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(Disclosure: Vonnik's cofounder creator of dl4j) That's more than likely the valley echo chamber. These kinds of startups are going to continue to appeal to pe

(Disclosure: Vonnik's cofounder creator of dl4j) That's more than likely the valley echo chamber. These kinds of startups are going to continue to appeal to people like themselves getting acquired by google eventually. The same scientists who create these services have zero clue about the sales process in enterprise and can't often figure out how to sell in to these corporations and actually create some kind of long term value. Our differentiation in this space is being able to do EXACTLY that while also adding features that are actually useful. By the time they have, they're usually bored and realize they just want to work on cool problems and therefore: sell. Most research out there generated quickly is actually useless for products. I'd rather implement every 5th iteration of a technique that has significantly better results than jump on the feature treadmill.

cs702 10y ago
As much as I want you guys to succeed, I must disagree with this comment, because there are an awful LOT of people in the valley who know how to build scalable enterprise-sales organizations, and "AI service" startups with traction will have little trouble finding and recruiting those people. I would not bet against the valley's "how to build a giant business" expertise.
- agibsonccc 10y ago
  We're far from that. Much of what we actually sell are solutions focused on anomaly detection. We actually do most of our business in asia where the on prem market is much larger and people move faster. What I'm trying to point out here is that the people who run these startups are typically Phds with little interest in building an actual business. Many of these deep learning startups are built to be quick flips while having fun for a few years building something cool. That in and of itself is fine. They build something differentiated and valuable, get paid a handsome chunk of money. No one really loses when that happens, it's just different when you want to go to market. That being said, you have every right to bet against us. That's how many large companies start is with detractors. 90% of startups also fail. We have a different opinion on how it's done. Only time will tell whether we succeed or not. That being said: Where's your deep learning startup? ;) At least we're trying something different.
  - cs702 10y ago
    There will be room for a good number of niche frameworks, some of which could be highly profitable for their creators/maintainers. I don't disagree with that :-) My point was -- and still is -- that the competition to become the dominant framework has already been won, by TensorFlow, because it has captured the hearts and minds of the most developers. Most new AI services are being built atop TensorFlow -- not DL4J, nor Torch, nor Theano, nor Caffe, nor CNTK, nor anything else. Let me finish by sharing a video of Steve Ballmer, former CEO of Microsoft, in which he emphasizes the importance of attracting developers to a stadium packed with Microsoft employees: https://www.youtube.com/watch?v=Vhh_GeBPOhs https://www.youtube.com/watch?v=Vhh_GeBPOhs
    - agibsonccc 10y ago
      There's 2 things I'm not so sure about: python as the dominant ecosystem long term and 2: most people doing machine learning yet. I'm still convinced most people just star tensorflow "cuz google". I'd like to see a statistical analysis of people actually using tensorflow for their job. I get the developers bit...but not much of it equates to revenue. Developers don't pay. Period. That devs bit might work for lock in to cloud services (google compute engine). That play doesn't work for on prem though. What's happening here is you're equating startups/cloud to "the whole world" while ignoring where most of the money is being made and how many people are actually using machine learning. I'd love to see this play out more before declaring a "winner". Still seems too early yet. Anyways - as I said we'll see who wins and who doesn't. I won't say whether my guess is absolutely right or wrong. I just don't believe in silicon valley's ability to build "hard stuff". I think we're still very much in the hype phase yet. It might return some day though.
      - cs702 10y ago
        On #1, although TensorFlow has well-supported APIs only for Python and C++ as of now (see https://www.tensorflow.org/versions/r0.9/api_docs/index.html https://www.tensorflow.org/versions/r0.9/api_docs/index.html ), support for other languages is on the way (search Google for specific examples); moreover, TensorFlow can be readily deployed as a service (see https://tensorflow.github.io/serving/ https://tensorflow.github.io/serving/ ) in mixed-language environments. On #2, I agree: most people will use higher-level AI services developed and provided by third parties. How about we revisit this in, say, three years, and see who turns out to be right?

cs702 10y ago

As much as I want you guys to succeed, I must disagree with this comment, because there are an awful LOT of people in the valley who know how to build scalable enterprise-sales organizations, and "AI service" startups with traction will have little trouble finding and recruiting those people. I would not bet against the valley's "how to build a giant business" expertise.

agibsonccc 10y ago
We're far from that. Much of what we actually sell are solutions focused on anomaly detection. We actually do most of our business in asia where the on prem market is much larger and people move faster. What I'm trying to point out here is that the people who run these startups are typically Phds with little interest in building an actual business. Many of these deep learning startups are built to be quick flips while having fun for a few years building something cool. That in and of itself is fine. They build something differentiated and valuable, get paid a handsome chunk of money. No one really loses when that happens, it's just different when you want to go to market. That being said, you have every right to bet against us. That's how many large companies start is with detractors. 90% of startups also fail. We have a different opinion on how it's done. Only time will tell whether we succeed or not. That being said: Where's your deep learning startup? ;) At least we're trying something different.
- cs702 10y ago
  There will be room for a good number of niche frameworks, some of which could be highly profitable for their creators/maintainers. I don't disagree with that :-) My point was -- and still is -- that the competition to become the dominant framework has already been won, by TensorFlow, because it has captured the hearts and minds of the most developers. Most new AI services are being built atop TensorFlow -- not DL4J, nor Torch, nor Theano, nor Caffe, nor CNTK, nor anything else. Let me finish by sharing a video of Steve Ballmer, former CEO of Microsoft, in which he emphasizes the importance of attracting developers to a stadium packed with Microsoft employees: https://www.youtube.com/watch?v=Vhh_GeBPOhs https://www.youtube.com/watch?v=Vhh_GeBPOhs
  - agibsonccc 10y ago
    There's 2 things I'm not so sure about: python as the dominant ecosystem long term and 2: most people doing machine learning yet. I'm still convinced most people just star tensorflow "cuz google". I'd like to see a statistical analysis of people actually using tensorflow for their job. I get the developers bit...but not much of it equates to revenue. Developers don't pay. Period. That devs bit might work for lock in to cloud services (google compute engine). That play doesn't work for on prem though. What's happening here is you're equating startups/cloud to "the whole world" while ignoring where most of the money is being made and how many people are actually using machine learning. I'd love to see this play out more before declaring a "winner". Still seems too early yet. Anyways - as I said we'll see who wins and who doesn't. I won't say whether my guess is absolutely right or wrong. I just don't believe in silicon valley's ability to build "hard stuff". I think we're still very much in the hype phase yet. It might return some day though.
    - cs702 10y ago
      On #1, although TensorFlow has well-supported APIs only for Python and C++ as of now (see https://www.tensorflow.org/versions/r0.9/api_docs/index.html https://www.tensorflow.org/versions/r0.9/api_docs/index.html ), support for other languages is on the way (search Google for specific examples); moreover, TensorFlow can be readily deployed as a service (see https://tensorflow.github.io/serving/ https://tensorflow.github.io/serving/ ) in mixed-language environments. On #2, I agree: most people will use higher-level AI services developed and provided by third parties. How about we revisit this in, say, three years, and see who turns out to be right?