4 ms·

> I just can't see Kurzweil being in the same league as Peter Norvig. The problem with Peter Norvig is that he comes from a mathematical background and is a st

by slacka 14y ago

> I just can't see Kurzweil being in the same league as Peter Norvig. The problem with Peter Norvig is that he comes from a mathematical background and is a strong defender the use of statistical models that have no biological basis.[1] While they have their use in specific areas, they will never lead us to a general purpose strong AI. Lately Kurzweil has come around to see that symbolic and bayesian networks have been holding AI back for the past 50 years. He is now a proponent of using biologically inspired methods similar to Jeff Hawkins' approach of Hierarchical Temporal Memory. Hopefully, he'll bring some fresh ideas to Google. This will be especially useful in areas like voice recognition and translation. For example, just last week, I needed to translate. "I need to meet up" to Chinese. Google translates it to 我需要满足, meaning "I need to satisfy". This is where statistical translations fail, because statistics and probabilities will never teach machines to "understand" language. [1] http://www.tor.com/blogs/2011/06/norvig-vs-chomsky-and-the-fight-for-the-future-of-ai http://www.tor.com/blogs/2011/06/norvig-vs-chomsky-and-the-f...

raverbashing 14y ago
" the use of statistical models that have no biological basis." this is irrelevant This is like saying a computer using an x86 processor is different, from the point of view of the user than an ARM computer, beyond differences in software Or like saying DNA is needed for "data storage" in biological systems and not another technology Sure, you can get inspiration from biology, but doesn't necessarily mean you have to copy it. ""I need to meet up" to Chinese. Google translates it to 我需要满足, meaning "I need to satisfy". This is where statistical translations fail, " It's not really a fault of statistical translations (more likely quality of data issue), even though it has its limitations. Besides, google translation has been successful exactly because it's better than other existing methods (and Google has the resources, both in people and data to make it better)
- jacquesm 14y ago
  I think that the google translator did pretty good on that fragment. Garbage in, garbage out! If you use 'I' in a sentence fragment when you mean to use 'We' then you can't really blame the translator for getting it wrong. 'We need to meet up' is a sentence with a completely different meaning from the incorrect and semantically confusing 'I need to meet up', it really does sound as if you need to meet up to some expectation.
  - trevelyan 14y ago
    In further defense of Google, "I need to meet up with him" translates as 我需要与他见面. If someone wants to attack Google's Chinese translation, it should be over snippets like 8十多万 or its failure to recognize many personal and place names which could easily be handled by a pre-processor. Google has never been competent in China in part because of their hiring decisions, but this isn't Franz Och's fault.
nostrademons 14y ago
For several hundred years, inventors tried to learn to fly by creating contraptions that flapped their wings, often with feathers included. It was only when they figured out that wings don't have to flap and don't need feathers that they actually got off the ground. It's still flight, even if it's not done like a bird. Just because nature does it one way doesn't mean it's the only way. (On a side note, multilayer perceptrons aren't all that different from how neurons work - hence the term "artificial neural network". But they also bridge to a pure mathematical/statistical background. The divide between them is not clear-cut; the whole point of mathematics is to model the world.)
- Confusion 14y ago
  > aren't all that different from how neurons work Nobody knows how neurons actually work: http://www.newyorker.com/online/blogs/newsdesk/2012/11/ibm-brain-simulation-compass.html http://www.newyorker.com/online/blogs/newsdesk/2012/11/ibm-b.... We are missing vital pieces of information to understand that. Show me your accurate C. Elegans simulation and I will start to believe you have something. Perhaps in a hundred years, this is the argument: for several hundred years, inventors tried to learn to build an AI by creating artificial contraptions, ignoring how biology worked, inspired by an historically fallacious anecdote about how inventors only tried to learn to fly by building contraptions with flapping wings. It was only when they figured out that evolution, massively parallel mutation and selection, is actually necessary that they managed to build an AI.
  - kanzure 14y ago
    > Show me your accurate C. Elegans simulation and I will start to believe you have something. http://openworm.org/ http://openworm.org/ If you think they are insufficiently accurate, submit a pull request.
- slacka 14y ago
  > For several hundred years, inventors tried to learn to fly by creating contraptions that flapped their wings... To quote Jeff Halwkings "This kind of ends-justify-the-means interpretation of functionalism leads AI researchers astray. As Searle showed with the Chinese Room, behavioral equivalence is not enough. Since intelligence is an internal property of a brain, we have to look inside the brain to understand what intelligence is. In our investigations of the brain, and especially the neocortex, we will need to be careful in figuring out which details are just superfluous "frozen accidents" of our evolutionary past; undoubtedly, many Rube Goldberg–style processes are mixed in with the important features. But as we'll soon see, there is an underlying elegance of great power, one that surpasses our best computers, waiting to be extracted from these neural circuits. ... For half a century we've been bringing the full force of our species' considerable cleverness to trying to program intelligence into computers. In the process we've come up with word processors, databases, video games, the Internet, mobile phones, and convincing computer-animated dinosaurs. But intelligent machines still aren't anywhere in the picture. To succeed, we will need to crib heavily from nature's engine of intelligence, the neocortex. We have to extract intelligence from within the brain. No other road will get us there. " As someone with a strong background in Biology who took several AI classes at an Ivy League school, I found all of my CS professors had a disdain for anything to do with biology. The influence of these esteemed professors and the institution they perpetuate is what's been holding the field back. It's time people recognize it.
grepherder 14y ago
Obviously Google translate is not error free, nor is any statistical translation system going to be comparable to a human translator in the very near future, but you're underestimating the current development of statistical translation. Granted, I'm not a native speaker but I think "I need to meet up" is not even a sentence with proper grammar. Underlying model probably predicted something like meeting (satisfying) requirements due to the lack of an object in the sentence and context. Situations like this where the input is very short and noisy is obviously going to be a weakness of statistical systems for a long time to come, but looking at technologically how far we are from mastering biological systems, I think it's safe to say this is going to be the way of doing it for a while, and will be very successful in translating properly structured texts if proper context can be provided. Currently statistical translations have (almost) no awareness of context beside some phrase-based or hierarchical models. Many people are probably not factoring in the fact, that with exponentially more data, and exponentially higher computing power, a model can utilize the context of a whole book while translating just a sentence from that book - which is actually still much less than what human translators utilize in terms of context. While translating a sentence, I might even have to utilize what was on the news the night before to infer the correct context. We are currently definitely far from feeding this kind of information to our models, so I'd say this kind of criticism towards statistical translation is very unfair.
- slacka 14y ago
  "We need to meet up" also translates incorrectly "我们需要满足". In fact, I did not originally use a fragment, I wrote a full sentence that Google repeatedly incorrectly translated. I only used a fragment here to simply my example. To avoid the wrath of the Google fan boys, a better example would have been the pinnacle of statistical AI : The category was "U.S. Cities" and the clue was: "Its largest airport is named for a World War II hero; its second largest for a World War II battle." The human competitors Ken Jennings and Brad Rutter both answered correctly with "Chicago" but IBM's supercomputer Watson said "Toronto." Once again, Watson, a probability based system failed where real intelligence would not. Google has done an amazing job, with their machine translation considering they cling to these outdated statistical methods. And just like with speech recognition has found out over the last 20 years, they will continue to get diminishing returns until they start borrowing from nature's own engine of intelligence.

