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Data quantity and quality are key. Both, though. This is why it's foolish to go up against an ML product from Google, Facebook, or (maybe) Microsoft. You just

Data quantity and quality are key. Both, though. This is why it's foolish to go up against an ML product from Google, Facebook, or (maybe) Microsoft. You just can't compete with the volume and quality of data they can access. In sectors like automotive, where every brand is competing to try and build the best predictors using only their own data, there is a huge opportunity available for the first two companies to share data with each other. Doubling your data quantity brings a significant improvement to any model, and would put them ahead of any competition. That advantage only grows the more players you add to the sharing pool. I believe that if humanity is really going to harness machine learning, the concept of a bulk data commons is an inevitable requirement. This makes decentralized personal data control, homomorphic encryption, and similar technologies incredibly important.

nightcracker 8y ago
> This makes decentralized personal data control, homomorphic encryption, and similar technologies incredibly important. Call me a cynic, but we can't even get companies to do the bare minimum to protect data that is centralized, and you expect them to do all that? I'm not kidding when I say that your last sentence is a bigger, harder and more important task than the machine learning it is supposed to facilitate.
- ngcc_hk 8y ago
  Yes. Found that sentence very odd if one accept the idea of the more data needed meant sacrify all those values ... otherwise just ask around ... hence may be china and its firms will win as they do not need to.
jonathankoren 8y ago
They way you compete with Google et al, is to find a niche they think they’re serving, but in reality completely suck at.
- hayksaakian 8y ago
  If someone would like an example here, check out this animated video filters (see snapchat, tiktok etc). Just because Google et al have the underlying technology, doesn't mean they're the best at creating a product that uses that tech. Certainly gives them a leg-up to get started, but it's not the end-all be-all.
  - deleted 8y ago
    [deleted]
- chongli 8y ago
  But to do this successfully you have to hit the bullseye, grow extremely quickly, and have a strong moat. Otherwise, one of the big companies will either clone you (or buy out a startup who has cloned you) and leverage their dominating market position into serving your niche somewhat better than completely sucking at it, as they had before.
  - jonathankoren 8y ago
    Being bought out is an exit. Hardly a failure.
    - dredmorbius 8y ago
      That really depends. If you're being bought out at X to prevent you from reaching 10X or 1,000X, it's a loss. Is it an offer you can or cannot refuse?
      - jonathankoren 8y ago
        Future value isn’t set in stone, and the marginal value of a dollar decreases.
visarga 8y ago
> This is why it's foolish to go up against an ML product from Google, Facebook, or (maybe) Microsoft. I was sure someone will say this. I think it only applies for advertising, commerce and insurance. When it comes to learning ML models on images, text and games, big corporations don't have a unique trove of data. They only have the data advantage when it comes to personal data. The more important advantage big corporations have is hiring the best and hiring more ML scientists and engineers. The job demand far outstrips the offer.
- bobthepanda 8y ago
  Eh, the big companies have more resources to classify larger training data sets: e.g. Google reCAPTCHA
- throwawaymath 8y ago
  Large tech firms absolutely have access to unqiue image, text and game(play) data. I'm surprised you think they don't. How many tech startups do you suppose have access to every single multiplayer game of Destiny (Microsoft), every user's profile photo and interest metadata (Facebook), or every user's search history and email (Google)? But wait, there's more! How many startups have ready access to usage statistics for every mobile app (Google, Apple)? How many startups have virtually the entire corpus of available ebooks on hand for mining (Google, Amazon)? What about traffic patterns (Google), or virtual reality game(play) data (Facebook)? Telemetry data from the most widely used operating systems and web browsers (Google, Apple, Microsoft)? Video content with official subtitles (Netflix, Amazon, Google, Apple, ...). And don't forget login and activity data through social login (Facebook, Google) and image training via reCAPTCHA (Google). I can go on and on here. You should reconsider how large the set of training material is that be acquired by mining massive amounts of "personal" data. Of course, this is completely orthogonal to the other major advantage large companies have; namely, they have vast sums of money to throw at compute and storage resources.
- nostrademons 8y ago
  ImageNet has 14 million images. How many images does Google have available to them? They've got about 1000+ of my personal photos backed up on Google Photos, so if each of their 200M monthly active users is like me, that's 200 billion images from Google Photos alone. Then add every image crawled from the WWW for image search (that was several tens of billion when I was there, each tagged with text & structured data from its page of origin), every Street View photo (that was thousands of hard drives worth, measuring in the petabytes total), and high-res satellite images of the entire earth through their SkyBox/TerraBella acquisition. The Big Tech companies like to understate the size of their data advantage because it tempts competitors into doing stupid things that won't work in the market. Don't be fooled though - the majority of useful data is locked up inside proprietary silos.

