Search Results for author: Pattie Maes

Found 26 papers, 12 papers with code

Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task

no code implementations10 Jun 2025 Nataliya Kosmyna, Eugene Hauptmann, Ye Tong Yuan, Jessica Situ, Xian-Hao Liao, Ashly Vivian Beresnitzky, Iris Braunstein, Pattie Maes

In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM).

EEG

Discovering Interpretable Concepts in Large Generative Music Models

no code implementations18 May 2025 Nikhil Singh, Manuel Cherep, Pattie Maes

The fidelity with which neural networks can now generate content such as music presents a scientific opportunity: these systems appear to have learned implicit theories of the structure of such content through statistical learning alone.

Investigating Affective Use and Emotional Well-being on ChatGPT

1 code implementation4 Apr 2025 Jason Phang, Michael Lampe, Lama Ahmad, Sandhini Agarwal, Cathy Mengying Fang, Auren R. Liu, Valdemar Danry, Eunhae Lee, Samantha W. T. Chan, Pat Pataranutaporn, Pattie Maes

As AI chatbots see increased adoption and integration into everyday life, questions have been raised about the potential impact of human-like or anthropomorphic AI on users.

Privacy Preserving

Resonance: Drawing from Memories to Imagine Positive Futures through AI-Augmented Journaling

no code implementations31 Mar 2025 Wazeer Zulfikar, Treyden Chiaravalloti, Jocelyn Shen, Rosalind Picard, Pattie Maes

People inherently use experiences of their past while imagining their future, a capability that plays a crucial role in mental health.

AI persuading AI vs AI persuading Humans: LLMs' Differential Effectiveness in Promoting Pro-Environmental Behavior

no code implementations3 Mar 2025 Alexander Doudkin, Pat Pataranutaporn, Pattie Maes

Pro-environmental behavior (PEB) is vital to combat climate change, yet turning awareness into intention and action remains elusive.

Persuasion Strategies

OceanChat: The Effect of Virtual Conversational AI Agents on Sustainable Attitude and Behavior Change

no code implementations5 Feb 2025 Pat Pataranutaporn, Alexander Doudkin, Pattie Maes

Through a between-subjects experiment (N=900), we compared three conditions: (1) Static Scientific Information, providing conventional environmental education through text and images; (2) Static Character Narrative, featuring first-person storytelling from 3D-rendered marine creatures; and (3) Conversational Character Narrative, enabling real-time dialogue with AI-powered marine characters.

Can AI Solve the Peer Review Crisis? A Large Scale Cross Model Experiment of LLMs' Performance and Biases in Evaluating over 1000 Economics Papers

no code implementations31 Jan 2025 Pat Pataranutaporn, Nattavudh Powdthavee, Chayapatr Achiwaranguprok, Pattie Maes

This study examines the potential of large language models (LLMs) to augment the academic peer review process by reliably evaluating the quality of economics research without introducing systematic bias.

Language Modeling Language Modelling +1

Algorithmic Inheritance: Surname Bias in AI Decisions Reinforces Intergenerational Inequality

1 code implementation23 Jan 2025 Pat Pataranutaporn, Nattavudh Powdthavee, Pattie Maes

Our findings show that elite surnames consistently increase AI-generated perceptions of power, intelligence, and wealth, which in turn influence AI-driven decisions in high-stakes contexts.

Decision Making Fairness

Synthetic Human Memories: AI-Edited Images and Videos Can Implant False Memories and Distort Recollection

no code implementations13 Sep 2024 Pat Pataranutaporn, Chayapatr Archiwaranguprok, Samantha W. T. Chan, Elizabeth Loftus, Pattie Maes

AI-edited visuals significantly increased false recollections, with AI-generated videos of AI-edited images having the strongest effect (2. 05x compared to control).

Super-intelligence or Superstition? Exploring Psychological Factors Influencing Belief in AI Predictions about Personal Behavior

2 code implementations13 Aug 2024 Eunhae Lee, Pat Pataranutaporn, Judith Amores, Pattie Maes

Through an experiment with 238 participants, we examined how cognitive style, paranormal beliefs, AI attitudes, personality traits, and other factors affect perceived validity, reliability, usefulness, and personalization of predictions from different sources.

