Testing the Power of GPT Vision's Multimodal AI Capabilities
Table of Contents
- Creating a Simple Web App from a Hand-drawn Sketch
- Estimating Complex Quantities Like Jar Bead Counts
- Generating Explanations of YouTube Video Concepts
- Crafting Funny Memes from Ordinary Photos
- Building Websites from Simple Sketches
- Recommending Campsites from Scenic Photos
Creating a Simple Web App from a Hand-drawn Sketch
In the YouTube video, the host starts by showing a simple hand-drawn sketch of a web app flowchart. The sketch has boxes representing the frontend, backend, OpenAI API, and GPT-4 response. Arrows connect these elements to demonstrate the flow of data.
The host then asks GPT-4 Vision to generate Python and JavaScript code to actually build this web app, by providing the image of the hand-drawn sketch. Remarkably, GPT-4 Vision provides complete frontend and backend code in HTML, JavaScript, CSS and Python Flask.
Implementing the Frontend and Backend Code
The HTML file provided by GPT-4 contains all the user interface elements sketched out in the hand drawing. There are text input and output boxes connected to the OpenAI API on the backend. The Python Flask code handles routing the user input to the OpenAI text completion API and returning the response. The OpenAI API key just needs to be inserted. After copying the code into text files, the host runs the Python app and opens the localhost URL. He enters an example prompt asking how to learn Python, clicks the submit button, and gets a response suggesting resources to practice Python.
Testing the Final Web App
In the final web app, the user can enter text prompts which get sent to the OpenAI API. The API response text is nicely formatted and displayed back to the user. Considering only a basic hand-drawn sketch was provided initially, GPT-4 did an remarkable job generating full working code for a complete web application.
Estimating Complex Quantities Like Jar Bead Counts
The host tries an interesting test of having GPT-4 estimate how many beads are in a large jar. He provides an image of a man holding up a jar filled with small colorful beads.
GPT-4 Vision goes through an analytical process of estimating the volume of the jar, sizing of an individual bead, and calculates an approximate bead count.
The initial result of 27,800 beads is suspiciously accurate, as the revealed answer was 27,800 beads. However additional attempts provide widely varying guesses, proving the initial accuracy was just luck.
Generating Explanations of YouTube Video Concepts
The host demonstrates how GPT-4 can interpret images from YouTube videos to provide enhanced explanations. He's watching a video discussing a prompt mutation algorithm, captures a screenshot, and has GPT-4 explain the key concepts step-by-step.
This works well, as GPT-4 accurately describes the screenshot contents and even provides an example mutated prompt based on what it sees.
Crafting Funny Memes from Ordinary Photos
In an entertaining experiment, the host tests GPT-4's sense of humor by providing an ordinary photo of his front porch, which happens to prominently show his house numbered '69'.
He asks GPT-4 to generate funny memes from the photo. It creates humorous text and image combinations making jokes related to the number 69 and other quirky elements visible.
Building Websites from Simple Sketches
Inspired by an example where a complex website was generated from a napkin sketch, the host reproduces the experiment. He draws website elements like header, body, images, and footer box.
GPT-4 again manages to produce code for an entire 1990s hacker-themed website matching the layout in the sketch. After copying the HTML, CSS and JavaScript to an index.html file, the website loads successfully, including dynamic rain effects.
Recommending Campsites from Scenic Photos
The host goes on a nature walk and takes picturesque photos of a riverside and forest area, testing if GPT-4 can intelligently recommend good campsites.
For each image, GPT-4 provides pros and cons of camping in those spots. It accounts for factors like shelter, water access, firewood, wildlife risk, and scenic views. Finally it suggests an ideal hybrid location at the edge of the forest near the river.
FAQ
Q: What AI model powers GPT Vision?
A: GPT Vision is powered by a multimodal AI model trained by Anthropic called Claude.
Q: What types of tasks can GPT Vision perform?
A: GPT Vision can create web apps, estimate quantities, explain concepts, generate memes, build websites, and make recommendations - among many other capabilities.
Q: How accurate are GPT Vision's predictions?
A: The accuracy varies based on the complexity of the task, but with the right prompts, GPT Vision can produce highly accurate results.
Q: Can GPT Vision process images?
A: Yes, GPT Vision has the ability to analyze and understand visual information from images provided to it.
Q: What framework was used to build the web app?
A: Python Flask was used on the backend to build the web app, with HTML/CSS/JavaScript powering the frontend.
Q: What sports statistics did GPT Vision analyze?
A: GPT Vision analyzed Premier League defensive player stats and upcoming fixtures to recommend the top fantasy football picks.
Q: What TV shows were recommended based on The Office?
A: GPT Vision recommended similar mockumentary sitcoms like Parks and Recreation, Brooklyn Nine-Nine, 30 Rock, and more.
Q: How can GPT Vision be used to build websites?
A: By analyzing a hand-drawn website sketch, GPT Vision can generate full HTML, CSS, and JavaScript code.
Q: What survival factors were considered when recommending campsites?
A: Shelter, wood availability, dampness, wildlife risks, and access to water were evaluated by GPT Vision.
Q: What future capabilities can we expect from GPT Vision?
A: As Anthropic continues developing Claude, we can expect even more sophisticated multimodal abilities from GPT Vision.