Cool article. I really like repetition that model complexity is not a pancea. Seems like the industrial AI/ML movement as a whole has gone down a road where practitioners will, by default, throw the most powerful model they know at a problem and see how it pans out. Works well on benchmarks(if you regularize/validate carefully) but isn't a very sustainable way to engineer a system.
Separately, I do find it curious that his list of "pretty standard machine-learning methods" included Logistic Regression, K-means and....deep neural nets? Sure they're white hot in terms of popularity and the experts have done astounding things, but unless I've missed some major improvements in their off-the-shelf usability they strike me as out of place in this list.
Deep convolutional neural nets are the staple method for companies doing computer vision at scale. Google, Facebook, Yahoo, and Baidu make extensive use of them. In 2014 they definitely deserve their place on the shortlist of "pretty standard machine-learning methods". They are the current state of the art for visual recognition in particular (see the results on ImageNet from the past few years).
They have also been commoditized by libraries such as Theano (Python) and Torch (Lua). Google and Facebook use their own tools based on Torch.
My own version of the shortlist would be:
Logistic regression, KNN, random forests, SVM, and deep convnets.
In addition, I don't know why anyone would think Google is going to make all the AI, just because today's most notable, state-of-the-art AI systems are made by a few big companies with the resources to fund large teams of experts for years. Fifty years ago this article could have been called "Untapped opportunities in software" (or operating systems) and talked about IBM -- is there software IBM can't or won't write for us?
That's fair pushback re how "standard" deep learning is. That said, those methods are rapidly establishing themselves as the go-to for applications in speech, vision, and text. At least for those outfits that can afford the substantial dev costs.
I do find it curious that his list of "pretty standard machine-learning methods" included Logistic Regression, K-means and....deep neural nets?
The next sentence is much more curious to me: "the point is that they’re widely available in high-quality open source packages" because I have yet to find a proper well documented well maintained non-toy open source deep neural network implementation.
Massive datasets do outperform clever theories... but I think that's just because no one has yet worked out the theories that work best with the data. This requires insight, in addition to data, and could come from anyone.
The alternative - that massively complex probabilistic models are the best theory of the data - is hopefully not true. Especially not of our minds. But it could be true, and if so, it would mean that our intelligence is irreducible, and we are forever beyond our own self-understanding (even in principle). Our history is full of inexplicable mysteries that were eventually understood. But not all: quantum randomness. I really hope intelligence is will be one of the former.
There are a lot of AI problems that can be solved with less than human intelligence but some human numbers for reference:
By the time you're 30 you have been exposed to:
~1.4 petabytes of visual information
~1.8 terabytes of auditory information
Touch and proprioceptive bandwidth is harder to calculate but the ascending pathway through the spinal cord is about 10 million fibers, which is 10x the optic nerve (Or 5x the number of fibers from both eyes). So:
Between 1.4 and 14 petabytes of touch and proprioceptive information.
So we're a fairly large data problem on top of millions of years of evolution that have baked in some knowledge and abilities.
No reason to be so pessimistic about quantum randomness. Quantum theory is barely 100 years old and our understanding of it is still evolving massively. Though the latter is not always appreciated by the public.
I don't see much cause for optimism. Human intelligence is the end result of tens of millions of years of evolution. That software project you hacked on for a few months until it worked really well? Now multiply that by about seven orders of magnitude. You simply can't comprehend how much trial and error led us to the state we're in now. To think that we could reverse engineer ourselves in the span of a few centuries always seemed pretty naive to me.
I can honestly say that this post has revolutionized my thoughts on AI. Primarily this is because of what I perceive as the thesis statement, which is:
"<AI> is the construction of weighted tables (choices, data, meta relations, whatever) from large sets of <prior data> by <method>"
This is kind of crazy, because I think it says you could make a Turing AI by using large datasets of prior life data for humans. In essence, "<my life> is the construction of weighted tables from large sets of <life experience> by <human learning>." For example, if you had an AI that could learn through text, you could have extensive transcribed conversation logs of people and then large time-activity logs to use as your inputs.