Deceptive AI systems that give explanations are more convincing than honest AI systems and can amplify belief in misinformation

no code implementations31 Jul 2024 Valdemar Danry, Pat Pataranutaporn, Matthew Groh, Ziv Epstein, Pattie Maes

Advanced Artificial Intelligence (AI) systems, specifically large language models (LLMs), have the capability to generate not just misinformation, but also deceptive explanations that can justify and propagate false information and erode trust in the truth.

Logical Reasoning Misinformation +1

Future You: A Conversation with an AI-Generated Future Self Reduces Anxiety, Negative Emotions, and Increases Future Self-Continuity

no code implementations21 May 2024 Pat Pataranutaporn, Kavin Winson, Peggy Yin, Auttasak Lapapirojn, Pichayoot Ouppaphan, Monchai Lertsutthiwong, Pattie Maes, Hal Hershfield

We introduce "Future You," an interactive, brief, single-session, digital chat intervention designed to improve future self-continuity--the degree of connection an individual feels with a temporally distant future self--a characteristic that is positively related to mental health and wellbeing.

Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text

1 code implementation26 Jun 2021 Pulkit Tandon, Shubham Chandak, Pat Pataranutaporn, Yimeng Liu, Anesu M. Mapuranga, Pattie Maes, Tsachy Weissman, Misha Sra

Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure.

Talking Face Generation Talking Head Generation +2

Pretrained Encoders are All You Need

1 code implementation ICML Workshop URL 2021 Mina Khan, P Srivatsa, Advait Rane, Shriram Chenniappa, Rishabh Anand, Sherjil Ozair, Pattie Maes

Data-efficiency and generalization are key challenges in deep learning and deep reinforcement learning as many models are trained on large-scale, domain-specific, and expensive-to-label datasets.

All Contrastive Learning +4

Personalizing Pre-trained Models

no code implementations2 Jun 2021 Mina Khan, P Srivatsa, Advait Rane, Shriram Chenniappa, Asadali Hazariwala, Pattie Maes

Self-supervised or weakly supervised models trained on large-scale datasets have shown sample-efficient transfer to diverse datasets in few-shot settings.

Continual Learning Few-Shot Learning +2

PAL: Intelligence Augmentation using Egocentric Visual Context Detection

no code implementations22 May 2021 Mina Khan, Pattie Maes

We created a wearable system, called PAL, for wearable, personalized, and privacy-preserving egocentric visual context detection.

Clustering Deep Learning +2

Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings

1 code implementation21 Apr 2021 Utkarsh Sarawgi, Rishab Khincha, Wazeer Zulfikar, Satrajit Ghosh, Pattie Maes

Reliability of machine learning (ML) systems is crucial in safety-critical applications such as healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of ML systems in deployment.

Prediction Intervals

Robustness to Missing Features using Hierarchical Clustering with Split Neural Networks

1 code implementation19 Nov 2020 Rishab Khincha, Utkarsh Sarawgi, Wazeer Zulfikar, Pattie Maes

In this work, we propose a simple yet effective approach that clusters similar input features together using hierarchical clustering and then trains proportionately split neural networks with a joint loss.

Attribute Clustering +1

Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia

1 code implementation3 Oct 2020 Utkarsh Sarawgi, Wazeer Zulfikar, Rishab Khincha, Pattie Maes

Reliability in Neural Networks (NNs) is crucial in safety-critical applications like healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of NNs in deployment.

severity prediction

Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensembles

1 code implementation25 Sep 2020 Utkarsh Sarawgi, Wazeer Zulfikar, Rishab Khincha, Pattie Maes

Our work further demonstrates its applicability in a multi-modal setting using a benchmark Alzheimer's dataset and also shows how deep split ensembles can highlight hidden modality-specific biases.

Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity

1 code implementation30 Aug 2020 Utkarsh Sarawgi, Wazeer Zulfikar, Nouran Soliman, Pattie Maes

Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83. 3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4. 60 for MMSE score regression.

Transfer Learning

Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG

2 code implementations25 Nov 2018 Abhay Koushik, Judith Amores, Pattie Maes

We present the first real-time sleep staging system that uses deep learning without the need for servers in a smartphone application for a wearable EEG.

EEG General Classification +1

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