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I feel like, as a solo app developer, I'm being left out of this 'revolution'. It seems like deep learning only makes sense if you have enough data to feed the

I feel like, as a solo app developer, I'm being left out of this 'revolution'. It seems like deep learning only makes sense if you have enough data to feed the algorithm with. The kind of data that only big companies can produce or harvest. Sure you can produce some video and audio data and you can spider the web a little bit, but that doesn't even come close to the resources that these big corporations have and the 'depth' of learning that they can achieve. So I'm not even trying. Or should I ? Is there any place for solo/indy developers in this field ?

amenghra 10y ago
I have seen small companies build better product targeting than Fb/Google/etc do with their ads. Yes you have less data, but your users are probably more similar to each other and less scattered all over the place. While I feel this is true for recommendation engines, it might not be true for other AI applications.
- amenghra 10y ago
  Prisma is an example of small team doing something novel with AI. Over time they are going to have a huge amount of data to further improve their product.
skybrian 10y ago
I don't know much about it, but there are public datasets you can train on and contests you can enter. But this seems more like doing research in hopes of coming up with something new that big companies will be interested in. Or, if that doesn't happen, learning enough so that they can hire you as a researcher or consultant.
web007 10y ago
Depending on what you're trying to do you have some options, but you should probably outsource. Option 1: If you're working on image recognition or anything similar, it's easy to get a ton of data. There are many corpora of image data available, probably on the order of hundreds of terabytes, with tags or at least some structured data. If you're going to "spider the web" you should use Common Crawl instead, and can access all of Blekko's data for the cost of data transfer via AWS. Same for text data, use the Google N-gram corpus. Knowing that all of that exists, I would still recommend Option 2: outsourcing your AI needs unless you're a researcher or have a decent budget for AI development. Go search "ai api" and pick one that matches what you want to do. Match your skill and risk tolerance; nobody got fired for using IBM, but you may get a better experience from a startup with the possibility of flaming out in a year. You'll get the leverage of whatever company is spending those dollars on your behalf, and you'll be able to concentrate on the user experience instead of the science-ey part. Option 3 is "if you can't beat 'em, join 'em". Go work for MS / GOOG / FB / IBM building something, and get access to those resources for yourself. Then at some point in the future you'll know their API interface, and you can go back to option #2 with much better data.
garysieling 10y ago
You can do some cool stuff with the APIs and datasets the big companies provide. Most are typically priced per API call with generous free tiers. I expect these will get cheaper. I'm working on a search engine for lectures (https://www.findlectures.com https://www.findlectures.com) - for ~30k API requests I've been able to do everything free. A lot of interesting large data sets are hosted in the "cloud" too for you to use for research, so you can get at them that way.
- Plough_Jogger 10y ago
  Results for 'deep learning' were completely irrelevant.
  - garysieling 10y ago
    Thanks for the report - that hasn't been an area I've focused one, but I'll work on that one.
acgourley 10y ago
There is a lot of design and UX and market exploration to do around learning systems. The way that a user will consume the outputs of a learning system are far from well defined right now. Larger companies have an advantage in many ways (like Google Now can exploit google's reach into android home screens) but consider a tool that helps you garden, find a restaurant or dress yourself based on your preferences - what would that UI look like? Google doesn't have an answer to that, yet.
brianobush 10y ago
The obvious focus is deep learning, but those models need data, trained and deployed. There is a lot of work on those edges that require some AI knowledge. Also don't forget there are lots of AI techniques that are just as important now. So focus on an area that interests you, learn some of those AI methods and go! Every developer in the future will have to understand some of these methods at a basic level.
- draker 10y ago
