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 dateSeptember 25, 2023
AuthorOpenAI
DocumentationOpenAI's Guide
TypeAI 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:


GPT-4 Vision Tutorials


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.

Cricket Video Analyzer - 12 labs RAG

Cricket Video Analyzer - 12 labs RAG

Cricket VIDEO ANALYZER to understand a batsman using 12 labs API +llama index + GPT 4 Solution: Choose 1 batsman (Glenn Maxwell of Australia) (Source 1) : Use 12 labs API to understand their playing style - Understand the video Generate the text (Source 2 for additional augmentation) : Use cricinfo.com APIs to get ball by ball commentary of the batsman Build a vector store of the dataset built using (2) and (3) Use GPT-4 to answer questions on this dataset (RAG application) Global Popularity and Demand: The immense popularity of cricket has led to a high demand for video analysis tools. Coaches, players, and teams use these tools to analyze player performance, match strategies, and opponent weaknesses. The ability to break down every aspect of a game, from batting techniques to bowling actions, has become crucial for success. As cricket leagues (such as the Indian Premier League) and international tournaments continue to grow, the demand for sophisticated video analysis software has skyrocketed. Advanced Technology and Data Insights: Cricket video analysis tools have evolved significantly over the years. High-speed cameras, ball-tracking systems, and AI algorithms allow for precise analysis of player movements, ball trajectories, and field placements. Coaches can identify patterns, assess player fitness, and make data-driven decisions. For example, analyzing a batsmanā€™s scoring zones or a bowlerā€™s release point can provide valuable insights.

InvestMate - Mutual Fund Advisor

InvestMate - Mutual Fund Advisor

Investmate is an AI Assistant that lets its user explore our list of mutual funds, and helps in creating a diversified portfolio for wide range of investment objectives. The user can interact with Investmate and ask questions. Investmate can give portfolio based on several different characteristics. Some of them are current age, risk tolerance, and retirement age. Based on answers to these questions, Investmate selects appropriate funds based on investment duration, risk tolerance to select from a wide range of mutual funds. This is targeted towards investment firms and can also target individuals. Mutual fund industry is a behemoth with nearly 33 trillion dollars in assets. There are nearly 160 millions individual investors in US. Even a $5/per month fees with 20% user base in year 5 would give more than 1.8 billion dollars in revenue per year. The goal of this project is to develop a fully-functional OpenAI chat application that illustrates: 1. Streaming OpenAI Assistant responses from the server to deliver real time responses. 2. Customizing the appearance and behavior of your OpenAI chat application. 3. Handling a long thread of messages without losing context. 4. Designing an OpenAI app to work on both mobile and desktop. The InvestMate presents a strong value proposition for investment advisory firms, bridging the gap between vast data quantities and actionable insights.With strategic pricing and targeted marketing, this chatbot is poised for successful market penetration and sustained growth. Technologies used: OpenAI LLM, AI Assistant, RAG, Nodejs, LLM ResponseĀ Evaluation.