Search Results for author: William Agnew

Found 15 papers, 4 papers with code

The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation

no code implementations5 Feb 2025 Martin Mundt, Anaelia Ovalle, Felix Friedrich, A Pranav, Subarnaduti Paul, Manuel Brack, Kristian Kersting, William Agnew

In a widely popular analogy by Turing Award Laureate Yann LeCun, machine intelligence has been compared to cake - where unsupervised learning forms the base, supervised learning adds the icing, and reinforcement learning is the cherry on top.

Sound Check: Auditing Audio Datasets

no code implementations17 Oct 2024 William Agnew, Julia Barnett, Annie Chu, Rachel Hong, Michael Feffer, Robin Netzorg, Harry H. Jiang, Ezra Awumey, Sauvik Das

Generative audio models are rapidly advancing in both capabilities and public utilization -- several powerful generative audio models have readily available open weights, and some tech companies have released high quality generative audio products.

Data Defenses Against Large Language Models

1 code implementation17 Oct 2024 William Agnew, Harry H. Jiang, Cella Sum, Maarten Sap, Sauvik Das

Large language models excel at performing inference over text to extract information, summarize information, or generate additional text.

Ethics

'Simulacrum of Stories': Examining Large Language Models as Qualitative Research Participants

no code implementations28 Sep 2024 Shivani Kapania, William Agnew, Motahhare Eslami, Hoda Heidari, Sarah Fox

The recent excitement around generative models has sparked a wave of proposals suggesting the replacement of human participation and labor in research and development--e. g., through surveys, experiments, and interviews--with synthetic research data generated by large language models (LLMs).

Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp

1 code implementation13 May 2024 Rachel Hong, William Agnew, Tadayoshi Kohno, Jamie Morgenstern

As training datasets become increasingly drawn from unstructured, uncontrolled environments such as the web, researchers and industry practitioners have increasingly relied upon data filtering techniques to "filter out the noise" of web-scraped data.

Image Classification Zero-Shot Image Classification

The Surveillance AI Pipeline

no code implementations26 Sep 2023 Pratyusha Ria Kalluri, William Agnew, Myra Cheng, Kentrell Owens, Luca Soldaini, Abeba Birhane

Moreover, the majority of these technologies specifically enable extracting data about human bodies and body parts.

Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms

no code implementations15 Jul 2023 Organizers Of QueerInAI, Nathan Dennler, Anaelia Ovalle, Ashwin Singh, Luca Soldaini, Arjun Subramonian, Huy Tu, William Agnew, Avijit Ghosh, Kyra Yee, Irene Font Peradejordi, Zeerak Talat, Mayra Russo, Jess de Jesus de Pinho Pinhal

However, these auditing processes have been criticized for their failure to integrate the knowledge of marginalized communities and consider the power dynamics between auditors and the communities.

Robots Enact Malignant Stereotypes

no code implementations23 Jul 2022 Andrew Hundt, William Agnew, Vicky Zeng, Severin Kacianka, Matthew Gombolay

Stereotypes, bias, and discrimination have been extensively documented in Machine Learning (ML) methods such as Computer Vision (CV) [18, 80], Natural Language Processing (NLP) [6], or both, in the case of large image and caption models such as OpenAI CLIP [14].

Bias Detection Gender Bias Detection +4

Rebuilding Trust: Queer in AI Approach to Artificial Intelligence Risk Management

no code implementations21 Sep 2021 Ashwin, William Agnew, Umut Pajaro, Hetvi Jethwani, Arjun Subramonian

Trustworthy artificial intelligence (AI) has become an important topic because trust in AI systems and their creators has been lost.

Diversity Fairness +1

The Values Encoded in Machine Learning Research

1 code implementation NeurIPS 2021 Abeba Birhane, Pratyusha Kalluri, Dallas Card, William Agnew, Ravit Dotan, Michelle Bao

We present extensive textual evidence and identify key themes in the definitions and operationalization of these values.

BIG-bench Machine Learning

Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity

1 code implementation28 Sep 2020 William Agnew, Christopher Xie, Aaron Walsman, Octavian Murad, Caelen Wang, Pedro Domingos, Siddhartha Srinivasa

By using these priors over the physical properties of objects, our system improves reconstruction quality not just by standard visual metrics, but also performance of model-based control on a variety of robotics manipulation tasks in challenging, cluttered environments.

3D Object Reconstruction 3D Reconstruction +1

Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning

no code implementations3 Mar 2020 William Agnew, Pedro Domingos

Current deep reinforcement learning (RL) approaches incorporate minimal prior knowledge about the environment, limiting computational and sample efficiency.

Atari Games Deep Reinforcement Learning +6

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