3,949 research outputs found
What should a robot learn from an infant? Mechanisms of action interpretation and observational learning in infancy
The paper provides a summary of our
recent research on preverbal infants (using
violation-of-expectation and observational
learning paradigms) demonstrating that one-year-olds interpret and draw systematic
inferences about other’s goal-directed actions,
and can rely on such inferences when imitating
other’s actions or emulating their goals. To
account for these findings it is proposed that one-year-olds apply a non-mentalistic action
interpretational system, the ’teleological stance’
that represents actions by relating relevant
aspects of reality (action, goal-state, and
situational constraints) through the principle of
rational action, which assumes that actions
function to realize goal-states by the most
efficient means available in the actor’s situation.
The relevance of these research findings and the
proposed theoretical model for how to realize the
goal of epigenetic robotics of building a ’socially
relevant’ humanoid robot is discussed
PINTO: Faithful Language Reasoning Using Prompt-Generated Rationales
Neural language models (LMs) have achieved impressive results on various
language-based reasoning tasks by utilizing latent knowledge encoded in their
own pretrained parameters. To make this reasoning process more explicit, recent
works retrieve a rationalizing LM's internal knowledge by training or prompting
it to generate free-text rationales, which can be used to guide task
predictions made by either the same LM or a separate reasoning LM. However,
rationalizing LMs require expensive rationale annotation and/or computation,
without any assurance that their generated rationales improve LM task
performance or faithfully reflect LM decision-making. In this paper, we propose
PINTO, an LM pipeline that rationalizes via prompt-based learning, and learns
to faithfully reason over rationales via counterfactual regularization. First,
PINTO maps out a suitable reasoning process for the task input by prompting a
frozen rationalizing LM to generate a free-text rationale. Second, PINTO's
reasoning LM is fine-tuned to solve the task using the generated rationale as
context, while regularized to output less confident predictions when the
rationale is perturbed. Across four datasets, we show that PINTO significantly
improves the generalization ability of the reasoning LM, yielding higher
performance on both in-distribution and out-of-distribution test sets. Also, we
find that PINTO's rationales are more faithful to its task predictions than
those generated by competitive baselines.Comment: 19 pages, 6 figures, preprin
How Spinal Neural Networks Reduce Discrepancies between Motor Intention and Motor Realization
This paper attempts a rational, step-by-step reconstruction of many aspects of the mammalian neural circuitry known to be involved in the spinal cord's regulation of opposing muscles acting on skeletal segments. Mathematical analyses and local circuit simulations based on neural membrane equations are used to clarify the behavioral function of five fundamental cell types, their complex connectivities, and their physiological actions. These cell types are: α-MNs, γ-MNs, IaINs, IbINs, and Renshaw cells. It is shown that many of the complexities of spinal circuitry are necessary to ensure near invariant realization of motor intentions when descending signals of two basic types independently vary over large ranges of magnitude and rate of change. Because these two types of signal afford independent control, or Factorization, of muscle LEngth and muscle TEnsion, our construction was named the FLETE model (Bullock and Grossberg, 1988b, 1989). The present paper significantly extends the range of experimental data encompassed by this evolving model.National Science Foundation (IRI-87-16960, IRI-90-24877); Instituto Tecnológico y de Estudios Superiores de Monterre
Sum-of-Parts Models: Faithful Attributions for Groups of Features
An explanation of a machine learning model is considered "faithful" if it
accurately reflects the model's decision-making process. However, explanations
such as feature attributions for deep learning are not guaranteed to be
faithful, and can produce potentially misleading interpretations. In this work,
we develop Sum-of-Parts (SOP), a class of models whose predictions come with
grouped feature attributions that are faithful-by-construction. This model
decomposes a prediction into an interpretable sum of scores, each of which is
directly attributable to a sparse group of features. We evaluate SOP on
benchmarks with standard interpretability metrics, and in a case study, we use
the faithful explanations from SOP to help astrophysicists discover new
knowledge about galaxy formation
Cultures of Compliance
There has been a cultural turn in discussion and debates about the promise of corporate compliance efforts. These efforts are occurring quickly, without great confidence in their efficacy. Thus the interest in culture. This article explores what a culture of compliance means and why it is so hard to achieve. The dark side that enables non-compliance in organizations is powerful and often hidden from view, working via scripts that rationalize or normalize, denigrations of regulation, and celebrations of beliefs and attitudes that bring with them compliance dangers. The article addresses how both culture and compliance should be judged by those wishing for better corporate behavior
Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods
Saliency maps can explain a neural model's predictions by identifying
important input features. They are difficult to interpret for laypeople,
especially for instances with many features. In order to make them more
accessible, we formalize the underexplored task of translating saliency maps
into natural language and compare methods that address two key challenges of
this approach -- what and how to verbalize. In both automatic and human
evaluation setups, using token-level attributions from text classification
tasks, we compare two novel methods (search-based and instruction-based
verbalizations) against conventional feature importance representations
(heatmap visualizations and extractive rationales), measuring simulatability,
faithfulness, helpfulness and ease of understanding. Instructing GPT-3.5 to
generate saliency map verbalizations yields plausible explanations which
include associations, abstractive summarization and commonsense reasoning,
achieving by far the highest human ratings, but they are not faithfully
capturing numeric information and are inconsistent in their interpretation of
the task. In comparison, our search-based, model-free verbalization approach
efficiently completes templated verbalizations, is faithful by design, but
falls short in helpfulness and simulatability. Our results suggest that
saliency map verbalization makes feature attribution explanations more
comprehensible and less cognitively challenging to humans than conventional
representations.Comment: ACL 2023 Workshop on Natural Language Reasoning and Structured
Explanations (NLRSE
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