If it could learn through video (IE, it could view images, understand objects, object relations, events in time, and assign will to the person behind actions / events) then you could instead just feed it huge video logs of people's lives. If you wanted a copy of a person, you could feed it only a single individual, and if you wanted a more general AI, then you could feed it cross sections of the population.
In addition, there's a very cool meta aspect to the large dataset concept, in that it can be large datasets for when to use, or to feed data to, specialized sub-AI's. For example, you might have a math sub-AI that has been trained by feeding it massive sets of math problems (or perhaps it can learn math through the video life logs of a person?). If its then being used as a part of a larger piece, then you'd want to know when to use it to solve problems, or when to feed it experience inputs for further learning. In essence, its tables of categories for experience types, and then grown / paired sub-AI's for those types.
I would wager that it is possible, right now, to create a chatbot that can pass Turing using the above by feeding it the equivalent of mass IRC chat or somesuch huge, human interaction by text dataset over a variety of topics. This would naturally need sub-AI's for mechanical things like grammar or parts of speech, and then possibly higher level meta-AI's for interpreting intent, orchestrating long form thought, or planning. In a way, its layers of AI based on level of thought abstraction. If it were a human, the high intensity portions of sub-AI would occupy space relative to intensity within reconfigurable co-processor zones (sight:visual cortex, sight:face recognition:occipital and temporal lobes, executive functions:frontal lobes, ect...)
No current AI implementation, to my knowledge, can understand such a sentence nearly as well as humans do. A competent chatbot judge could suggest a novel situation, say a broken-winged black Pegasus appeared in Jane's hometown when she was seven, and ask pointed questions to find out if the interlocutor is a human or a bot.
The issue with almost all current approaches to AI is that it is either purely symbolic or sub-symbolic. The current symbolic approaches cannot completely capture preconceptual experience human use to make sense of the world. When we hear "idyllic rural area", humans use our mental imagery and sensory experiences to help us understand the sentence much more deeply than the list of words suggests.
The subsymbolic approach could potentially solve this issue, but it raises the problem of integrating all those complex, interacting parts, vision, auditory, motor control, conceptual thoughts, etc. into a unifying whole. More importantly, would we be able to control and direct the beast sufficiently well once it becomes reality?
There is now some AGI (Artificial General Intelligence) research on integrating the two paradigms. If anyone is interested, a presentation is available here: http://ieet.org/index.php/IEET/more/goertzel20130531
As a postdoctoral candidate in biology, I can say that my approach to problem solving is exactly the opposite: My job is to infer as much as I can from the scant amount of data I can obtain. The goals outlined in this article are to collect as much data as you can, creating what is essentially a glorified lookup table of results. I must say the latter approach seems a hell of a lot easier.
[+] [-] striglia|11 years ago|reply
Separately, I do find it curious that his list of "pretty standard machine-learning methods" included Logistic Regression, K-means and....deep neural nets? Sure they're white hot in terms of popularity and the experts have done astounding things, but unless I've missed some major improvements in their off-the-shelf usability they strike me as out of place in this list.
[+] [-] fchollet|11 years ago|reply
They have also been commoditized by libraries such as Theano (Python) and Torch (Lua). Google and Facebook use their own tools based on Torch.
My own version of the shortlist would be: Logistic regression, KNN, random forests, SVM, and deep convnets.
[+] [-] dgreensp|11 years ago|reply
In addition, I don't know why anyone would think Google is going to make all the AI, just because today's most notable, state-of-the-art AI systems are made by a few big companies with the resources to fund large teams of experts for years. Fifty years ago this article could have been called "Untapped opportunities in software" (or operating systems) and talked about IBM -- is there software IBM can't or won't write for us?
[+] [-] craigching|11 years ago|reply
http://torch.ch/ (used widely at large companies) http://deeplearning4j.org/ (newer)
I have no idea if they are quality or open source, just posting the information.
[+] [-] beaucronin|11 years ago|reply
[+] [-] dkersten|11 years ago|reply
The next sentence is much more curious to me: "the point is that they’re widely available in high-quality open source packages" because I have yet to find a proper well documented well maintained non-toy open source deep neural network implementation.
