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Recent advances in AI/ML (i.e. deep learning) have shown ground breaking progress in a number of fields especially in computer vision problems, natural language

Recent advances in AI/ML (i.e. deep learning) have shown ground breaking progress in a number of fields especially in computer vision problems, natural language processing, and audio processing. AI is a tool that we are now seeing solve many problems which computers couldn't even touch 10 years ago with better than human accuracy today. This field is very new and there is significant room for improvement and new approaches. IMO, a lot of the excitement comes from the really impressive progress in a short time and a very wide open field of problems still to be explored.

itcrowd 10y ago
Thank you for the serious reply. I would like to ask you and anyone reading this: what exactly gives AI/ML the "edge"? I mean, faster computers are a given these days compared to any other time in humanity. They are giving us simulations so accurate that people in the 80's would be extremely jealous. However, my question is why these new techniques are superior to the established techniques. Why would an AI solution be better? In what sense would it be different from any other method? Again, I am not anti-AI or anti-ML but I'm interested in hearing opinions. If you could give me concrete examples where AI or ML is better in a way that is different from "faster computing" I would be sort-of satisfied. For the ones pursuing the CTM line, forget that. I know it's BS and I hope nobody falls for that stuff. However, s/AI/TCM/g in the post would not make clear what the "edge" of AI is over conventional computers. Thanks for the civil discussion.
- chrisfosterelli 10y ago
  The imagenet competition is a great concrete example. This is a massive dataset of images and the competition is to identify correctly the object in the image (truck, person, etc). The results the past few years look something like this: https://lh4.googleusercontent.com/0FLG_HLy-dHk5kPFIocPMHn5jchJJa4Sb2kALdlb8Y7kSDmuw-XCVAubXnlM_cVO7G6urGVDdGuoA4ppbpaTYYJV7ndCwUXp9Zj2CY3pudtJaez_sLUWL1PZae6QqqPtYg https://lh4.googleusercontent.com/0FLG_HLy-dHk5kPFIocPMHn5jc... Prior to 2012, this was done using traditional computer vision algorithms with hand picked features. The accuracy was often around 30-40%, until the first deep learning model beat the 2nd place competitor by near half the error. Today, almost every single competitor is deep learning based and accuracy is equal to human classifiers. Since deep learning has so many applications, it's hard to generalize what the "edge" is against all other approaches. In my opinion, other approaches often require handpicking features (where a ANN learns them) and struggle to recognize patterns in many areas (where a ANN does).
  - itcrowd 10y ago
    Thanks! This is exactly the kind of comparison I'm looking for (other fields are still welcome). If I look at the link you provide, I see an improvement which is great. However, the improvement seems to be somewhat linear year-on-year. In the original post, the first sentence is: "Some think the excitement around Artificial Intelligence is overhyped. They might be right. But if they’re wrong, we’re on the precipice of something really big. We can’t afford to ignore what might be the biggest technological leap since the Internet." Am I right in assuming that there is the expectation of AI/ML to have "hockey stick" growth in the (not-so distant) future? If so, what kind of methods could possibly make this real? If anyone could give me some papers, an essay or some words to search for this would help me a lot. Essentially, I am intrigued why there is this expectation of hockey stick growth. If my premise is wrong, please enlighten me too.
    - woah 10y ago
      It's a family of new techniques that has had great success doing things that are usually very hard or tedious to do with computers. These techniques seem more similar to how a human would do things than most other ways of programming things. This is the reason for a lot of the interest and hype. Just one random example: http://lmb.informatik.uni-freiburg.de/Publications/2016/TDB16a/tdb16.pdf http://lmb.informatik.uni-freiburg.de/Publications/2016/TDB1...
- jaypaulynice 10y ago