  What would be considered "basic" AI techniques? Any resources you would recommend for a good introduction to AI/deep learning?
SwellJoe 10y ago
I've just started tinkering with deep learning for a solo project; I recognized pretty quickly that there are many areas where I can't "play with the big boys", but I also realized there's a lot of low-hanging fruit that is accessible to me exactly because the big guys are open sourcing so much. So, I can build products that aren't big enough to interest Google, but include a bunch of tech developed by Google (and others) and leverages their APIs to provide a unique service that is feasible for me to build, and will be useful to a wide variety of people. I can offer it for very little money (one person's side project), and hopefully have some fun learning about deep learning. I'm so new to it that I'm not even thinking about advancing the state of the art or doing novel work. But, in a couple of years, who knows. Just tinkering with things in a new industry often provides pathways to cool stuff because so many doors are opening all the time. This is like being involved in the Internet in the early-to-mid 90s. You probably won't become the next Google, but the odds of finding a highly profitable smaller niche seems pretty high. Also, there's going to be a ton of acquisitions in the AI/deep learning space over the next decade. Every company that even does a little tech will "need" an AI story to keep their investors happy. Your tiny thing could be one of those acqui-hires, or maybe not. Then again, if you have an interest in other stuff, and really don't feel excited about it...probably not worth forcing yourself to get into it. Life is short, you should do stuff that's fun, even if you have to ring the cash register now and then.
- liamzebedee 10y ago
  Could you provide an example of a project you'd develop? :-)
  - SwellJoe 10y ago
    I'll be posting a Show HN post in a few weeks with one of those ideas. But, there's a bunch of ideas I've brainstormed around using things like sentiment analysis and other kinds of very simple-to-use AI concepts for automating tedious stuff. Things like automatically triaging support requests based on how angry the customer sounds, or based on keywords and an analysis of earlier requests; off-the-shelf NLP algorithms can do this today (and Google uses it that way for their own support tools, but doesn't make it widely available in that form, though Inbox has some of that kind of tech working in it). All you need is training data and some familiarity with Python. My brainstorming exercise goes something like this: Append "with spooky powers" to a bunch of common things until one of them seems cool and useful to me. So, "forum notifications bot with spooky powers", "IRC bot with spooky powers", "twitter bot with spooky powers", "customer relationship management with spooky powers", "analytics with spooky powers", "server monitoring with spooky powers", "log analysis with spooky powers", etc. And I try to think of what I would use such a thing for, if it existed. Then, I sit down and see if I can make it real. The Yahoo NSFW image detection announcement reminded me of ideas I had and tinkered with a decade ago when I worked on a content filtering system for schools...the difference is that now we have the horsepower, the data sets, and the algorithms to actually make it work (but, I haven't worked in that field in a decade and never really liked being a purveyor of censorship tools, even if only for children, anyway). Anyway, the possibilities are kinda huge and wide open. Many of these ideas will fizzle out, even the ones that look promising, but as with the Internet a lot of millionaires are going to be made by people saying, "It's like X, but with AI." just as people used to say, "It's like X, but on the Internet."
ww520 10y ago
It's hard to just have a product centered around AI, but you can add AI to one of the components in your product. E.g. drawing a box around the face in a photo using face recognition technique is fairly approachable. It just means you need to call a library. A lots of the AI are well understood and encapsulated.
hinkley 10y ago
If someone proves that deep learning is applicable to most software categories (and I honestly don't think it is, at least not for a very long time), then someone will sell a solution you can plug into your app. We already have a disturbing quantity and variety of user metric apps for the web. Realtime feedback just requires someone to bring engineering discipline to bear on the space and produce a functional and efficient version. Instead of a bunch of code monkey asshats making my beautiful, fast web app soul crushingly slow because my bosses said yes to one more tracking addon.

