When AI Meets Asphalt: Teaching GPT-4-Vision to Drive a Car
I decided to take GPT-4-Vision, this high-tech AI, for a spin — literally. I plugged it into a driving simulator, handed it the virtual keys, and said, “Show me what you’ve got.” The goal was simple: drive around without turning the city into a pile of digital rubble.
Well, things got off to a rocky start. It felt like I’d given a joystick to a cat and expected it to play a video game. The AI was gung-ho but confused, ramming into walls with the enthusiasm of a kid in a candy store. It was choosing ‘W’ and ‘D’ like those were the only buttons that mattered, and the results were… let’s just say, demolition derby-esque.
But here’s the thing: I wasn’t clear enough in my instructions, and the poor AI was doing its best with what it had — like trying to make sense of a foreign movie without subtitles. I kept at it, refining how I communicated with it, and you know what? The AI began to show signs of getting it. It wasn’t long before it started to avoid obstacles and keep the car on the road for the most part.
It’s funny, in a way. You’d think a cutting-edge AI would have driving in the bag, but there’s a charming humility in watching it learn from scratch — reminds me of my first time behind the wheel.
So yeah, my little experiment showed that there’s still a gap between AI knowing stuff and actually doing stuff. But with each tweak and adjustment, I watched it improve, and that was pretty darn cool. I’m not about to let the AI take my car out on the actual roads yet, but hey, we’re making progress, one key press at a time.
One thing to note about my little adventure is that most self-driving car systems today are really good at one thing: object detection. They’re like whiz kids at spotting stop signs, pedestrians, and other cars, but they don’t juggle tasks outside their specialty. That’s their whole world — seeing, not chatting or thinking in the human sense.
Now, enter GPT-4-Vision and its multi-modal capabilities. This is where things get spicy. Unlike traditional systems, it’s not just looking at the world; it’s trying to understand it, bringing in a blend of seeing and processing complex information, kinda like how we humans do it. It’s a big leap from the one-trick ponies driving around today.
If I can help this AI learn to drive without bumping into every wall or streetlamp, imagine the potential. We could be looking at a future where self-driving cars are not just reacting to objects but understanding context, making decisions with a level of nuance that’s more human-like. It’s a game-changer.
My experiment might’ve been a small step for AI-kind, but it’s pointing to a future where multi-modal systems like GPT-4-Vision could revolutionize how we think about and implement self-driving technology. The possibilities are as thrilling as they are vast.
Code or GTFO: https://github.com/Tylersuard/GPT-4-V-Self-Driving-Car/tree/main
