Rapid Mobile App Development for Generative AI Agents on MIT App Inventor
- URL: http://arxiv.org/abs/2405.01561v1
- Date: Mon, 1 Apr 2024 02:35:19 GMT
- Title: Rapid Mobile App Development for Generative AI Agents on MIT App Inventor
- Authors: Jaida Gao, Calab Su, Etai Miller, Kevin Lu, Yu Meng,
- Abstract summary: We present a methodology for the rapid development of AI agent applications using the development platform provided by MIT App Inventor.
We share the development journey of three distinct mobile applications: SynchroNet for fostering sustainable communities; ProductiviTeams for addressing procrastination; and iHELP for enhancing community safety.
- Score: 12.091916451641021
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The evolution of Artificial Intelligence (AI) stands as a pivotal force shaping our society, finding applications across diverse domains such as education, sustainability, and safety. Leveraging AI within mobile applications makes it easily accessible to the public, catalyzing its transformative potential. In this paper, we present a methodology for the rapid development of AI agent applications using the development platform provided by MIT App Inventor. To demonstrate its efficacy, we share the development journey of three distinct mobile applications: SynchroNet for fostering sustainable communities; ProductiviTeams for addressing procrastination; and iHELP for enhancing community safety. All three applications seamlessly integrate a spectrum of generative AI features, leveraging OpenAI APIs. Furthermore, we offer insights gleaned from overcoming challenges in integrating diverse tools and AI functionalities, aiming to inspire young developers to join our efforts in building practical AI agent applications.
Related papers
- Improving Performance of Commercially Available AI Products in a Multi-Agent Configuration [11.626057561212694]
Crowdbotics PRD AI is a tool for generating software requirements using AI.
GitHub Copilot is an AI pair-programming tool.
By sharing business requirements from PRD AI, we improve the code suggestion capabilities of GitHub Copilot by 13.8% and developer task success rate by 24.5%.
arXiv Detail & Related papers (2024-10-29T15:28:19Z) - OpenHands: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
We introduce OpenHands, a platform for the development of AI agents that interact with the world in similar ways to a human developer.
We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
arXiv Detail & Related papers (2024-07-23T17:50:43Z) - Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents.
We propose the Internet of Agents (IoA), a novel framework that addresses these limitations.
IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
arXiv Detail & Related papers (2024-07-09T17:33:24Z) - AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications [23.3982451133068]
We introduce AI2Apps, a Visual Integrated Development Environment (Visual IDE) with full-cycle capabilities that accelerates developers to build deployable AI agent Applications.
On one hand, AI2Apps integrates a comprehensive development toolkit ranging from a prototyping canvas and AI-assisted code editor to agent debugger, management system, and deployment tools all within a web-based graphical user interface.
On the other hand, AI2Apps visualizes reusable front-end and back-end code as intuitive drag-and-drop components.
arXiv Detail & Related papers (2024-04-07T10:02:09Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - Exploring the intersection of Generative AI and Software Development [0.0]
The synergy between generative AI and Software Engineering emerges as a transformative frontier.
This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development.
It serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering.
arXiv Detail & Related papers (2023-12-21T19:23:23Z) - From Generative AI to Generative Internet of Things: Fundamentals,
Framework, and Outlooks [82.964958051535]
Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making.
By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society.
arXiv Detail & Related papers (2023-10-27T02:58:11Z) - The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI)
We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents.
We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
arXiv Detail & Related papers (2023-09-14T17:12:03Z) - Towards Real Smart Apps: Investigating Human-AI Interactions in
Smartphone On-Device AI Apps [17.630597106970466]
A good interaction design is important to make an AI feature usable and understandable.
Existing guidelines and tools either do not cover AI features or consider mobile apps.
We conducted the first empirical study to explore user-AI-interaction in mobile apps.
arXiv Detail & Related papers (2023-07-03T05:04:34Z) - An Empirical Study of AI Techniques in Mobile Applications [10.43634556488264]
We conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps.
Our study has strong implications for AI app developers, users, and AI R&D.
arXiv Detail & Related papers (2022-12-03T15:31:34Z) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z)
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.