To our knowledge this preliminary study is the first to predict large
increases in home care for smart city analytics
Sequence Modelling For Analysing Student Interaction with Educational Systems
The analysis of log data generated by online educational systems is an
important task for improving the systems, and furthering our knowledge of how
students learn. This paper uses previously unseen log data from Edulab, the
largest provider of digital learning for mathematics in Denmark, to analyse the
sessions of its users, where 1.08 million student sessions are extracted from a
subset of their data. We propose to model students as a distribution of
different underlying student behaviours, where the sequence of actions from
each session belongs to an underlying student behaviour. We model student
behaviour as Markov chains, such that a student is modelled as a distribution
of Markov chains, which are estimated using a modified k-means clustering
algorithm. The resulting Markov chains are readily interpretable, and in a
qualitative analysis around 125,000 student sessions are identified as
exhibiting unproductive student behaviour. Based on our results this student
representation is promising, especially for educational systems offering many
different learning usages, and offers an alternative to common approaches like
modelling student behaviour as a single Markov chain often done in the
literature.Comment: The 10th International Conference on Educational Data Mining 201
Neural Speed Reading with Structural-Jump-LSTM
Recurrent neural networks (RNNs) can model natural language by sequentially
'reading' input tokens and outputting a distributed representation of each
token. Due to the sequential nature of RNNs, inference time is linearly
dependent on the input length, and all inputs are read regardless of their
importance. Efforts to speed up this inference, known as 'neural speed
reading', either ignore or skim over part of the input. We present
Structural-Jump-LSTM: the first neural speed reading model to both skip and
jump text during inference. The model consists of a standard LSTM and two
agents: one capable of skipping single words when reading, and one capable of
exploiting punctuation structure (sub-sentence separators (,:), sentence end
symbols (.!?), or end of text markers) to jump ahead after reading a word. A
comprehensive experimental evaluation of our model against all five
state-of-the-art neural reading models shows that Structural-Jump-LSTM achieves
the best overall floating point operations (FLOP) reduction (hence is faster),
while keeping the same accuracy or even improving it compared to a vanilla LSTM
that reads the whole text.Comment: 10 page
Modelling Sequential Music Track Skips using a Multi-RNN Approach
Modelling sequential music skips provides streaming companies the ability to
better understand the needs of the user base, resulting in a better user
experience by reducing the need to manually skip certain music tracks. This
paper describes the solution of the University of Copenhagen DIKU-IR team in
the 'Spotify Sequential Skip Prediction Challenge', where the task was to
predict the skip behaviour of the second half in a music listening session
conditioned on the first half. We model this task using a Multi-RNN approach
consisting of two distinct stacked recurrent neural networks, where one network
focuses on encoding the first half of the session and the other network focuses
on utilizing the encoding to make sequential skip predictions. The encoder
network is initialized by a learned session-wide music encoding, and both of
them utilize a learned track embedding. Our final model consists of a majority
voted ensemble of individually trained models, and ranked 2nd out of 45
participating teams in the competition with a mean average accuracy of 0.641
and an accuracy on the first skip prediction of 0.807. Our code is released at
https://github.com/Varyn/WSDM-challenge-2019-spotify.Comment: 4 page
The punch-drunk boxer and the battered wife: Gender and brain injury research.
This essay uses gender as a category of historical and sociological analysis to situate two populations-boxers and victims of domestic violence-in context and explain the temporal and ontological discrepancies between them as potential brain injury patients. In boxing, the question of brain injury and its sequelae were analyzed from 1928 on, often on profoundly somatic grounds. With domestic violence, in contrast, the question of brain injury and its sequelae appear to have been first examined only after 1990. Symptoms prior to that period were often cast as functional in specific psychiatric and psychological nomenclatures. We examine this chronological and epistemological disconnection between forms of violence that appear otherwise highly similar even if existing in profoundly different spaces
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Setting and motivation in the decision to participate: An approach to the engagement of diverse samples in mobile research.
Internet and mobile based research are powerful tools in the creation of large, cohort studies (eCohort). However, recent analysis indicates that an underrepresentation of minority and low income groups in these studies might exceed that found in traditional research [1-5]. In this report, we present findings from an experiment in research engagement using the Eureka Research Platform developed to enroll diverse populations in support of biomedical clinical research. This experiment involved the recruitment of African American and Latino participants in a smartphone based survey at a temporary, charitable, dental event sponsored, in part, by the research team, in order to explore the impact of setting and approach on recruitment outcomes. 211 participants enrolled including a significant representation of African Americans (51%) and Latinos (31%) and those with education levels at high school or less (37%). Interviews conducted after the study confirmed that our recruitment efforts within the context of a service event affected the decision to participate. While further research is necessary, this experiment holds promise for the engagement of underrepresented groups in research
Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents
Adversarial examples can be useful for identifying vulnerabilities in AI
systems before they are deployed. In reinforcement learning (RL), adversarial
policies can be developed by training an adversarial agent to minimize a target
agent's rewards. Prior work has studied black-box versions of these attacks
where the adversary only observes the world state and treats the target agent
as any other part of the environment. However, this does not take into account
additional structure in the problem. In this work, we study white-box
adversarial policies and show that having access to a target agent's internal
state can be useful for identifying its vulnerabilities. We make two
contributions. (1) We introduce white-box adversarial policies where an
attacker observes both a target's internal state and the world state at each
timestep. We formulate ways of using these policies to attack agents in
2-player games and text-generating language models. (2) We demonstrate that
these policies can achieve higher initial and asymptotic performance against a
target agent than black-box controls. Code is available at
https://github.com/thestephencasper/lm_white_box_attacksComment: Code is available at
https://github.com/thestephencasper/lm_white_box_attack
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