4 ms·

In this context, I'm referring to logic as a knowledge base or some similar. Either this information has to be manually curated (which doesn't scale), have huge

In this context, I'm referring to logic as a knowledge base or some similar. Either this information has to be manually curated (which doesn't scale), have huge gaps, or have loads of false positives (ie incorrect information). No one is arguing whether we should or shouldn't be using programming languages or hardware based in logic.

YeGoblynQueenne 11y ago
Alright, thank you for clarifying this. I guess I got carried away a bit last night and allowed this to escalate to a sort of unproductive us-vs-them kind of thing. Sorry about that. It wasn't my intention and some of my replies from yesterday are a bit shit. Now- >> this information has to be manually curated (which doesn't scale) Aye, that's exactly right. I know this first-hand. I study machine learning, so I'm not really-really against it (it works, so). My hunch though is that although we've made a lot of progress with what Hinton calls "analogy" (and what I think he means to call "approximation") we haven't gone _that_ far and every time we made some progress we've eventually hit a wall. Take language for instance: first there was the Bag of Words model, language as independent words. That worked for a while, but eventually it hit a wall. Now we got word embeddings. It gives great results and yes, you can do arithmetic with words, which is major, but then we still haven't made real progress. And note that the top-rated NLP system currently, Watson (Schoher's grad student obviously didn't make something as performant as that) combines statistical learning with knowledge engineering. They use a statistical parser to fill in frame [1] slots. Each approach handles different aspects of the problem. Both together cover more of the solution than each on its own can ever hope to. If you want to do really well, you need to pick the best of both worlds, approximation and knowledge, together. [1] https://en.wikipedia.org/wiki/Frame_%28artificial_intelligence%29 https://en.wikipedia.org/wiki/Frame_%28artificial_intelligen...

YeGoblynQueenne 11y ago

Alright, thank you for clarifying this. I guess I got carried away a bit last night and allowed this to escalate to a sort of unproductive us-vs-them kind of thing. Sorry about that. It wasn't my intention and some of my replies from yesterday are a bit shit. Now- >> this information has to be manually curated (which doesn't scale) Aye, that's exactly right. I know this first-hand. I study machine learning, so I'm not really-really against it (it works, so). My hunch though is that although we've made a lot of progress with what Hinton calls "analogy" (and what I think he means to call "approximation") we haven't gone _that_ far and every time we made some progress we've eventually hit a wall. Take language for instance: first there was the Bag of Words model, language as independent words. That worked for a while, but eventually it hit a wall. Now we got word embeddings. It gives great results and yes, you can do arithmetic with words, which is major, but then we still haven't made real progress. And note that the top-rated NLP system currently, Watson (Schoher's grad student obviously didn't make something as performant as that) combines statistical learning with knowledge engineering. They use a statistical parser to fill in frame [1] slots. Each approach handles different aspects of the problem. Both together cover more of the solution than each on its own can ever hope to. If you want to do really well, you need to pick the best of both worlds, approximation and knowledge, together. [1] https://en.wikipedia.org/wiki/Frame_%28artificial_intelligence%29 https://en.wikipedia.org/wiki/Frame_%28artificial_intelligen...