The Birth of o1: More Than a Fancy Name
OpenAI's playing the name game again, folks. They're calling this one "o1" - like they're hitting the reset button on AI evolution. As Daniel Warfield, our resident tech guru, puts it:
"Open AI said in their press release that it's such a big deal that they're resetting the clock again. Just like your favorite movie or video game, where they go back to number one every couple of years."
But is it really a game-changer, or just clever marketing?
At its core, o1 is all about thinking before speaking. It's using what’s called Chain of Thought, which is a fancy way of saying it breaks down problems into bite-sized pieces before spitting out an answer. It's not exactly reinventing the wheel with o1, but it is souping up the engine.
Chain of Thought: The Method Behind the Madness
Now, Chain of Thought isn't some groundbreaking new concept. It's been floating around in AI circles for a while. But o1's take on it? That's where things get interesting.
Warfield breaks it down:
"The idea of Chain of Thought is instead of spitting out the answer in the very beginning, you think about the answer and incrementally break it down and solve sub-problems, and then you can use those results to create the last output which is the answer."
It's like showing your work in math class. The AI isn't just giving you the answer; it's walking you through its thought process. It's this transparency that has people buzzing.
The Rule Follower: o1's Superpower
Here's where o1 really flexes its muscles. This thing is like the straight-A student of the AI world when it comes to following instructions. Warfield put it through its paces with a test that would make most LLMs cry:
"I need it to generate four and a half question answer pairs designed to test a model. The last pair should only be a question without the answer. The questions should be either about cats or pirates from popular fiction movies The questions should alternate in terms of their starting word, output the response in a CSV format, etc."
And guess what? o1 nailed it. We're talking perfect formatting, sticking to the rules, the whole nine yards. For developers, this is huge. It means you can actually rely on this thing to do what you tell it to do, consistently.
Check out the full clip with o1’s response.
Why RAG Developers Should Care
If you're in the trenches building AI-powered apps, o1 could be your new best friend.
Here's why, straight from Warfield:
"If you're trying to build an application around an LLM, you need to give it stuff where it outputs stuff in a way that makes sense and works, and it's really really difficult if you have an LLM that's randomly messing stuff up arbitrarily for seemingly no reason at all. It’s really hard to fit a model like that into a complex application where you’re relying on the output of the model to obey certain characteristics."
Translation: o1 could save you a ton of headaches. It's not just about getting the right answer; it's about getting it in a way that doesn't break your entire system.
The Skeptic's Corner: Not All Sunshine and Rainbows
Before we get too carried away, let's pump the brakes a bit. Not everyone's drinking the o1 Kool-Aid. Some folks in the AI world are understandably skeptical, with one professor calling it "a mediocre grad student who is not completely unredeemable but can be trained to be decent. But is not a PhD at anything yet."
And let's not forget, OpenAI's keeping their cards close to their chest. We don't know exactly how this thing works under the hood, which makes it tough for the community to fully evaluate it.
Even Warfield's got some doubts:
"I'm beginning to think that these are descriptions of what it's thinking, not actually what it's thinking... I think it's generating tokens under the hood and then it's just describing from a high level summarizing those tokens for us with other tokens."
In other words, we might be seeing a highlight reel rather than the full game footage of o1's thought process.
The Real-World Test: Coding Challenges
Warfield put o1 through its paces with a real-world coding challenge - creating a game called "insane tic-tac-toe." Basically an embedded game of tic-tac-toe within each square of the board, with complexities and extra rules thrown in.
The results? Mixed bag. While o1 showed some improvements over previous models, it still stumbled:
"In GPT-4 it basically didn't function, it just superficially looked like insane tic-tac-toe. In o1 it ran and seemed to generally obey the rules but those rules broke down as you played the game."
This tells us that while o1 is making strides, it's not the perfect coding genius some are making it out to be. It's better at understanding and following complex instructions, but it's not going to put developers out of a job anytime soon.
The Bottom Line: Progress, Not Perfection
Here's the deal: o1 is cool. Really cool. It's a major step forward in AI. But let's not start planning the "Humans are Obsolete" party just yet. This isn't Artificial General Intelligence, or AGI. It's not going to turn on humanity or start writing Pulitzer-winning novels.
What it is, at its core, is a promising tool for developers. It's better at following rules, more consistent in its outputs, and gives us a peek into its decision-making process. For building AI-powered applications, that's gold.
As we keep pushing the boundaries of what AI can do, models like o1 are important milestones. They show us how far we've come, but also how far we've still got to go in creating truly intelligent machines.
So, is o1 the dawn of a new era in AI? Maybe not. But it's definitely a solid step in the right direction. And for now, that's something worth paying attention to.
Remember, in the world of AI, today's breakthrough is tomorrow's baseline. Stay curious, stay skeptical, and keep pushing those boundaries. Who knows? The next big leap might be just around the corner.
Watch the full episode of RAG Masters:
