How to Do Things with Deep Learning Code
- URL: http://arxiv.org/abs/2304.09406v1
- Date: Wed, 19 Apr 2023 03:46:12 GMT
- Title: How to Do Things with Deep Learning Code
- Authors: Minh Hua, Rita Raley
- Abstract summary: We draw attention to the means by which ordinary users might interact with, and even direct, the behavior of deep learning systems.
What is at stake is the possibility of achieving an informed sociotechnical consensus about the responsible applications of large language models.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The premise of this article is that a basic understanding of the composition
and functioning of large language models is critically urgent. To that end, we
extract a representational map of OpenAI's GPT-2 with what we articulate as two
classes of deep learning code, that which pertains to the model and that which
underwrites applications built around the model. We then verify this map
through case studies of two popular GPT-2 applications: the text adventure
game, AI Dungeon, and the language art project, This Word Does Not Exist. Such
an exercise allows us to test the potential of Critical Code Studies when the
object of study is deep learning code and to demonstrate the validity of code
as an analytical focus for researchers in the subfields of Critical Artificial
Intelligence and Critical Machine Learning Studies. More broadly, however, our
work draws attention to the means by which ordinary users might interact with,
and even direct, the behavior of deep learning systems, and by extension works
toward demystifying some of the auratic mystery of "AI." What is at stake is
the possibility of achieving an informed sociotechnical consensus about the
responsible applications of large language models, as well as a more expansive
sense of their creative capabilities-indeed, understanding how and where
engagement occurs allows all of us to become more active participants in the
development of machine learning systems.
Related papers
- Interpreting Latent Student Knowledge Representations in Programming Assignments [2.184775414778289]
We present an Information regularized Open-ended Item Response Theory model, which encourages latent student knowledge states to be interpretable.
In this paper, we show that InfoOIRT can both accurately generate student code and lead to interpretable student knowledge representations.
arXiv Detail & Related papers (2024-05-13T22:01:03Z) - Knowledge Plugins: Enhancing Large Language Models for Domain-Specific
Recommendations [50.81844184210381]
We propose a general paradigm that augments large language models with DOmain-specific KnowledgE to enhance their performance on practical applications, namely DOKE.
This paradigm relies on a domain knowledge extractor, working in three steps: 1) preparing effective knowledge for the task; 2) selecting the knowledge for each specific sample; and 3) expressing the knowledge in an LLM-understandable way.
arXiv Detail & Related papers (2023-11-16T07:09:38Z) - Detecting Any Human-Object Interaction Relationship: Universal HOI
Detector with Spatial Prompt Learning on Foundation Models [55.20626448358655]
This study explores the universal interaction recognition in an open-world setting through the use of Vision-Language (VL) foundation models and large language models (LLMs)
Our design includes an HO Prompt-guided Decoder (HOPD), facilitates the association of high-level relation representations in the foundation model with various HO pairs within the image.
For open-category interaction recognition, our method supports either of two input types: interaction phrase or interpretive sentence.
arXiv Detail & Related papers (2023-11-07T08:27:32Z) - Beyond Factuality: A Comprehensive Evaluation of Large Language Models
as Knowledge Generators [78.63553017938911]
Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks.
However, community concerns abound regarding the factuality and potential implications of using this uncensored knowledge.
We introduce CONNER, designed to evaluate generated knowledge from six important perspectives.
arXiv Detail & Related papers (2023-10-11T08:22:37Z) - How Generative AI models such as ChatGPT can be (Mis)Used in SPC
Practice, Education, and Research? An Exploratory Study [2.0841728192954663]
Generative Artificial Intelligence (AI) models have the potential to revolutionize Statistical Process Control (SPC) practice, learning, and research.
These tools are in the early stages of development and can be easily misused or misunderstood.
We explore ChatGPT's ability to provide code, explain basic concepts, and create knowledge related to SPC practice, learning, and research.
arXiv Detail & Related papers (2023-02-17T15:48:37Z) - Exploring External Knowledge for Accurate modeling of Visual and
Language Problems [2.7190267444272056]
This dissertation focuses on visual and language understanding which involves many challenging tasks.
The state-of-the-art methods for solving these problems usually involves only two parts: source data and target labels.
We developed a methodology that we can first extract external knowledge and then integrate it with the original models.
arXiv Detail & Related papers (2023-01-27T02:01:50Z) - Learning Action-Effect Dynamics for Hypothetical Vision-Language
Reasoning Task [50.72283841720014]
We propose a novel learning strategy that can improve reasoning about the effects of actions.
We demonstrate the effectiveness of our proposed approach and discuss its advantages over previous baselines in terms of performance, data efficiency, and generalization capability.
arXiv Detail & Related papers (2022-12-07T05:41:58Z) - Ten Quick Tips for Deep Learning in Biology [116.78436313026478]
Machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling.
Deep learning has become its own subfield of machine learning.
In the context of biological research, deep learning has been increasingly used to derive novel insights from high-dimensional biological data.
arXiv Detail & Related papers (2021-05-29T21:02:44Z) - Semi-Supervised Learning Approach to Discover Enterprise User Insights
from Feedback and Support [9.66491980663996]
We propose and developed an innovative Semi-Supervised Learning approach by utilizing Deep Learning and Topic Modeling.
This approach combines a BERT-based multiclassification algorithm through supervised learning combined with a novel Probabilistic and Semantic Hybrid Topic Inference (PSHTI) Model.
Our system enables mapping the top words to the self-help issues by utilizing domain knowledge about the product through web-crawling.
arXiv Detail & Related papers (2020-07-18T01:18:00Z) - Plausible Counterfactuals: Auditing Deep Learning Classifiers with
Realistic Adversarial Examples [84.8370546614042]
Black-box nature of Deep Learning models has posed unanswered questions about what they learn from data.
Generative Adversarial Network (GAN) and multi-objectives are used to furnish a plausible attack to the audited model.
Its utility is showcased within a human face classification task, unveiling the enormous potential of the proposed framework.
arXiv Detail & Related papers (2020-03-25T11:08:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.