Some of these functions are easy to compose to get something impressive, true. But that is not often the case. Take the case for games and imagine we wanted a meta-algorithm to select an algorithm to apply to each game. The intelligence would then shift into, how does one select the correct algorithm for the current game in the shortest time possible?
There was a recent blog post covering this and the difficulties involved:
I also posted a link (https://arxiv.org/pdf/1604.00289v2.pdf) above which is an easily readable exposition on just how current approaches fall short. It's nothing so trivial as "it's just not what we're used to".
> Take the case for games and imagine we wanted a meta-algorithm to select an algorithm to apply to each game.
I take it you haven't seen the previous accomplishment of Deep Mind before they tackled Go. They used a Reinforcement Learning algorithm to play 50 Atari games - the same algo - with great results. They really created a generic learning algorithm.
I'm fully informed about this area of research. Including other research that found simple linear methods could also get good results over a large number of games and DeepMind's recent work where far less computationally involved methods as random projections and Nearest neighbors outperformed Deep Reinforcement learners at the more complex 3D mazes and Frostbite.
But like I keep emphasizing, you can't take a neural net trained on space invaders and have it play Asteroids because each is a task specialized program that was the result of a search. While the search method is more general, the resulting program is not. You can use a single algorithm as simple as linear methods based reinforcement learning and get great results across a wide swathe of tasks but you can't claim to have found a universal learner.
There was a recent blog post covering this and the difficulties involved:
http://togelius.blogspot.ca/2016/08/algorithms-that-select-w...
I also posted a link (https://arxiv.org/pdf/1604.00289v2.pdf) above which is an easily readable exposition on just how current approaches fall short. It's nothing so trivial as "it's just not what we're used to".