When information overload occurs, pattern recognition is how to determine truth.
The multiplicity of facts and writings is become so great that every thing must soon be reduced to extracts and dictionaries.
Ultimately, no one can extract from things, books included, more than he already knows.
The goal is to turn data into information, and information into insight.
Those with engineering skills will build tomorrow's genius computers. But those with the ability to create knowledge of any kind will be the ones who are best able to extract great value from them. The way to create value in the age of genius machines will be to compile and disseminate knowledge that other people will find useful.
I have somewhere seen it observed that we should make the same use of a book that the bee does of a flower: she steals sweets from it, but does not injure it.
If you want access to the files of valuable information in a computer, you must understand how to retrieve the data by asking for it with the proper commands. Likewise, what enables you to get anything you want from your own personal databanks is the commanding power of asking questions.
Knowledge is a sacred cow, and my problem will be how we can milk her while keeping clear of her horns.
AI2 was born from a desire to create a system that could truly reason about knowledge, rather than just offer up what had been written on a subject before.
The problem with Google is you have 360 degrees of omnidirectional information on a linear basis, but the algorithms for irony and ambiguity are not there. And those are the algorithms of wisdom.
I almost think of nerd brains as rattlesnake venom; like, you can milk it. You can milk the pulpy venom out of the nerd brain and use it for good if you want to.
Data is not intelligence.
What I've learned about teaching is to refer back to the root of that word, which is educo, which means "to pull from." Education does not mean jamming information into somebody's head. Rather, it's that ancient idea that all knowledge is within us; to teach is to help somebody pull it out of themselves.
Knowledge exists in minds, not in books. Before what has been found can be used by practitioners, someone must organize it, integrate it, extract the message.
You take the noise and put it in data information knowledge, and you get insight from that knowledge. How to execute the trade, the timing, sizing, long, short, and then you risk manage it.
Most people in AI, particularly the younger ones, now believe that if you want a system that has a lot of knowledge in, like an amount of knowledge that would take millions of bits to quantify, the only way to get a good system with all that knowledge in it is to make it learn it. You are not going to be able to put it in by hand.
All true histories contain instruction; though, in some, the treasure may be hard to find, and when found, so trivial in quantity, that the dry, shriveled kernel scarcely compensates for the trouble of cracking the nut.
