paper-with-me

Papers

Exploring the Innovation Opportunities for Pre-trained Models

2025-05-21 · Minjung Park, Jodi Forlizzi, John Zimmerman

Innovators transform the world by understanding where services are successfully meeting customers' needs and then using this knowledge to identify failsafe opportunities for innovation. Pre-trained models have changed the AI innovation landscape, making it faster and easier to create new AI products and services. Understanding where pre-trained models are successful is critical for supporting AI innovation. Unfortunately, the hype cycle surrounding pre-trained models makes it hard to know where AI can really be successful. To address this, we investigated pre-trained model applications developed by HCI researchers as a proxy for commercially successful applications. The research applications demonstrate technical capabilities, address real user needs, and avoid ethical challenges. Using an artifact analysis approach, we categorized capabilities, opportunity domains, data types, and emerging interaction design patterns, uncovering some of the opportunity space for innovation with pre-trained models.

📄 PDF Abstract BibTeX arXiv:2505.15790

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Attention 설명 없음
RAdam 설명 없음
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Graph Self-Attention 설명 없음
HypE Hyperboloid Embeddings (HypE) is a novel self-supervised dynamic reasoning framework, that utilizes positive first-order existential queries on a KG to learn representations of…

Similar Papers 제목 키워드 기반

A computational model and tool for generating more novel opportunities in professional innovation processes

2025-10-23 · Neil Maiden, Konstantinos Zachos, James Lockerbie, Kostas Petrianakis 외 arxiv

This paper presents a new computational model of creative outcomes, informed by creativity theories and techniques, which was implemented to generate more novel opportunities for innovation projects. The model implemente…

AI-Researcher: Autonomous Scientific Innovation

2025-05-24 · Jiabin Tang, Lianghao Xia, Zhonghang Li, Chao Huang

The powerful reasoning capabilities of Large Language Models (LLMs) in mathematics and coding, combined with their ability to automate complex tasks through agentic frameworks, present unprecedented opportunities for acc…

scientific discovery

Exploring ChatGPT for Next-generation Information Retrieval: Opportunities and Challenges

2024-02-17 · Yizheng Huang, Jimmy Huang

The rapid advancement of artificial intelligence (AI) has highlighted ChatGPT as a pivotal technology in the field of information retrieval (IR). Distinguished from its predecessors, ChatGPT offers significant benefits t…

AttributeInformation RetrievalRetrieval

FinXplore: An Adaptive Deep Reinforcement Learning Framework for Balancing and Discovering Investment Opportunities

2025-09-05 · Himanshu Choudhary, Arishi Orra, Manoj Thakur arxiv

Portfolio optimization is essential for balancing risk and return in financial decision-making. Deep Reinforcement Learning (DRL) has stood out as a cutting-edge tool for portfolio optimization that learns dynamic asset …

Reinforcement LearningPortfolio Optimization

Large Language Models for Computer-Aided Design: A Survey

2025-05-13 · Licheng Zhang, Bach Le, Naveed Akhtar, Siew-Kei Lam 외

Large Language Models (LLMs) have seen rapid advancements in recent years, with models like ChatGPT and DeepSeek, showcasing their remarkable capabilities across diverse domains. While substantial research has been condu…

Survey