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Let me get this straight: H2O's contribution to this project is a bunch of deep-learning libraries that they didn't create? How will they maintain and extend th

Let me get this straight: H2O's contribution to this project is a bunch of deep-learning libraries that they didn't create? How will they maintain and extend that? Do they know how credibility in open-source works? H2O wrote its random forests and GBMs, neither of which show large performance gains on GPUs compared to neural nets. And the neural nets in this initiative were created by Google, Berkeley, Microsoft... This looks like the OpenStack of machine learning, an unmaintainable project that will crumble under its own complexity.

aub3bhat 9y ago
I really don't understand your arguments, let alone the overt negative tone here? >> 1. How will they maintain and extend that? Umm just like how everyone else does it. >> 2. H2O wrote its random forests and GBMs, neither of which show large performance gains on GPUs compared to neural nets. What does your statement even means? [1] >> And the neural nets in this initiative were created by Google, Berkeley, Microsoft... You mean libraries? Note: NOT an employee of H2O or anyway affiliated. [1] https://devblogs.nvidia.com/parallelforall/bidmach-machine-learning-limit-gpus/ https://devblogs.nvidia.com/parallelforall/bidmach-machine-l...
- blueyes 9y ago
  I'm negative on marketing fluff. You're free to enjoy it of course. >> Umm just like how everyone else does it. Wrong. Their ability to maintain and extend any of those libraries is limited. TensorFlow is hundreds of thousands of lines of code. Google can maintain and extend just fine. I doubt that H2O has the chops. That matters to their clients, because TensorFlow breaks things with new releases, and those clients will want someone to fix it. Do you call H2O? Do they patch Google into the conference line? I'm saying they're not in a position to offer commercial support. Anyone who has worked in enterprise will see the risks here. >> What does your statement even means? The algorithms that H2O specializes in do not benefit that much from the massively parallel computation that GPUs offer. If you don't understand the differences in the computation required by neural nets as opposed to random forests, you will not understand my critique. Neural nets offer superior accuracy on many problem sets and often need GPUs. Random forests do neither of those things. H2O, for most of its long existence, has operated purely on CPUs just fine. But GPUs and deep learning are hot, so they are associating themselves with the buzzwords, even though they are not really in a position to a) build deep learning solutions or b) make their real product perform well on GPUs. Marketing fluff. >> You mean libraries? Why yes, I mean libraries. Thanks for clearing that up. There's a lot of noise in the deep learning and AI space. Watson is one egregious example. H2O is another. While the technology itself is promising, some of the vendors are dubious.
  - arnon 9y ago
    In the end, most AI/DL implementations in products are fluff. It feels like a lot of products are about "HEY LOOK AT ME, I CAN DO IT TOO!", which screams immaturity of the concepts. From personal experience, many customers ask about our capabilities - but when it actually comes to using any of them there are only a handful of actually relevant ML algorithms that are useful, and those used to be called 'statistics' up until fairly recently.
deleted 9y ago
[deleted]
vindows 9y ago
I wish you had actually looked through the website and the associated Github repo. H2O has actually ported their machine learning algorithms starting with GLM & GBM to run on GPUs and achieved some pretty significant performance gains by doing so. https://github.com/h2oai/perf https://github.com/h2oai/perf
- blueyes 9y ago
  I wish you knew the history of H2O's attempts to make a decent product. H2O, the open-source project, was largely created by H2O the company's technical co-founder, Cliff Click, who was forced out of the company more than a year ago for internal political reasons. With him, H2O lost much of its ability to develop and extend its code base, particularly since Cliff built many things in house that might have better been dependencies. That's why Steam never happened. Now H2O is mostly marketing, an overfunded startup in slow decline that was forced to fire its sales team last year because, well, nobody wants their stuff. https://venturebeat.com/2016/09/24/machine-learning-startup-h2o-lays-off-10-of-employees/ https://venturebeat.com/2016/09/24/machine-learning-startup-... H2O is not nearly as fast as SMILE by most metrics: http://haifengl.github.io/smile/ http://haifengl.github.io/smile/ https://github.com/haifengl/smile https://github.com/haifengl/smile But what I'm saying is that true gains are seen for algorithms that require more parallelization than H2O's specialties. It's a critique of the relative improvements.

