393 research outputs found
Towards Evaluating AI Systems for Moral Status Using Self-Reports
As AI systems become more advanced and widely deployed, there will likely be
increasing debate over whether AI systems could have conscious experiences,
desires, or other states of potential moral significance. It is important to
inform these discussions with empirical evidence to the extent possible. We
argue that under the right circumstances, self-reports, or an AI system's
statements about its own internal states, could provide an avenue for
investigating whether AI systems have states of moral significance.
Self-reports are the main way such states are assessed in humans ("Are you in
pain?"), but self-reports from current systems like large language models are
spurious for many reasons (e.g. often just reflecting what humans would say).
To make self-reports more appropriate for this purpose, we propose to train
models to answer many kinds of questions about themselves with known answers,
while avoiding or limiting training incentives that bias self-reports. The hope
of this approach is that models will develop introspection-like capabilities,
and that these capabilities will generalize to questions about states of moral
significance. We then propose methods for assessing the extent to which these
techniques have succeeded: evaluating self-report consistency across contexts
and between similar models, measuring the confidence and resilience of models'
self-reports, and using interpretability to corroborate self-reports. We also
discuss challenges for our approach, from philosophical difficulties in
interpreting self-reports to technical reasons why our proposal might fail. We
hope our discussion inspires philosophers and AI researchers to criticize and
improve our proposed methodology, as well as to run experiments to test whether
self-reports can be made reliable enough to provide information about states of
moral significance
Learning Visual Reasoning Without Strong Priors
Achieving artificial visual reasoning - the ability to answer image-related
questions which require a multi-step, high-level process - is an important step
towards artificial general intelligence. This multi-modal task requires
learning a question-dependent, structured reasoning process over images from
language. Standard deep learning approaches tend to exploit biases in the data
rather than learn this underlying structure, while leading methods learn to
visually reason successfully but are hand-crafted for reasoning. We show that a
general-purpose, Conditional Batch Normalization approach achieves
state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4%
error rate. We outperform the next best end-to-end method (4.5%) and even
methods that use extra supervision (3.1%). We probe our model to shed light on
how it reasons, showing it has learned a question-dependent, multi-step
process. Previous work has operated under the assumption that visual reasoning
calls for a specialized architecture, but we show that a general architecture
with proper conditioning can learn to visually reason effectively.Comment: Full AAAI 2018 paper is at arXiv:1709.07871. Presented at ICML 2017's
Machine Learning in Speech and Language Processing Workshop. Code is at
http://github.com/ethanjperez/fil
FiLM: Visual Reasoning with a General Conditioning Layer
We introduce a general-purpose conditioning method for neural networks called
FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network
computation via a simple, feature-wise affine transformation based on
conditioning information. We show that FiLM layers are highly effective for
visual reasoning - answering image-related questions which require a
multi-step, high-level process - a task which has proven difficult for standard
deep learning methods that do not explicitly model reasoning. Specifically, we
show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error
for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are
robust to ablations and architectural modifications, and 4) generalize well to
challenging, new data from few examples or even zero-shot.Comment: AAAI 2018. Code available at http://github.com/ethanjperez/film .
Extends arXiv:1707.0301
Confirming Presence and Mobilization of Partner Quality Genes in Sinorhizobium meliloti
Mutualism is an interaction in which two organisms benefit from each other. The symbiotic relationship between the bacterium Sinorhizobium meliloti and the plant Medicago truncatula is dictated by the bacterial plasmids. The Heath lab has a collection of 191 Sinorhizobium meliloti strains with different symbiotic plasmids that vary across populations (Riley et al., 2022). Each strain can be a better or worse partner for its plant host in symbiosis with the variation in fitness being called partner quality (Fig. 1) These traits can be traced back to the genetic elements that underlie this effect (Batstone et al., These genetic elements can be found in gene clusters that are gained or loss across strains (Fig. 1; Riaz, Sosa Marquez, in prep.).
We need assurances of these important and variable clusters presence to understand their mechanism of gene movemen
HoME: a Household Multimodal Environment
We introduce HoME: a Household Multimodal Environment for artificial agents
to learn from vision, audio, semantics, physics, and interaction with objects
and other agents, all within a realistic context. HoME integrates over 45,000
diverse 3D house layouts based on the SUNCG dataset, a scale which may
facilitate learning, generalization, and transfer. HoME is an open-source,
OpenAI Gym-compatible platform extensible to tasks in reinforcement learning,
language grounding, sound-based navigation, robotics, multi-agent learning, and
more. We hope HoME better enables artificial agents to learn as humans do: in
an interactive, multimodal, and richly contextualized setting.Comment: Presented at NIPS 2017's Visually-Grounded Interaction and Language
Worksho
Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning
Reinforcement learning (RL) requires either manually specifying a reward
function, which is often infeasible, or learning a reward model from a large
amount of human feedback, which is often very expensive. We study a more
sample-efficient alternative: using pretrained vision-language models (VLMs) as
zero-shot reward models (RMs) to specify tasks via natural language. We propose
a natural and general approach to using VLMs as reward models, which we call
VLM-RMs. We use VLM-RMs based on CLIP to train a MuJoCo humanoid to learn
complex tasks without a manually specified reward function, such as kneeling,
doing the splits, and sitting in a lotus position. For each of these tasks, we
only provide a single sentence text prompt describing the desired task with
minimal prompt engineering. We provide videos of the trained agents at:
https://sites.google.com/view/vlm-rm. We can improve performance by providing a
second ``baseline'' prompt and projecting out parts of the CLIP embedding space
irrelevant to distinguish between goal and baseline. Further, we find a strong
scaling effect for VLM-RMs: larger VLMs trained with more compute and data are
better reward models. The failure modes of VLM-RMs we encountered are all
related to known capability limitations of current VLMs, such as limited
spatial reasoning ability or visually unrealistic environments that are far
off-distribution for the VLM. We find that VLM-RMs are remarkably robust as
long as the VLM is large enough. This suggests that future VLMs will become
more and more useful reward models for a wide range of RL applications
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