Well we understand it (mostly) now, but at the time it was created: Specifics of aerodynamics required for flight.[1]

The Wright brothers basically mimicked nature and tested till they got it right. They didn't use Bernoulli's equation to create a NACA airfoil and L/D max calculations. [2].

To that end, in one of my undergraduate Aeronautics classes, when we were doing a bunch of Bernoulli equations someone calculated that based on that approach helicopters couldn't actually fly. To which our instructor said, "Oh yea, helicopters fly by magic."

[1]https://secretofflight.wordpress.com/incorrect-theories/ [2]https://wright.nasa.gov/overview.htm

This is a decent example, but it's not quite comparable to developing general AI. We can build helicopters without a full conceptual understanding of aerodynamics, but we can't produce a physical simulation which will accurately determine which helicopter designs will and will not fly for any given design without a full understanding of aerodynamics under the conditions in which helicopters operate.

Creating aerodynamics simulation is creating an aerodynamic system. Creating a general intelligence is creating an intelligent system. A better analog to creating a helicopter using incomplete understanding of aerodynamics is something like creating a system that can discriminate between what is and isn't a general intelligence. We don't have such a system, but it at least doesn't seem like the sort of thing that would require a full understanding of intelligence.

That seems at best totally unrelated? You've asserted that general AI is some sort of different category than other things which we can and have built without understanding the fundamentals of their operation. But you haven't actually provided any real evidence of that.

Humor.

Interesting. Of course, computers tell bad jokes, I'm told.

Visual processing also comes to mind. Computers can "see", but it's very difficult to understand the many rules humans use to parse their environment, thus computers are not yet at human level of visual processing.

Makes me wonder if those things aren't "solved" by our brains, but we just don't quite know how. Another thing worth noting is that I know people who have no sense of humor. They simply do not understand jokes or sarcasm.

Unsupervised joke generation from big data:

http://homepages.inf.ed.ac.uk/s0894589/petrovic13unsupervise...

You mean humans doing humor? Because humans also do intelligence. Computers don't do humor.

What I meant by my comment was, I understood stinkytaco's comment as a request for a specific task that humans do (so, nothing as nebulous as "intelligence") that hasn't been "conceptually solved" with something like a walk through a search tree.

Even Go was "conceptually solved" decades ago- it's "only" iterating over a search tree; we were just waiting for the hardware and tree-pruning algorithms to catch up. Humor and other human tasks can be more precisely defined but still don't even have theoretical algorithms to solve them.

I didn't mean that humans can't do things that haven't been conceptually solved. I agree that humans can do humor and that humor isn't conceptually solved, so I'll assume that you mean to say that we can make computers do humor, even though it isn't conceptually solved.

I disagree that we can make computers do humor. Kobeya linked to a paper that shows unsupervised joke generation, but it's a stretch to call this humor. I haven't explored beyond what's in that article, but the examples of jokes produced do little to convince me that this program is doing humor:

I like my relationships like I like my source, open

I like my coffee like I like my war, cold

I like my boys like I like my sectors, bad

Show me an AI that can produce original joke forms and I'll reevaluate.

Go was not conceptually solved decades ago if it required the development of new tree-pruning algorithms to work. Improving an algorithm is conceptual innovation. If it could have worked with only a few orders of magnitude difference in performance, or with only marginally inferior results without new algorithms then maybe it was conceptually solved decades ago, else no.

You study these things on fixed forms because it is easier to analyze and understand what is going on. But the extension to general joke-making is straight forward, provided you have working NLP (a big if!): data-mine the concept graph for thoughts/memes which have similar structural content but surprisingly (in the technical sense) different meanings, then construct a sentence that maximizes probability during reading of the first interpretation but ends with the second. This captures basically all humor from puns to "I like my X like I like Y: Z" to most forms of stand-up comedy.