For OpenAI, o1 represents a step toward its broader goal of human-like artificial intelligence. More practically, it does a better job at writing code and solving multistep problems than previous models. But it’s also more expensive and slower to use than GPT-4o. OpenAI is calling this release of o1 a “preview” to emphasize how nascent it is.
The training behind o1 is fundamentally different from its predecessors, OpenAI’s research lead, Jerry Tworek, tells me, though the company is being vague about the exact details. He says o1 “has been trained using a completely new optimization algorithm and a new training dataset specifically tailored for it.”
OpenAI taught previous GPT models to mimic patterns from its training data. With o1, it trained the model to solve problems on its own using a technique known as reinforcement learning, which teaches the system through rewards and penalties. It then uses a “chain of thought” to process queries, similarly to how humans process problems by going through them step-by-step.
At the same time, o1 is not as capable as GPT-4o in a lot of areas. It doesn’t do as well on factual knowledge about the world. It also doesn’t have the ability to browse the web or process files and images. Still, the company believes it represents a brand-new class of capabilities. It was named o1 to indicate “resetting the counter back to 1.”
I think this is the most important part (emphasis mine):
As a result of this new training methodology, OpenAI says the model should be more accurate. “We have noticed that this model hallucinates less,” Tworek says. But the problem still persists. “We can’t say we solved hallucinations.”
It is literally artificial intelligence though. Just because chatGPT doesn’t perform as a layperson imagined it would, it doesn’t mean it’s not AI. They just have an unrealistic expectation of what counts as AI along with the common misconception of AI and AGI being the same thing.
A chess playing robot uses artificial intelligence as well. It’s a narrow AI, meaning it can do one thing really well but that doesn’t translate to other things. AGI on the other hand stands for Artificial General Intelligence. Humans are an example of general intelligence meaning that we have the cognitive ability to perform well on several unrelated tasks.
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I don’t see the need to be such a dick about it. The term AGI was coined in the 90’s.
It offends me when hype chasers do this to try and legitimize their snake oil. I don’t care what like 5 random researchers mentioned one time in the 90s, it does not justify calling a language prediction model “AI”. That’s not what the term has ever meant.
That’s a bit like taking issue with the terms jig, spinner, spoon, and fly, and saying you don’t care what some random fishermen call them; to the rest of us, they’re just lures.
AGI is a subcategory of AI. We’ve had AGI systems in science fiction for decades, but we’ve just been calling them AI, which isn’t wrong, but it’s an unspecific term. AI is broad and encompasses everything from predictive text input to AGI and beyond. Every AGI system is also AI, but not everything AI is generally intelligent. ASI (Artificial Super Intelligence) would be an even more specific term, referring to something that is not only artificial and generally intelligent but exceeds human intelligence.