by slacka 14y ago

raverbashing 14y ago

" the use of statistical models that have no biological basis." this is irrelevant This is like saying a computer using an x86 processor is different, from the point of view of the user than an ARM computer, beyond differences in software Or like saying DNA is needed for "data storage" in biological systems and not another technology Sure, you can get inspiration from biology, but doesn't necessarily mean you have to copy it. ""I need to meet up" to Chinese. Google translates it to 我需要满足, meaning "I need to satisfy". This is where statistical translations fail, " It's not really a fault of statistical translations (more likely quality of data issue), even though it has its limitations. Besides, google translation has been successful exactly because it's better than other existing methods (and Google has the resources, both in people and data to make it better)

jacquesm 14y ago
I think that the google translator did pretty good on that fragment. Garbage in, garbage out! If you use 'I' in a sentence fragment when you mean to use 'We' then you can't really blame the translator for getting it wrong. 'We need to meet up' is a sentence with a completely different meaning from the incorrect and semantically confusing 'I need to meet up', it really does sound as if you need to meet up to some expectation.
- trevelyan 14y ago
  In further defense of Google, "I need to meet up with him" translates as 我需要与他见面. If someone wants to attack Google's Chinese translation, it should be over snippets like 8十多万 or its failure to recognize many personal and place names which could easily be handled by a pre-processor. Google has never been competent in China in part because of their hiring decisions, but this isn't Franz Och's fault.

nostrademons 14y ago

For several hundred years, inventors tried to learn to fly by creating contraptions that flapped their wings, often with feathers included. It was only when they figured out that wings don't have to flap and don't need feathers that they actually got off the ground. It's still flight, even if it's not done like a bird. Just because nature does it one way doesn't mean it's the only way. (On a side note, multilayer perceptrons aren't all that different from how neurons work - hence the term "artificial neural network". But they also bridge to a pure mathematical/statistical background. The divide between them is not clear-cut; the whole point of mathematics is to model the world.)

