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When Will My Computer Understand Me?
aarondubrow writes "For more than 50 years, linguists and computer scientists have tried to get computers to understand human language by programming semantics as software, with mixed results. Enabled by supercomputers at the Texas Advanced Computing Center, University of Texas researchers are using new methods to more accurately represent language so computers can interpret it. Recently, they were awarded a grant from DARPA to combine distributional representation of word meanings with Markov logic networks to better capture the human understanding of language."
Should a computer understand us when we can't understand each other?
Non bene pro toto libertas venditur auro
Instead of trying to build computers that can understand us, we should be building computers that can learn based on stimuli. If a computer can somehow see, and hear, at the very least and it could somehow capture this information and then over time, develop algorithms to make sense of these things. You know.. the code it would generate could then be used ... Anyways, sounds crazy, but, to me, it makes more sense that way. After all, we didn't just 'communicate' instantly, we learned over time.
It was on Star Trek only because tv and movies are dialogue driven media. But in reality voice limits input
Take the Siri sports example
Ask for your team scores
Get scores
Open app for detailed sports news
Or just open the app and get the scores and news in one step. Same with any other data. Modern GUI's can present a lot more data faster than using voice to ask for the data
voice recognition will need to be a lot better
Instead of translating a human natural language to an interpretation in binary space, why not construct a conlang that sits in the middle. No expressions with double interpretation like in natural language, but also no command-line sentences that mimic for-loops and the like.
Take the best of 2 worlds.
I do not fail; I succeed at finding out what does not work.
I'm struck by how much more accurate and responsive Dragon Naturally Speaking was in 1999 on my Pentium 2 than is Siri on my iPhone 5 and Apple's cloud servers today. Maybe it's a microphone problem, but in that case why was the $4.99 tiny microphone from Radioshack in 1999 better than the microphone in my iPhone 5 today?
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M-x doctor
Other people don't understand WTF you're talking about either, they're just better at faking it.
Live today, because you never know what tomorrow brings
Each time I've researched NLP solutions, the full sensory experience is ultimately found to play a role in full context and meaning. This begins in a very tight locale, and expands outward, or hopping around locations/time as part of context.
Instead, when most solutions attempt to pick a "general corpus" of a language, they pick such a general version of the language that contextual associations are difficult to follow for any conversation. Even the most ubiquitous vocabulary, such as in national broadcast news, there are assumptions that point all the way back to simplistic models of our experiences via sight/hearing, taste/smell, touch/movement and planning/expectation. Even in our best attempts, nothing such as metaphor or allusion is followed well, and only the most robotic - formal - language understood. This interaction is certainly nothing "natural".
I don't believe NLP problems will be (as easily) solved until we begin to solve the "general stimulus" for input, storage, searching and recall across the senses that humans have - their true "natural" habitat that language is describing. So that when apple goes from "round" to "red" to "about 4in" to "computer" to "beatles" to "not yet in season here" to "sometimes bitter" to "my favorite of grandma's pies", etc - and onward, like potential quantum states until the rest of the conversation collapses most of them - we may be able to get a computer to really understand natural language. This isn't possible in just the manipulation of pieces of text and pointers.
When computer scientist guys understand what it means to understand. Go read some epistemology books. You'll understand.
When will my computer understand me?
I am sorry, but I do not know when Mike's uterus will unhand you.
in the likeness of a man's mind. -Orange Catholic Bible
When will your computer understand you? Not for awhile.
Speech recognition is a part of AI, to the extent that the computer understands what you're saying. Sure, programs like SIRI or ELIZA can put words together, but only so long as we can anticipate the form and context of the question. SIRI only knows about the things it has been programmed to do, which is (unfortunately) not nearly the amount we expect an intelligence to do.
AI has languished for about 60 years now, mostly because it is not a science. There is no formal definition of intelligence, and no roadmap for what to study. As a result, the field studies everything-and-the-kitchen-sink and says: "this is AI!".
Contrast with, for example, Complexity: a straightforward definition drives a rich field of study, producing many interesting results.
In this particular misguided example, they are using Markov logic networks, even though the human brain does not make the Markov assumption(*). We have no definition for intelligence, and the model they work on is demonstrably different from the only real-world example we know of. This may be interesting mathematical research, but it isn't about AI.
