# Moravec's Paradox Comes From The Availability Heuristic

*Epistemic Status: very quick one-thought post, may very well be arguing against a position nobody actually holds, but I haven’t seen this said explicitly anywhere so I figured I would say it.*

Setting Up The Paradox
----------------------

According to Wikipedia:

> Moravec’s paradox is the observation by artificial intelligence and robotics researchers that, contrary to traditional assumptions, reasoning requires very little computation, but sensorimotor and perception skills require enormous computational resources.
> 
> -[https://en.wikipedia.org/wiki/Moravec’s_paradox](https://en.wikipedia.org/wiki/Moravec’s_paradox)

I think this is probably close to what to Hans Moravec originally meant to say in the 1980’s, but not very close to how the term is used today. Here is my best attempt to specify the statement I think people generally point at when they use the term nowadays:

> Moravec’s paradox  is the observation that in general, tasks that are hard for humans are easy for computers, and tasks that are easy for humans are hard for computers.
> 
> -me

If you found yourself nodding along to that second one, that’s some evidence I’ve roughly captured the modern colloquial meaning. Even when it’s not attached to the name “Moravec’s Paradox”, I think this general sentiment is a very widespread meme nowadays. Some example uses of this version of the idea that led me to write up this post are [here](https://www.alignmentforum.org/s/mzgtmmTKKn5MuCzFJ/p/bz5GdmCWj8o48726N#The_likelihood_of_developing_highly_agentic_AGI) and [here](https://www.lesswrong.com/posts/6XLyM22PBd9qDtin8/learning-human-preferences-optimistic-and-pessimistic#Pessimism__humans_interpreting_other_humans).

To be clear, from here on out I will be talking about the modern, popular-meme version of Moravec's Paradox.

And Dissolving It
-----------------

I think Moravec’s Paradox is an illusion that comes from the [availability heuristic](https://en.wikipedia.org/wiki/Availability_heuristic), or something like it. The mechanism is very simple – it’s just not that memorable when we get results that match up with our expectations for what will be easy/hard.

If you try, you can pretty easily come up with exceptions to Moravec’s Paradox. Lots of them. Things like single digit arithmetic, repetitive labor, and drawing simple shapes are easy for both humans and computers. Things like protein folding, the traveling salesman problem, and geopolitical forecasting are difficult for both humans and computers. But these examples aren’t particularly interesting, because they feel *obvious*. We focus our attention on the non-obvious cases instead – the examples where human/computer strengths are opposite, counter to our expectations.

Now, this isn’t to say that the idea behind Moravec’s Paradox is wrong and bad and we should throw it out. It’s definitely a useful observation, I just think it needs some clearing up and re-phrasing. **This is the key difference: it’s not that human and computer difficulty ratings for various tasks are opposites – they’re just not particularly aligned at all.**

![](https://i1.wp.com/jlucassen.com/wp-content/uploads/2021/10/image.png?resize=1024%2C400&ssl=1)

We expected “hard for humans” and “hard for computers” to be strongly correlated. In reality they aren’t. But when we focus just on the memorable cases, this makes it appear as if all tasks are either easy for humans and hard for computers, or vice versa.

The observations cited to support Moravec’s Paradox do still give us an update. They tell us that something easy for humans won’t necessarily be easy for computers. But we should remember that something easy for humans won’t necessarily be hard for computers either. The lesson we take from Moravec’s observations in the 1980’s is that it tells us very little about what to expect.

That’s definitely valuable to know. But the widespread meme that humans and computers have opposite strengths is mostly just misleading. It updates too hard, due to a focus on the memorable cases. This produces a conclusion that’s the opposite of the original (negative correlation vs positive correlation), but that’s equally incorrect (should be little to no correlation at all).