  I worked at a startup where we used machine learning to review legal documents. When you have 10M+ documents, it takes a long time for a team of lawyers to review them. So a company with AI/ML is at a huge advantage in identifying the documents a lawyer needs to win a case. To get accurate results, you need a lot of data, but to crunch that data you need a lot of power, not just speed. A lot of the processing is done now with GPUs (thousands of cores)...traditional computers are CPU intensive and have limited number of cores. Think for example a multi-threaded application running on GPUs vs CPUs...the GPU can divide the problem in more tasks and the processing can be done more accurately not just faster. http://www.nvidia.com/object/what-is-gpu-computing.html http://www.nvidia.com/object/what-is-gpu-computing.html PS: I think people overuse "Artificial intelligence" we're no where close...but it excites people compared to "machine learning" or "deep learning" or "convolutional network" or just statistics...It's just trying to find an equation that fits the data like linear regression.
  - itcrowd 10y ago
    I agree with your sentiment: to analyze these amounts of documents you need computers. And I understand the importance of parallelism, GPU computing can be way faster than CPUs for certain tasks. In a sense, and correct me if I'm wrong, I get the feeling that the whole ML is just starting now because we have faster computers. To analyze such amounts of text and make some sense of it, you'd need a massive "database" or "fitted model" to decipher what the legalese means. Only recently has this been possible to create (thanks to Moore's law). It's not like there is a breakthrough in mathematics (statistics) right? The theory probably has been around for ages but is only now becoming practical.
    - dnautics 10y ago
      > It's not like there is a breakthrough in mathematics (statistics) right? The theory probably has been around for ages but is only now becoming practical. That's absolutely correct. But to pooh pooh it on those grounds is like pooh-poohing the fourier transform around the 70s (which theory had been around for 200 years) and only had then "become practical" with the advent of transistor-based computer. Now the FT and relatives are used in everything from audio processing and compression, image filters, statistical analysis, pretty much every form of scientific spectroscopy, MRIs, etc. If you had generally made investments in "FT-related technologies" you'd be doing well.
- carapace 10y ago
  > what exactly gives AI/ML the "edge"? Backpropagation. [1] Are you familiar with the old Polaroid instant film? [2] You took a picture and pulled it out of the camera and it developed over the next few minutes. The picture went from dark grey nothingness to the image you took, like "magic". Anyhow, so-called "neural" networks (they are no more neural than Kermit the frog is amphibious) are basically just giant polynomials with changeable coefficients. [3] The input variables are e.g. pixels from an image, and the polynomial is solved to generate a single output value indicating that it is e.g. a cat. (But before backprop this will be uncorrelated, random, useless.) Now this image-to-yes/no-cat function is very difficult for a human programmer to write out by hand. I mean it's so difficult that no one can do it very well at all. But using backprogagation, and a big pile of images (some of which are cats and some of which are not) and an index of which are cats for the computer to use (humans will have had to already sort and tag the images for this to work, TANSTAAFL), you can run a simple loop to develop a "function" (in the form of coefficients for the polynomial) that can "see" cats. The "training" phase adjusts the variable coefficients until the result correlates with the tagged "catness" of the images, and then you're done. After that, you can input new images to the function and it will be able to indicate whether they depict cats or not. The function forms out of the data+backprop like an image developing on the film, projected there by the data set. So yeah, this AI/ML stuff lets us generate useful functions that we have no other way of generating (literally no one knows how.) It's a big deal. [1] https://en.wikipedia.org/wiki/Backpropagation https://en.wikipedia.org/wiki/Backpropagation [2] https://en.wikipedia.org/wiki/Instant_film https://en.wikipedia.org/wiki/Instant_film [3] https://en.wikipedia.org/wiki/Polynomial https://en.wikipedia.org/wiki/Polynomial
codingdave 10y ago
It isn't just the advances in the field... it is the accessibility of it all. The resources to run it are becoming more available, as GPU-focused cloud instances are now generally available, as well as just buying your own GPUs. Education is available to coders on how to get up to speed on this field, so it isn't just those with a academic background in it who are able to contribute to projects anymore. I'm not saying that us weekend dabblers in this field will surpass those who have dedicated years of study to it, but we are gaining enough understanding to come up with innovative ways to use it, and to contribute to projects. Even if we are not doing the ground breaking work, more people working on more projects will bring more innovation.

