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Found 26 papers, 12 papers with code
Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
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).
Discovering Interpretable Concepts in Large Generative Music Models
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
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.
Resonance: Drawing from Memories to Imagine Positive Futures through AI-Augmented Journaling
People inherently use experiences of their past while imagining their future, a capability that plays a crucial role in mental health.
NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences
Generative AI is transforming education by enabling personalized, on-demand learning experiences.
AI persuading AI vs AI persuading Humans: LLMs' Differential Effectiveness in Promoting Pro-Environmental Behavior
Pro-environmental behavior (PEB) is vital to combat climate change, yet turning awareness into intention and action remains elusive.
OceanChat: The Effect of Virtual Conversational AI Agents on Sustainable Attitude and Behavior Change
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
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.
Algorithmic Inheritance: Surname Bias in AI Decisions Reinforces Intergenerational Inequality
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.
Synthetic Human Memories: AI-Edited Images and Videos Can Implant False Memories and Distort Recollection
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
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.
People over trust AI-generated medical responses and view them to be as valid as doctors, despite low accuracy
This problematic reaction was comparable if not more to the reaction they displayed towards doctors' responses.
Conversational AI Powered by Large Language Models Amplifies False Memories in Witness Interviews
However, confidence in these false memories remained higher than the control after one week.
Deceptive AI systems that give explanations are more convincing than honest AI systems and can amplify belief in misinformation
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.
Analyzing Speech Motor Movement using Surface Electromyography in Minimally Verbal Adults with Autism Spectrum Disorder
Adults who are minimally verbal with autism spectrum disorder (mvASD) have pronounced speech difficulties linked to impaired motor skills.
Future You: A Conversation with an AI-Generated Future Self Reduces Anxiety, Negative Emotions, and Increases Future Self-Continuity
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
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.
Pretrained Encoders are All You Need
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.
Personalizing Pre-trained Models
Self-supervised or weakly supervised models trained on large-scale datasets have shown sample-efficient transfer to diverse datasets in few-shot settings.
PAL: Intelligence Augmentation using Egocentric Visual Context Detection
We created a wearable system, called PAL, for wearable, personalized, and privacy-preserving egocentric visual context detection.
Uncertainty-Aware Boosted Ensembling in Multi-Modal Settings
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.
Robustness to Missing Features using Hierarchical Clustering with Split Neural Networks
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.
Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia
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.
Why have a Unified Predictive Uncertainty? Disentangling it using Deep Split Ensembles
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
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.
Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG
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.