aub3bhat 9y ago

I really don't understand your arguments, let alone the overt negative tone here? >> 1. How will they maintain and extend that? Umm just like how everyone else does it. >> 2. H2O wrote its random forests and GBMs, neither of which show large performance gains on GPUs compared to neural nets. What does your statement even means? [1] >> And the neural nets in this initiative were created by Google, Berkeley, Microsoft... You mean libraries? Note: NOT an employee of H2O or anyway affiliated. [1] https://devblogs.nvidia.com/parallelforall/bidmach-machine-learning-limit-gpus/ https://devblogs.nvidia.com/parallelforall/bidmach-machine-l...

blueyes 9y ago
I'm negative on marketing fluff. You're free to enjoy it of course. >> Umm just like how everyone else does it. Wrong. Their ability to maintain and extend any of those libraries is limited. TensorFlow is hundreds of thousands of lines of code. Google can maintain and extend just fine. I doubt that H2O has the chops. That matters to their clients, because TensorFlow breaks things with new releases, and those clients will want someone to fix it. Do you call H2O? Do they patch Google into the conference line? I'm saying they're not in a position to offer commercial support. Anyone who has worked in enterprise will see the risks here. >> What does your statement even means? The algorithms that H2O specializes in do not benefit that much from the massively parallel computation that GPUs offer. If you don't understand the differences in the computation required by neural nets as opposed to random forests, you will not understand my critique. Neural nets offer superior accuracy on many problem sets and often need GPUs. Random forests do neither of those things. H2O, for most of its long existence, has operated purely on CPUs just fine. But GPUs and deep learning are hot, so they are associating themselves with the buzzwords, even though they are not really in a position to a) build deep learning solutions or b) make their real product perform well on GPUs. Marketing fluff. >> You mean libraries? Why yes, I mean libraries. Thanks for clearing that up. There's a lot of noise in the deep learning and AI space. Watson is one egregious example. H2O is another. While the technology itself is promising, some of the vendors are dubious.
- arnon 9y ago
  In the end, most AI/DL implementations in products are fluff. It feels like a lot of products are about "HEY LOOK AT ME, I CAN DO IT TOO!", which screams immaturity of the concepts. From personal experience, many customers ask about our capabilities - but when it actually comes to using any of them there are only a handful of actually relevant ML algorithms that are useful, and those used to be called 'statistics' up until fairly recently.

deleted 9y ago

[deleted]

vindows 9y ago

I wish you had actually looked through the website and the associated Github repo. H2O has actually ported their machine learning algorithms starting with GLM & GBM to run on GPUs and achieved some pretty significant performance gains by doing so. https://github.com/h2oai/perf https://github.com/h2oai/perf

blueyes 9y ago
I wish you knew the history of H2O's attempts to make a decent product. H2O, the open-source project, was largely created by H2O the company's technical co-founder, Cliff Click, who was forced out of the company more than a year ago for internal political reasons. With him, H2O lost much of its ability to develop and extend its code base, particularly since Cliff built many things in house that might have better been dependencies. That's why Steam never happened. Now H2O is mostly marketing, an overfunded startup in slow decline that was forced to fire its sales team last year because, well, nobody wants their stuff. https://venturebeat.com/2016/09/24/machine-learning-startup-h2o-lays-off-10-of-employees/ https://venturebeat.com/2016/09/24/machine-learning-startup-... H2O is not nearly as fast as SMILE by most metrics: http://haifengl.github.io/smile/ http://haifengl.github.io/smile/ https://github.com/haifengl/smile https://github.com/haifengl/smile But what I'm saying is that true gains are seen for algorithms that require more parallelization than H2O's specialties. It's a critique of the relative improvements.