itcrowd 10y ago

Thank you for the serious reply. I would like to ask you and anyone reading this: what exactly gives AI/ML the "edge"? I mean, faster computers are a given these days compared to any other time in humanity. They are giving us simulations so accurate that people in the 80's would be extremely jealous. However, my question is why these new techniques are superior to the established techniques. Why would an AI solution be better? In what sense would it be different from any other method? Again, I am not anti-AI or anti-ML but I'm interested in hearing opinions. If you could give me concrete examples where AI or ML is better in a way that is different from "faster computing" I would be sort-of satisfied. For the ones pursuing the CTM line, forget that. I know it's BS and I hope nobody falls for that stuff. However, s/AI/TCM/g in the post would not make clear what the "edge" of AI is over conventional computers. Thanks for the civil discussion.

chrisfosterelli 10y ago
The imagenet competition is a great concrete example. This is a massive dataset of images and the competition is to identify correctly the object in the image (truck, person, etc). The results the past few years look something like this: https://lh4.googleusercontent.com/0FLG_HLy-dHk5kPFIocPMHn5jchJJa4Sb2kALdlb8Y7kSDmuw-XCVAubXnlM_cVO7G6urGVDdGuoA4ppbpaTYYJV7ndCwUXp9Zj2CY3pudtJaez_sLUWL1PZae6QqqPtYg https://lh4.googleusercontent.com/0FLG_HLy-dHk5kPFIocPMHn5jc... Prior to 2012, this was done using traditional computer vision algorithms with hand picked features. The accuracy was often around 30-40%, until the first deep learning model beat the 2nd place competitor by near half the error. Today, almost every single competitor is deep learning based and accuracy is equal to human classifiers. Since deep learning has so many applications, it's hard to generalize what the "edge" is against all other approaches. In my opinion, other approaches often require handpicking features (where a ANN learns them) and struggle to recognize patterns in many areas (where a ANN does).
- itcrowd 10y ago
  Thanks! This is exactly the kind of comparison I'm looking for (other fields are still welcome). If I look at the link you provide, I see an improvement which is great. However, the improvement seems to be somewhat linear year-on-year. In the original post, the first sentence is: "Some think the excitement around Artificial Intelligence is overhyped. They might be right. But if they’re wrong, we’re on the precipice of something really big. We can’t afford to ignore what might be the biggest technological leap since the Internet." Am I right in assuming that there is the expectation of AI/ML to have "hockey stick" growth in the (not-so distant) future? If so, what kind of methods could possibly make this real? If anyone could give me some papers, an essay or some words to search for this would help me a lot. Essentially, I am intrigued why there is this expectation of hockey stick growth. If my premise is wrong, please enlighten me too.
  - woah 10y ago
    It's a family of new techniques that has had great success doing things that are usually very hard or tedious to do with computers. These techniques seem more similar to how a human would do things than most other ways of programming things. This is the reason for a lot of the interest and hype. Just one random example: http://lmb.informatik.uni-freiburg.de/Publications/2016/TDB16a/tdb16.pdf http://lmb.informatik.uni-freiburg.de/Publications/2016/TDB1...
jaypaulynice 10y ago
I worked at a startup where we used machine learning to review legal documents. When you have 10M+ documents, it takes a long time for a team of lawyers to review them. So a company with AI/ML is at a huge advantage in identifying the documents a lawyer needs to win a case. To get accurate results, you need a lot of data, but to crunch that data you need a lot of power, not just speed. A lot of the processing is done now with GPUs (thousands of cores)...traditional computers are CPU intensive and have limited number of cores. Think for example a multi-threaded application running on GPUs vs CPUs...the GPU can divide the problem in more tasks and the processing can be done more accurately not just faster. http://www.nvidia.com/object/what-is-gpu-computing.html http://www.nvidia.com/object/what-is-gpu-computing.html PS: I think people overuse "Artificial intelligence" we're no where close...but it excites people compared to "machine learning" or "deep learning" or "convolutional network" or just statistics...It's just trying to find an equation that fits the data like linear regression.
