OpenAI AI technology page Top Builders
Explore the top contributors showcasing the highest number of OpenAI AI technology page app submissions within our community.
OpenAI OpenAI is an American artificial intelligence (AI) research laboratory, with the declared intention of promoting and developing a friendly AI. Their main goal is creating safe AGI that benefits all of humanity. Among their products you can find: GPT-4, DALL-E, OpenAI Five, ChatGPT, OpenAI Codex.
|December 11, 2015
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Start building with OpenAI’s products
OpenAI has an amazing potential and extraordinary usage potential - we all saw the internet flooded with showcases of ChatGPT usage. Recently they introduced ChatGPT plugins. You can incorporate OpenAI’s technology into many of your app ideas and it will solve many of the problems you faced perfectly. To get inspired, see many apps created with ChatGPT, Whisper and more during lablab.ai Hackathons!
GPT-3 stands for Generative Pre-trained Transformer 3 and it is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation language prediction model in the GPT-n series created by OpenAI. GPT-3 is currently in open beta.
You can easily use GPT-3 for your app, and all necessary APIs, boilerplates, tutorials explaining how to do so and more, you can find on our OpenAI GPT-3 tech page.
ChatGPT is a large language model trained by OpenAI to generate human-like text in a conversational style. It is a variant of the GPT-3 model, which was specifically designed to be used to generate text in response to user input.
You can easily use ChatGPT for your app, and all necessary APIs, boilerplates, tutorials explaining how to do so and more, you can find on our OpenAI ChatGPT tech page.
GPT-4 is OpenAI's 4th generation Generative Pre-trained Transformer. It is a multimodal large language model that uses deep learning to produce human-like text, accepting image and text inputs. GPT-4 is OpenAI's most advanced system, producing safer and more useful responses
You can easily use GPT-4 for your app, and all necessary APIs, boilerplates, tutorials explaining how to do so and more, you can find on our OpenAI GPT-4 tech page.
Whisper operates by recognizing words from web-sourced data collected from 680,000 hours of multilingual and multitask training. With this, English speech recognition can be made more robust and accurate to reach the level of human performance. Technical language, accents, and background noise are not a problem for Whisper.
You can easily use Whisper for your app, and all necessary APIs, boilerplates, tutorials explaining how to do so and more, you can find on our OpenAI Whisper tech page.
Dalle-e uses deep learning models developed by OpenAI to generate digital images from natural language descriptions, called "prompts".
You can easily use DALL-E 2 for your app, and all necessary APIs, boilerplates, tutorials explaining how to do so and more, you can find on our OpenAI DALL-E tech page.
OpenAI Codex is an artificial intelligence system that enables developers to translate natural language into code & much more
You can easily use OpenAI CODEX for your app, and all necessary APIs, boilerplates, tutorials explaining how to do so and more, you can find on our OpenAI Codex tech page.
OpenAI AI technology page Hackathon projects
Discover innovative solutions crafted with OpenAI AI technology page, developed by our community members during our engaging hackathons.
Unleash your child's creativity with our AI-powered storytelling app! Imagine a world where children's imaginations run wild, fueled by technology that understands them. Our app uses cutting-edge Generative AI to create personalized stories and artwork based on their choices and drawings. Here's how it works: Choose values: Kids pick themes and values they care about, shaping the story's direction. Create art: They draw their own pictures, unlocking unique stories inspired by their creativity. AI magic: Our AI analyzes their choices and artwork, generating captivating stories, stunning images, and engaging narration. Interactive learning: Quizzes test comprehension and reinforce key skills, making learning fun and rewarding. Benefits: Boosts imagination and creativity: Kids become active participants in the storytelling process. Develops critical thinking: Quizzes challenge them to think deeply about the story's message. Promotes literacy and language skills: Engaging stories and narration encourage reading and comprehension. Personalized experience: Each child's journey is unique, reflecting their values and interests. With our app, learning becomes an adventure, sparking a love for stories and igniting young minds.
Text to flow chart
Our project aims to revolutionize the process of translating text into visual representations by introducing a text-to-flowchart maker. Leveraging the advanced capabilities of OpenAI and Trulens, our solution simplifies complex concepts into intuitive flowcharts with minimal effort. Users can input text, and our system automatically generates a corresponding flowchart, eliminating the need for manual charting. Whether for project management, educational materials, or brainstorming sessions, our tool enhances communication and comprehension by transforming textual information into easily digestible visual diagrams. By streamlining this process, we empower individuals and teams to convey ideas more effectively and efficiently in various domains.
Multilingual Video Translator and Transcriber
In this project, the goal is to translate an English video into Spanish using a multi-step process. The initial step involves transcribing the audio from the English video using OpenAI Whisper models on Colab. Subsequently, the generated transcript undergoes translation utilizing GPT-4. To produce the translated voiceover, OpenAI's TTS-1 model is employed, but due to a character limit of 4096, the translated transcript is divided into manageable chunks. Voiceovers for each segment are then obtained, and the individual audio files are merged using ffmpeg. The final challenge lies in synchronizing the source video and audio, an ongoing effort requiring significant time and dedication. Despite this synchronization hurdle, the team is submitting the completed sections of the project, showcasing progress in the intricate process of translating and dubbing the source content.
Analyzes the given resume and the job description and then suggests few questions. The user will be able to answer them and get feedback. The feedback will be help to find their shortfalls and helps them to prepare for the interview. We made API calls to GPT-3.5 to analyze the resume and job descriptions and then GPT3.5 will be able to generate set of questions and poses to the users. The users are required to answer the questions as elobrately as possible and get the required feedback. The prompts are tailored in such way to give good results to the users. We are further working on to improve the application and release the next version.
The "Market Mentor" project, presented by Team Bit Rebels, introduces an innovative investment education bot leveraging data from Professor Aswath Damodaran. We created 3 models to represent Prof. Damodaran's knowledge: The vanilla RAG model, a Hype Model, and our novel modified HyDE model that is built to do away with the weaknesses of the HyDE model of poor similarity matchings. These models act as personal finance instructors who can teach the fundamentals of investing and valuations. This tool also simplifies financial analysis with a novel Text-to-SQL model, transforming user inquiries into SQL queries for efficient data extraction. It boasts personalized bots, specialized smaller models, and an intuitive, user-friendly interface that replicates expert communication styles, bypassing the need for extensive data. This technology empowers users with easy access to complex data, supporting informed decisions and strategic planning while improving with use.
According to a study by McKinsey, employees spend almost 2 hours per day searching and gathering information. Manual extraction processes cannot scale to handle large volumes of data. Automated data extraction solves the inefficiencies of manual information gathering. LexaScan is an AI solution that extracts data from text and images, delivering structured JSON format. We used GPT-4 Vision from OpenAI to extract data from text and images. We also used TruLens to ensure that the output is relevant and harmless. The total addressable market for data extraction software was estimated at $1.4 billion US dollars in 2022 and is expected to reach $3.8 billion dollars by 2028. Conservatively, we estimate the serviceable addressable market to be $140 million and the serviceable obtainable market to be $42 million.