From: Ben Goertzel <MIla- To: "jeffrey E." <[email protected]> Cc: Joscha Bach Subject: Re: Date: Mon, 07 Sep 2015 00:16:40 +0000 Hi, I agree w/ Joscha's caution about discrimination tasks: They can be often be solved rather well, but in devious ways, by statistical supervised learning algorithms. Suppose you pose a linguistic discrimination task of some sort -- and a supervised learning algorithm, trained on a mass of data, can solve it with 97% accuracy. The algorithm's pattern of errors may indicate to YOU, intuitively, that it doesn't really understand what's going on. But then, it may be that the average person solves the task with only 95% accuracy, though with a different pattern of errors that indicates intuitively they have a different kind of understanding... I like the idea of a language learning challenge, but posing it properly seems tricky. As soon as something becomes a "challenge", one has to worry about protecting against various subterfuges (deception, once again!). Suppose one poses a challenge to learn a language from an un-annotated corpus of texts. OK, but then some nefarious clever person can try to solve this using an algorithm whose parameters were all carefully tuned via analysis of an annotated corpus in that same language. And these parameters may be quite complex structures. The winning approach would then not be able to work on another language for which there was no large annotated corpus (no Penn Treebank analogue, etc.). It seems that challenges are easier to formulate for engineering breakthroughs than science breakthroughs... Here is one idea, off the top of my head.... Perhaps at least it can stimulate thoughts .... This is not about language learning, though, it's about recognizing and generating coherent, meaningful language.. I) Show human subjects some videos of game characters carrying out certain sequences of behaviors in a video-game environment 2) For each behavior-sequence B, ask th