- itcrowd 10y ago
  I agree with your sentiment: to analyze these amounts of documents you need computers. And I understand the importance of parallelism, GPU computing can be way faster than CPUs for certain tasks. In a sense, and correct me if I'm wrong, I get the feeling that the whole ML is just starting now because we have faster computers. To analyze such amounts of text and make some sense of it, you'd need a massive "database" or "fitted model" to decipher what the legalese means. Only recently has this been possible to create (thanks to Moore's law). It's not like there is a breakthrough in mathematics (statistics) right? The theory probably has been around for ages but is only now becoming practical.
  - dnautics 10y ago
    > It's not like there is a breakthrough in mathematics (statistics) right? The theory probably has been around for ages but is only now becoming practical. That's absolutely correct. But to pooh pooh it on those grounds is like pooh-poohing the fourier transform around the 70s (which theory had been around for 200 years) and only had then "become practical" with the advent of transistor-based computer. Now the FT and relatives are used in everything from audio processing and compression, image filters, statistical analysis, pretty much every form of scientific spectroscopy, MRIs, etc. If you had generally made investments in "FT-related technologies" you'd be doing well.
carapace 10y ago
> what exactly gives AI/ML the "edge"? Backpropagation. [1] Are you familiar with the old Polaroid instant film? [2] You took a picture and pulled it out of the camera and it developed over the next few minutes. The picture went from dark grey nothingness to the image you took, like "magic". Anyhow, so-called "neural" networks (they are no more neural than Kermit the frog is amphibious) are basically just giant polynomials with changeable coefficients. [3] The input variables are e.g. pixels from an image, and the polynomial is solved to generate a single output value indicating that it is e.g. a cat. (But before backprop this will be uncorrelated, random, useless.) Now this image-to-yes/no-cat function is very difficult for a human programmer to write out by hand. I mean it's so difficult that no one can do it very well at all. But using backprogagation, and a big pile of images (some of which are cats and some of which are not) and an index of which are cats for the computer to use (humans will have had to already sort and tag the images for this to work, TANSTAAFL), you can run a simple loop to develop a "function" (in the form of coefficients for the polynomial) that can "see" cats. The "training" phase adjusts the variable coefficients until the result correlates with the tagged "catness" of the images, and then you're done. After that, you can input new images to the function and it will be able to indicate whether they depict cats or not. The function forms out of the data+backprop like an image developing on the film, projected there by the data set. So yeah, this AI/ML stuff lets us generate useful functions that we have no other way of generating (literally no one knows how.) It's a big deal. [1] https://en.wikipedia.org/wiki/Backpropagation https://en.wikipedia.org/wiki/Backpropagation [2] https://en.wikipedia.org/wiki/Instant_film https://en.wikipedia.org/wiki/Instant_film [3] https://en.wikipedia.org/wiki/Polynomial https://en.wikipedia.org/wiki/Polynomial

codingdave 10y ago

It isn't just the advances in the field... it is the accessibility of it all. The resources to run it are becoming more available, as GPU-focused cloud instances are now generally available, as well as just buying your own GPUs. Education is available to coders on how to get up to speed on this field, so it isn't just those with a academic background in it who are able to contribute to projects anymore. I'm not saying that us weekend dabblers in this field will surpass those who have dedicated years of study to it, but we are gaining enough understanding to come up with innovative ways to use it, and to contribute to projects. Even if we are not doing the ground breaking work, more people working on more projects will bring more innovation.