[+] [-] hershel|11 years ago|reply
[+] [-] hyp0|11 years ago|reply
The alternative - that massively complex probabilistic models are the best theory of the data - is hopefully not true. Especially not of our minds. But it could be true, and if so, it would mean that our intelligence is irreducible, and we are forever beyond our own self-understanding (even in principle). Our history is full of inexplicable mysteries that were eventually understood. But not all: quantum randomness. I really hope intelligence is will be one of the former.
[+] [-] iandanforth|11 years ago|reply
By the time you're 30 you have been exposed to:
~1.4 petabytes of visual information ~1.8 terabytes of auditory information
Touch and proprioceptive bandwidth is harder to calculate but the ascending pathway through the spinal cord is about 10 million fibers, which is 10x the optic nerve (Or 5x the number of fibers from both eyes). So:
Between 1.4 and 14 petabytes of touch and proprioceptive information.
So we're a fairly large data problem on top of millions of years of evolution that have baked in some knowledge and abilities.
[+] [-] rwissmann|11 years ago|reply
[+] [-] hyperbovine|11 years ago|reply
[+] [-] araes|11 years ago|reply
"<AI> is the construction of weighted tables (choices, data, meta relations, whatever) from large sets of <prior data> by <method>"
This is kind of crazy, because I think it says you could make a Turing AI by using large datasets of prior life data for humans. In essence, "<my life> is the construction of weighted tables from large sets of <life experience> by <human learning>." For example, if you had an AI that could learn through text, you could have extensive transcribed conversation logs of people and then large time-activity logs to use as your inputs.
If it could learn through video (IE, it could view images, understand objects, object relations, events in time, and assign will to the person behind actions / events) then you could instead just feed it huge video logs of people's lives. If you wanted a copy of a person, you could feed it only a single individual, and if you wanted a more general AI, then you could feed it cross sections of the population.
In addition, there's a very cool meta aspect to the large dataset concept, in that it can be large datasets for when to use, or to feed data to, specialized sub-AI's. For example, you might have a math sub-AI that has been trained by feeding it massive sets of math problems (or perhaps it can learn math through the video life logs of a person?). If its then being used as a part of a larger piece, then you'd want to know when to use it to solve problems, or when to feed it experience inputs for further learning. In essence, its tables of categories for experience types, and then grown / paired sub-AI's for those types.
I would wager that it is possible, right now, to create a chatbot that can pass Turing using the above by feeding it the equivalent of mass IRC chat or somesuch huge, human interaction by text dataset over a variety of topics. This would naturally need sub-AI's for mechanical things like grammar or parts of speech, and then possibly higher level meta-AI's for interpreting intent, orchestrating long form thought, or planning. In a way, its layers of AI based on level of thought abstraction. If it were a human, the high intensity portions of sub-AI would occupy space relative to intensity within reconfigurable co-processor zones (sight:visual cortex, sight:face recognition:occipital and temporal lobes, executive functions:frontal lobes, ect...)
[+] [-] nopinsight|11 years ago|reply
"Jane grew up in an idyllic rural area."
No current AI implementation, to my knowledge, can understand such a sentence nearly as well as humans do. A competent chatbot judge could suggest a novel situation, say a broken-winged black Pegasus appeared in Jane's hometown when she was seven, and ask pointed questions to find out if the interlocutor is a human or a bot.
The issue with almost all current approaches to AI is that it is either purely symbolic or sub-symbolic. The current symbolic approaches cannot completely capture preconceptual experience human use to make sense of the world. When we hear "idyllic rural area", humans use our mental imagery and sensory experiences to help us understand the sentence much more deeply than the list of words suggests.
The subsymbolic approach could potentially solve this issue, but it raises the problem of integrating all those complex, interacting parts, vision, auditory, motor control, conceptual thoughts, etc. into a unifying whole. More importantly, would we be able to control and direct the beast sufficiently well once it becomes reality?
There is now some AGI (Artificial General Intelligence) research on integrating the two paradigms. If anyone is interested, a presentation is available here: http://ieet.org/index.php/IEET/more/goertzel20130531
[+] [-] maaku|11 years ago|reply
[+] [-] sp332|11 years ago|reply
[+] [-] jostmey|11 years ago|reply