OpenAI GPT-4 Vision AI technology Top Builders

Explore the top contributors showcasing the highest number of OpenAI GPT-4 Vision AI technology app submissions within our community.

GPT-4V(ision)

Discover the groundbreaking integration of GPT-4 Vision, an innovative addition to the GPT-4 series. Witness AI's transformative leap into the visual realm, elevating its capabilities across diverse domains.

General
Release date	September 25, 2023
Author	OpenAI
Documentation	OpenAI's Guide
Type	AI Model with Visual Understanding

Overview

GPT-4 Vision seamlessly integrates visual interpretation into the GPT-4 framework, expanding the model's capabilities beyond language understanding. It empowers AI to process diverse visual data alongside textual inputs.

Visionary Integration

GPT-4 Vision blends language reasoning with image analysis, introducing unparalleled capabilities to AI systems.

Capabilities

Discover the transformative abilities of GPT-4 Vision across various domains and tasks:

1. Visual Understanding

Object Detection

Accurate identification and analysis of objects within images, showcasing proficiency in comprehensive image understanding.

Visual Question Answering

Adept handling of follow-up questions based on visual prompts, offering insightful information and suggestions.

2. Multifaceted Processing

Multiple Condition Processing

Interpreting and responding to multiple instructions simultaneously, demonstrating versatility in handling complex queries.

Data Analysis

Enhanced data comprehension and analysis, providing valuable insights when presented with visual data, including graphs and charts.

3. Language and Visual Fusion

Text Deciphering

Proficiency in deciphering handwritten notes and challenging text, maintaining high accuracy even in difficult scenarios.

Addressing Challenges

Mitigating Limitations

While pioneering in vision integration, GPT-4 faces inherent challenges:

Reliability Issues: Occasional inaccuracies or hallucinations in visual interpretations.
Overreliance Concerns: Potential for users to overly trust inaccurate responses.
Complex Reasoning: Challenges in nuanced, multifaceted visual tasks.

Safety Measures

OpenAI implements safety measures, including safety reward signals during training and reinforcement learning, to mitigate risks associated with inaccurate or unsafe outputs.

GPT-4 Vision Resources

Explore GPT-4 Vision's detailed documentation and quick start guides for insights, usage guidelines, and safety measures:

Official Documentation: GPT-4 Vision Documentation
Quick Start Guide: GPT-4 Vision Quick Start
GPT-4Vision system card

GPT-4 Vision Tutorials

Crafting Engaging Stories with AI: Building an Interactive Media App

👉 Discover more GPT-4 Vision Tutorials on lablab.ai

Edit on Github

OpenAI GPT-4 Vision AI technology Hackathon projects

Discover innovative solutions crafted with OpenAI GPT-4 Vision AI technology, developed by our community members during our engaging hackathons.

PrepAlly

PrepAlly transforms coding interview prep with cutting-edge AI, offering real-time feedback, voice-guided support, and immersive, personalized insights. Using Aria for natural interactions and ElevenLabs for lifelike feedback, PrepAlly builds candidates' confidence and sharpens performance. Open-Source and Community-Driven, it’s built on a modern tech stack and deployed on Vercel for seamless global access. PrepAlly empowers candidates to go beyond routine practice, making each session a powerful step toward interview mastery. Prep smarter, perform stronger, and tackle every coding challenge with clarity and confidence.

movie-travel-SanFrancisco-Edition

Using LangGrpah and OpenAI latest models and function calling capability, we created an agent that interacts with the user over a very interesting dataset maintained by the city of San Francisco government about all the locations in the city that have been used by film and TV projects since the early 20th century. Our goal is to inform tourists and locals alike about all the interesting history associated with different landmarks and streets in the city as it relates to visual arts ad culture. We envision users asking the Agent questions about different categories of films shot in SF; Asking the agent to provide an itinerary customized to the user's geo-location and interests. As of now, we have a working prototype that offers information based on the dataset. However, the geo-location feature has not been completely implemented yet.