Confusion 14y ago
> aren't all that different from how neurons work Nobody knows how neurons actually work: http://www.newyorker.com/online/blogs/newsdesk/2012/11/ibm-brain-simulation-compass.html http://www.newyorker.com/online/blogs/newsdesk/2012/11/ibm-b.... We are missing vital pieces of information to understand that. Show me your accurate C. Elegans simulation and I will start to believe you have something. Perhaps in a hundred years, this is the argument: for several hundred years, inventors tried to learn to build an AI by creating artificial contraptions, ignoring how biology worked, inspired by an historically fallacious anecdote about how inventors only tried to learn to fly by building contraptions with flapping wings. It was only when they figured out that evolution, massively parallel mutation and selection, is actually necessary that they managed to build an AI.
- kanzure 14y ago
  > Show me your accurate C. Elegans simulation and I will start to believe you have something. http://openworm.org/ http://openworm.org/ If you think they are insufficiently accurate, submit a pull request.
slacka 14y ago
> For several hundred years, inventors tried to learn to fly by creating contraptions that flapped their wings... To quote Jeff Halwkings "This kind of ends-justify-the-means interpretation of functionalism leads AI researchers astray. As Searle showed with the Chinese Room, behavioral equivalence is not enough. Since intelligence is an internal property of a brain, we have to look inside the brain to understand what intelligence is. In our investigations of the brain, and especially the neocortex, we will need to be careful in figuring out which details are just superfluous "frozen accidents" of our evolutionary past; undoubtedly, many Rube Goldberg–style processes are mixed in with the important features. But as we'll soon see, there is an underlying elegance of great power, one that surpasses our best computers, waiting to be extracted from these neural circuits. ... For half a century we've been bringing the full force of our species' considerable cleverness to trying to program intelligence into computers. In the process we've come up with word processors, databases, video games, the Internet, mobile phones, and convincing computer-animated dinosaurs. But intelligent machines still aren't anywhere in the picture. To succeed, we will need to crib heavily from nature's engine of intelligence, the neocortex. We have to extract intelligence from within the brain. No other road will get us there. " As someone with a strong background in Biology who took several AI classes at an Ivy League school, I found all of my CS professors had a disdain for anything to do with biology. The influence of these esteemed professors and the institution they perpetuate is what's been holding the field back. It's time people recognize it.

grepherder 14y ago

Obviously Google translate is not error free, nor is any statistical translation system going to be comparable to a human translator in the very near future, but you're underestimating the current development of statistical translation. Granted, I'm not a native speaker but I think "I need to meet up" is not even a sentence with proper grammar. Underlying model probably predicted something like meeting (satisfying) requirements due to the lack of an object in the sentence and context. Situations like this where the input is very short and noisy is obviously going to be a weakness of statistical systems for a long time to come, but looking at technologically how far we are from mastering biological systems, I think it's safe to say this is going to be the way of doing it for a while, and will be very successful in translating properly structured texts if proper context can be provided. Currently statistical translations have (almost) no awareness of context beside some phrase-based or hierarchical models. Many people are probably not factoring in the fact, that with exponentially more data, and exponentially higher computing power, a model can utilize the context of a whole book while translating just a sentence from that book - which is actually still much less than what human translators utilize in terms of context. While translating a sentence, I might even have to utilize what was on the news the night before to infer the correct context. We are currently definitely far from feeding this kind of information to our models, so I'd say this kind of criticism towards statistical translation is very unfair.

slacka 14y ago
"We need to meet up" also translates incorrectly "我们需要满足". In fact, I did not originally use a fragment, I wrote a full sentence that Google repeatedly incorrectly translated. I only used a fragment here to simply my example. To avoid the wrath of the Google fan boys, a better example would have been the pinnacle of statistical AI : The category was "U.S. Cities" and the clue was: "Its largest airport is named for a World War II hero; its second largest for a World War II battle." The human competitors Ken Jennings and Brad Rutter both answered correctly with "Chicago" but IBM's supercomputer Watson said "Toronto." Once again, Watson, a probability based system failed where real intelligence would not. Google has done an amazing job, with their machine translation considering they cling to these outdated statistical methods. And just like with speech recognition has found out over the last 20 years, they will continue to get diminishing returns until they start borrowing from nature's own engine of intelligence.