Not to worry - most AI research isn't really related to AI.
This is why your computer doesn't understand you, and won't for quite some time.
(*) Check out Priming and note that psychologists have measured priming effects three days (!) after the initial stimulus.
Is the current "lack" of power of current computers an excuse for not being able to make a "clever" computer? In other words, is main the problem computer power or is it the design of algorithms that run on the computer (Power vs method)? Hard to say until someone realizes that clever computer, but the recent "history" of electronic devices would let me think the problem is the method (algorithm).
Slashdot, fix the reply notifications... You won't get away with it...
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Most linux users don't know this, but the man pages were named after Chuck Norris. Chuck Norris fsck'ing hates noobs!
Sure, because
Computer, insert line... int line counter plus equals copy to tables bracket tables dot primary, comma tables uh, arrow thingy... last... comma sequelconne... no no, not that, erase last... ess que ell connection comma date helper bracket current date time bracket brack... uh, close bracket comma get cutoff bracket close bracket close bracket, semicolon.
Sounds so much easier than a keyboard and autocomplete.
I'll repeat what I said in a related thread:
"Larry Page's advisor at Stanford, Terry Winograd, wrote a book with Fernando Flores in 1987 titled Understanding Computers and Cognition.
It is a profound critique of the mental representation approach, based on biological and philosophical considerations. A must read for anybody interested in the AI field."
Language is not precise and computers like precision. The same words can mean entirely different things depending on context, where the speaker is from, how they say the words, etc. Furthermore, language evolves at a very rapid rate, new words are created on a daily basis. We're used to language and the vagueness that it implies but that translates very poorly to computer logic and it will never be perfect because it relies on variables that the computer will never know.
Taxation is legalized theft, no more, no less.
Currently, there are not even any convincing theories how strong AI could be implemented. Thatindicates this is >50 years inthe future, but alsocould be >1000 years or never. There may be fundamental physical limits on play here. All the people promising this based on NLP databases, Markov Modells, etc. are lying and usually know it.
Most ACs are not even worth the keystrokes to insult them. Be generically insulted by this and ignored otherwise.
I've theorized AI before back in like 2002. I figure Natural Language is straightforward if you describe it in a 3d imagination. My old page I'm not really tempted to get into AI as a solo project though myself as it would be over a life time of coding, and there is no profit in it until you have it completed. What point is there in being intelligent, hard working and broke?
God spoke to me
A server full of porn. Mission accomplished.
Have gnu, will travel.
Saw this article and the one about PRISM, thought for a moment thaat it said:
"When Will My Government Understand Me?"
And no, Offtopic is not what this is.
Switch to an easier to understand, universal language (both easier for computers and humans). Simple solution. Have everyone learn it. You're trying to put a rectangular prism into a round hole. Until we have a rectangular hole, use a cylinder...
The G
Wrong.
DATABASE WOW WOW
The real problem with language recognition is context. When we talk, our spoken words contain half of what we mean. The rest depends on external parameters, from our body language, to the time and place at the moment.
So, unless a computer can understand the same context, there is not gonna be serious language recognition, as we see it in sci fi.
Ziggy says there is a 85.45% chance of that being true
kowai desu ne. ano hito ha hontani hen desu ne.
zenzen kawakunai.
HELP MY ACCOUNT HAS BEEN HACKED BY AN ILLIBERAL ART STUDENT SET TO DESTROY THE INTERWEBZ!
...how well do you understand your computer? A relationship is a two-way street, you know.
That's not what he is taking about. Have a look at this video of Steve Jobs is hacking together a database app by some drag&drop on a NeXTStep machine 20 years ago to get a sense of what he is getting at.
It's not like the computing world hasn't made any progress, as a lot of the stuff demoed back then is now more or less common place in every OS, which wasn't the case back then, but at least as far as desktops are concerned we haven't really made much progress beyond that. Human/computer interaction is still much the same as demoed back then.
ANSWER: When you learn machine code.
Your thin skin doesn't make me a troll
and thank God for that.
I can't imagine how this technique could go beyond a graph of word associations to an understanding of their meaning. The graph seems to be entirely self-referential.
Humans do it the other way - if they are asked about the relatedness of words, they infer it from the meanings they attribute to the words.