nightcracker 8y ago

> This makes decentralized personal data control, homomorphic encryption, and similar technologies incredibly important. Call me a cynic, but we can't even get companies to do the bare minimum to protect data that is centralized, and you expect them to do all that? I'm not kidding when I say that your last sentence is a bigger, harder and more important task than the machine learning it is supposed to facilitate.

ngcc_hk 8y ago
Yes. Found that sentence very odd if one accept the idea of the more data needed meant sacrify all those values ... otherwise just ask around ... hence may be china and its firms will win as they do not need to.

jonathankoren 8y ago

They way you compete with Google et al, is to find a niche they think they’re serving, but in reality completely suck at.

hayksaakian 8y ago
If someone would like an example here, check out this animated video filters (see snapchat, tiktok etc). Just because Google et al have the underlying technology, doesn't mean they're the best at creating a product that uses that tech. Certainly gives them a leg-up to get started, but it's not the end-all be-all.
- deleted 8y ago
  [deleted]
chongli 8y ago
But to do this successfully you have to hit the bullseye, grow extremely quickly, and have a strong moat. Otherwise, one of the big companies will either clone you (or buy out a startup who has cloned you) and leverage their dominating market position into serving your niche somewhat better than completely sucking at it, as they had before.
- jonathankoren 8y ago
  Being bought out is an exit. Hardly a failure.
  - dredmorbius 8y ago
    That really depends. If you're being bought out at X to prevent you from reaching 10X or 1,000X, it's a loss. Is it an offer you can or cannot refuse?
    - jonathankoren 8y ago
      Future value isn’t set in stone, and the marginal value of a dollar decreases.

visarga 8y ago

> This is why it's foolish to go up against an ML product from Google, Facebook, or (maybe) Microsoft. I was sure someone will say this. I think it only applies for advertising, commerce and insurance. When it comes to learning ML models on images, text and games, big corporations don't have a unique trove of data. They only have the data advantage when it comes to personal data. The more important advantage big corporations have is hiring the best and hiring more ML scientists and engineers. The job demand far outstrips the offer.

bobthepanda 8y ago
Eh, the big companies have more resources to classify larger training data sets: e.g. Google reCAPTCHA
throwawaymath 8y ago
Large tech firms absolutely have access to unqiue image, text and game(play) data. I'm surprised you think they don't. How many tech startups do you suppose have access to every single multiplayer game of Destiny (Microsoft), every user's profile photo and interest metadata (Facebook), or every user's search history and email (Google)? But wait, there's more! How many startups have ready access to usage statistics for every mobile app (Google, Apple)? How many startups have virtually the entire corpus of available ebooks on hand for mining (Google, Amazon)? What about traffic patterns (Google), or virtual reality game(play) data (Facebook)? Telemetry data from the most widely used operating systems and web browsers (Google, Apple, Microsoft)? Video content with official subtitles (Netflix, Amazon, Google, Apple, ...). And don't forget login and activity data through social login (Facebook, Google) and image training via reCAPTCHA (Google). I can go on and on here. You should reconsider how large the set of training material is that be acquired by mining massive amounts of "personal" data. Of course, this is completely orthogonal to the other major advantage large companies have; namely, they have vast sums of money to throw at compute and storage resources.
nostrademons 8y ago
ImageNet has 14 million images. How many images does Google have available to them? They've got about 1000+ of my personal photos backed up on Google Photos, so if each of their 200M monthly active users is like me, that's 200 billion images from Google Photos alone. Then add every image crawled from the WWW for image search (that was several tens of billion when I was there, each tagged with text & structured data from its page of origin), every Street View photo (that was thousands of hard drives worth, measuring in the petabytes total), and high-res satellite images of the entire earth through their SkyBox/TerraBella acquisition. The Big Tech companies like to understate the size of their data advantage because it tempts competitors into doing stupid things that won't work in the market. Don't be fooled though - the majority of useful data is locked up inside proprietary silos.