"Steep Learning Curve" is an Erroneous Metaphor | Hacker News Reader

"Steep Learning Curve" is an Erroneous Metaphor

yann.lecun.com

43 pointsckuehne25 comments

The original meaning of a steep learning curve was exactly as he says. It's been changed in popular use from "easy" to "hard".

(Rummage rummage rummage...):

"Early uses of the metaphor focused on the pattern's positive aspect, namely the potential for quick progress in learning ... Over time, however, the metaphor has become more commonly used to focus on the pattern's negative aspect, namely the difficulty of learning once one gets beyond the basics of a subject."

http://en.wikipedia.org/wiki/Learning_curve

"One natural interpretation of such a curve, which was the predominant early usage (according to Wikipedia) and still exists in some technical circles, is that the thing being learnt is easy — a great amount of learning happens in a small amount of time. This is the opposite of the popular usage."

http://english.stackexchange.com/questions/6209/steep-learni...

"The phrase everyone gets wrong. Outside experimental psychology, where the term originated, I have never seen a correct usage. Learning curves show performance (e.g., percent correct) as a function of amount of training (e.g., number of trials). A steep learning curve means the organization, person, or animal quickly went from low to high performance — in other words, learning was fast."

http://blog.sethroberts.net/2008/08/18/steep-learning-curve/

alt.english.usage:

"The learning curve for bridge is not steep - on the contrary. But I have seen other examples of this confusing of "steep curve" with something that is hard to learn. The association is probably that it is hard to get uphill, but the true meaning is that with a steep learning curve a given effort will take you high up the curve and thus corresponds to something that is easy."

https://groups.google.com/forum/?hl=en#!topic/alt.usage.engl...

Definition:

"a graphic representation of progress in learning measured against the time required to achieve mastery."

...

'The phrase "steep learning curve" is sometimes used incorrectly to mean "hard to learn" whereas of course it implies rapid learning.'

http://dictionary.reference.com/browse/learning+curve

Thanks. I had seen the Wikipedia entry (but not the stackexchange and the Seth Roberts post) as well but did not post it because I found it interesting to see everyone come up with rationalizations for why their usage of the phrase is 'the correct one'. :)

We use it 'wrongly', and will continue to (unless you want to not be understood), but hey - it's interesting that somehow it got mixed up! I wonder who got it backwards first?

Similarly, Joel has an article on Hungarian Notation that says common usage is wrong. Would those who defend the (current, wrong) usage of "steep learning curve" not find it interesting that the proper H Notation was a lot more useful than current usage?

http://www.joelonsoftware.com/articles/Wrong.html

The concept of a "steep curve" is the physical analogy to a steep hill, in which forward progress/learning requires significant effort. Thus x is progress and y is the cumulative effort required to achieve that level of progress.

Right. I suspect the colloquial use keeps "learning" on the y-axis but has "progress" on the x-axis, instead of "training". That is, the psychologists consider learning a function of training, whereas others consider achievable work a function of learning.

"The concept of a 'steep curve' is the physical analogy to a steep hill"

Of course it is. That is likely the reason why the meaning got mixed up in the first place. The rest of your comment is rationalization (Otherwise, people would call it cumulative effort curve.). See richardw's sister comment.

Yes, that's exactly what the article said.

I understand the objection, but like most idioms the intent is understood, and like most idioms I've always accepted this one for its intent.

However, you can make it work logically. Look at his graph and imagine the two curves as hills, both going to the same altitude. X is time, Y is altitude. You'd have to work much harder in a short time to get to the same altitude as the shallow hill.

And unlike a machine, where the y-axis learning value is assumed to be achievable by any machine of a specific class, humans have a much higher degree of variability. Some will not reach the top of the curve where things level off. Thus a steep learning curve means that a greater percentage of individuals will give up as a result of the more compressed learning requirements.

In related news, scientists discover idioms not mathematically accurate. Movie at 11.

It's "film at 11," which used to mean that the news team actually had footage of the news, back before it was assumed that everyone had already seen it on YouTube.

Really? I always thought that was what TV anchors said after wrapping the daily news up, i.e. a movie was what was coming up next. However, Wikipedia agrees with you, so thanks for teaching me something!

Or you have your axis the wrong way round...

That's what I noticed. If you label the horizontal axis for the amount learned (more or less equivalent to his "Performance" that he has on the vertical axis) and the vertical axis for effort required, it would fit standard usage just fine.

Steepness doesn't have to refer to a graph of time versus skill, and at any rate the metaphor certainly predates machine learning curves. As others have pointed out, it's meant to evoke the difficulty of climbing a steep hill versus one that's flatter. This person is mixing concepts and etymologies amidst an insufferable air of superiority.

I think steep learning curves are used in two contexts -

1. for example at your job - steep learning curve is good, it means learning / time is high.

2. for example programming languages - steep learning curve is (possibly) bad, it means learning or aptitude /effort is high - lots of effort to gain aptitude. Somewhat confused by the fact that the reason it's hard is possibly because the final achievement is more useful (but not necessarily - you can have hard-to-learn interfaces/processes, that are just badly designed, so you've only learned to jump silly hoops).

Metaphor (if it is really a metaphor) is OK for me. I always understood it this way: time on X, stuff to learn (not some performance) on Y. Somewhere on that Y is the threshold point and by passing it you cross from "still learning" to "doing actual work" area.

I admit, time on X may look ambiguous but I kind of look at it as "some reasonable time interval".

That way something complex will require to learn more going from 0 to threshold point and hence steeper curve (line) than simpler stuff.

The real problem with this, apart from the inherent ambiguity of English, is that the axes are never defined so the graph can take any form you want. Steep (as in climbing) usually denotes effort or adversity - a point which may be missed by non-native speakers - hence the apparent contradiction when you add your own labels/axes and interpretation to the graph.

What happens when you break "learning" down into specific tasks? Is the compound learning of lots of simple tasks accurately represented by the generic "learning" graphs for complex tasks?

Steep may not be the correct word. Complex may be better.

That is actually the complaint I have seen most often with command lines - you need to learn so many commands at the very beginning to do anything useful; thus producing a steep curve. Once you have the absolutely necessary basic commands down (cd, pwd, ls, mkdir, chmod, and so on) you can easily add more commands gradually, as you need them.

The progress is the x axis, and the difficulty is the y axis.

It's only erroneous if you define the axes as such.

I feel like the graph is a poor choice in illustrating this idea. As it's central to the argument, the rest of the article falls apart, in my eyes.

First - why do both graphs start the user with 0% performance? Humans come into a new program/context/etc with prior experience. Put a child who's used Word before in front of ed, and he'll probably do well. Put him in front of vim, and he'll probably struggle to write a sentence.

Second, not all performances are created equal. The dependent axis should probably be some measurable output. Typically, something with a "steep learning curve" takes longer to master, but has much greater payout in the end. (If it doesn't, people generally don't learn it.) Compare making TV Dinner with cooking a family meal. The TV dinner is easier to learn, easier to master, but having "100% performance" in making TV Dinners is still not on the level of being even a good cook.

"I feel like the graph is a poor choice in illustrating this idea. As it's central to the argument, the rest of the article falls apart, in my eyes."

What, in your eyes, does the word "curve" refer to then?

I've always pictured a steep learning curve as this - http://imgur.com/8Wfih. One doesn't learn much at first until the "aha" moment. So I think the metaphor is still correct.

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