GPT-4 Is a Reasoning Engine (2023)

Large language models aren’t always right. Their strength—for now—is mimicry and prediction rather than accuracy. But as Dan Shipper writes in this essay from March 2023, these models are only as good as the knowledge they have access to. With OpenAI’s DevDay set for Oct. 1 and Every taking a quarterly Think Week, we thought this was a great time to republish Dan’s essay about AI and reasoning.

Also: We created eight custom wallpapers based on Every’s art for iPhone or Android. Download them for free.—Kate Lee

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In 1894, a Boston-based astronomer named Percivel Lowell found intelligent life on Mars.

Looking through a telescope from his private observatory he observed dark straight lines running across the Martian surface. He believed these lines to be evidence of canals built by an advanced but struggling alien civilization trying to tap water from the polar ice caps.

He spent years making intricate drawings of these lines, and his findings captured public imagination at the time. But you’ve never heard of him because he turned out to be dead wrong.

In the 1960s, NASA's Mariner missions captured high-resolution images of Mars, revealing that these "canals" were nothing more than an optical illusion caused by the distribution of craters on the planet's surface. With the low resolution available to his telescope at the time, these craters looked to Lowell like straight lines which, through a chain of reasoning, he theorized to be canals built by intelligent life.

Lowell’s story shows that there are at least two important components to thinking: reasoning and knowledge. Knowledge without reasoning is inert—you can’t do anything with it. But reasoning without knowledge can turn into compelling, confident fabrication.

Interestingly, this dichotomy isn’t limited to human cognition. It’s also a key thing that people fundamentally miss about AI.

Even though our AI models were trained by reading the whole internet, that training mostly enhances their reasoning abilities, not how much they know. And so, the performance of today’s AI models is constrained by their lack of knowledge.

I saw Sam Altman speak at a small Sequoia Capital event in San Francisco earlier in March 2023, and he emphasized this exact point: GPT models are actually reasoning engines, not knowledge databases.

This is crucial to understand because it predicts that advances in the usefulness of AI will come from advances in its ability to access the right knowledge at the right time—not just from advances in its reasoning powers.

Knowledge and reasoning in GPT models

Here’s an example to illustrate this point. GPT-4 is the most advanced model on the market today (note: as of this writing in March 2023). Its reasoning capabilities are so good that it can get a 5 on the AP Bio exam. But if I ask it who I am it says the following:

That’s close to being right except for one big problem…I’m the cofounder of a few companies, but neither of them is Superhuman or Fireflies.

AI critics will be quick to say that this proves GPT-4 is nothing more than a stochastic parrot, and that its results should be dismissed offhand. But they’re wrong. Its performance improves dramatically the second it has access to the right information.

For example, I have access to a version of ChatGPT that can use web searches to ground its answers with what it finds on the internet.

In other words, instead of using its reasoning capabilities to come up with a theoretically plausible answer, it does web research to create a knowledge base for itself. It then analyzes the collected information and distills a more accurate answer:

Now, that’s pretty good! The underlying model is the same, but the answer improves significantly because it has the right information to reason over.