mediflow

MediFlow is an AI-powered system designed to transform patient onboarding and diagnosis in healthcare. It addresses key challenges like long wait times, inefficient onboarding, limited pre-appointment data for doctors, and difficulty prioritizing urgent cases. By integrating AI and streamlining workflows, it enhances both patient experience and healthcare delivery. The system's core feature is an AI-driven Interactive Voice Response (IVR) system, which guides patients through the onboarding process, gathers essential data, and directs them to appropriate departments. This personalized experience improves data collection by adapting to individual medical histories and needs. MediFlow also features an automated diagnosis system. Its AI algorithms analyze patient symptoms and medical history to generate an initial diagnosis and a detailed report for doctors. This enables more personalized, informed care before the appointment. Patients benefit from 24/7 access to healthcare guidance, reduced appointment wait times, and a more efficient, personalized experience. Healthcare providers gain comprehensive pre-appointment information, improving decision-making and care planning. MediFlow also helps prioritize urgent cases for timely attention, optimizing resource use. Technically, MediFlow uses Twilio for its IVR system, GPT-4 models for diagnosis generation, and Django for AI and IVR integration. The frontend is built with Next.js, and the system is hosted on Vercel and DigitalOcean, ensuring scalability and reliability.

ConnectAssitant

TeleConnect AI revolutionizes telecommunications in underserved regions by providing a comprehensive mobile solution powered by Llama 3.2’s advanced AI models. The app delivers real-time network diagnostics and predictive maintenance to ensure optimal performance and minimize downtime. Users benefit from multimodal customer support, allowing them to seek assistance through text and image inputs seamlessly. Additionally, TeleConnect AI optimizes connectivity by analyzing usage patterns and environmental factors, enhancing network coverage and reliability. The application is optimized for edge devices, enabling efficient on-device processing and reducing dependency on cloud infrastructure. With augmented reality (AR) features for field technicians, the app streamlines installations and repairs, ensuring swift and accurate service deployment. By addressing critical needs in the telecommunications sector, TeleConnect AI fosters improved connectivity and operational efficiency in areas that need it most.

Automated marking machine for paper-based homework

We have designed a machine for correcting paper homework with traceable marks. Based on new large model technology and product forms, it enables one-click scanning, traceable correction, and data analysis of paper homework. It can correct more than 60 sheets of homework per minute, with a cost of about 5 cents per sheet and a correction accuracy rate of over 97%. Currently, it has been deployed at Benyuan Primary School in Shenzhen and Wen San Road Primary School in Hangzhou, having corrected over 10,000 sheets of homework and more than 200,000 questions. Additionally, the system aims to provide teachers with long-term tracking of student progress through data analysis, further enhancing teaching quality and student learning outcomes. Compared to existing photo-based homework correction software, our correction machine achieves one-click batch traceable correction, significantly reducing the workload of teachers in correcting homework. The video has been viewed over 50,000 times on platforms such as Bilibili and Xiaohongshu, with hundreds of teachers actively applying for trials. We developed a comprehensive AI grading system for school tests, incorporating automated exam paper scanning and question recognition with OCR enhanced and refined by LLM, and AI-generated grading results using Java (backend), Vue.js/JavaScript (frontend), and Junit and Jest(test).

Smarter AI for Offline Car Communication

In-Car AI Agents is an innovative project designed to make in-car artificial intelligence smarter and more responsive in offline scenarios. Currently, most AI-powered car assistants rely on internet connectivity to process commands, leaving drivers without key functionalities in areas with poor or no network coverage. Our solution aims to address this gap by deploying edge-based AI models that can function without Wi-Fi, allowing drivers to interact with their cars using voice commands for basic operations such as controlling the air conditioning, navigation prompts, and music playback. The system uses pre-trained voice recognition models and offline edge computing to deliver real-time responses to the driver’s requests, all processed locally. Additionally, the project explores simple computer vision tasks like lane detection, integrated to run without network dependency. Our vision is to make cars smarter, safer, and more autonomous without requiring constant internet access. By providing an MVP, we aim to showcase how AI in cars can be optimized for offline use, thus improving the overall driving experience in areas where internet connectivity is unreliable.