amenghra 10y ago

I have seen small companies build better product targeting than Fb/Google/etc do with their ads. Yes you have less data, but your users are probably more similar to each other and less scattered all over the place. While I feel this is true for recommendation engines, it might not be true for other AI applications.

amenghra 10y ago
Prisma is an example of small team doing something novel with AI. Over time they are going to have a huge amount of data to further improve their product.

skybrian 10y ago

I don't know much about it, but there are public datasets you can train on and contests you can enter. But this seems more like doing research in hopes of coming up with something new that big companies will be interested in. Or, if that doesn't happen, learning enough so that they can hire you as a researcher or consultant.

web007 10y ago

Depending on what you're trying to do you have some options, but you should probably outsource. Option 1: If you're working on image recognition or anything similar, it's easy to get a ton of data. There are many corpora of image data available, probably on the order of hundreds of terabytes, with tags or at least some structured data. If you're going to "spider the web" you should use Common Crawl instead, and can access all of Blekko's data for the cost of data transfer via AWS. Same for text data, use the Google N-gram corpus. Knowing that all of that exists, I would still recommend Option 2: outsourcing your AI needs unless you're a researcher or have a decent budget for AI development. Go search "ai api" and pick one that matches what you want to do. Match your skill and risk tolerance; nobody got fired for using IBM, but you may get a better experience from a startup with the possibility of flaming out in a year. You'll get the leverage of whatever company is spending those dollars on your behalf, and you'll be able to concentrate on the user experience instead of the science-ey part. Option 3 is "if you can't beat 'em, join 'em". Go work for MS / GOOG / FB / IBM building something, and get access to those resources for yourself. Then at some point in the future you'll know their API interface, and you can go back to option #2 with much better data.

garysieling 10y ago

You can do some cool stuff with the APIs and datasets the big companies provide. Most are typically priced per API call with generous free tiers. I expect these will get cheaper. I'm working on a search engine for lectures (https://www.findlectures.com https://www.findlectures.com) - for ~30k API requests I've been able to do everything free. A lot of interesting large data sets are hosted in the "cloud" too for you to use for research, so you can get at them that way.

Plough_Jogger 10y ago
Results for 'deep learning' were completely irrelevant.
- garysieling 10y ago
  Thanks for the report - that hasn't been an area I've focused one, but I'll work on that one.

acgourley 10y ago

There is a lot of design and UX and market exploration to do around learning systems. The way that a user will consume the outputs of a learning system are far from well defined right now. Larger companies have an advantage in many ways (like Google Now can exploit google's reach into android home screens) but consider a tool that helps you garden, find a restaurant or dress yourself based on your preferences - what would that UI look like? Google doesn't have an answer to that, yet.

brianobush 10y ago

The obvious focus is deep learning, but those models need data, trained and deployed. There is a lot of work on those edges that require some AI knowledge. Also don't forget there are lots of AI techniques that are just as important now. So focus on an area that interests you, learn some of those AI methods and go! Every developer in the future will have to understand some of these methods at a basic level.

draker 10y ago
What would be considered "basic" AI techniques? Any resources you would recommend for a good introduction to AI/deep learning?

SwellJoe 10y ago

I've just started tinkering with deep learning for a solo project; I recognized pretty quickly that there are many areas where I can't "play with the big boys", but I also realized there's a lot of low-hanging fruit that is accessible to me exactly because the big guys are open sourcing so much. So, I can build products that aren't big enough to interest Google, but include a bunch of tech developed by Google (and others) and leverages their APIs to provide a unique service that is feasible for me to build, and will be useful to a wide variety of people. I can offer it for very little money (one person's side project), and hopefully have some fun learning about deep learning. I'm so new to it that I'm not even thinking about advancing the state of the art or doing novel work. But, in a couple of years, who knows. Just tinkering with things in a new industry often provides pathways to cool stuff because so many doors are opening all the time. This is like being involved in the Internet in the early-to-mid 90s. You probably won't become the next Google, but the odds of finding a highly profitable smaller niche seems pretty high. Also, there's going to be a ton of acquisitions in the AI/deep learning space over the next decade. Every company that even does a little tech will "need" an AI story to keep their investors happy. Your tiny thing could be one of those acqui-hires, or maybe not. Then again, if you have an interest in other stuff, and really don't feel excited about it...probably not worth forcing yourself to get into it. Life is short, you should do stuff that's fun, even if you have to ring the cash register now and then.

liamzebedee 10y ago
Could you provide an example of a project you'd develop? :-)
- SwellJoe 10y ago
  I'll be posting a Show HN post in a few weeks with one of those ideas. But, there's a bunch of ideas I've brainstormed around using things like sentiment analysis and other kinds of very simple-to-use AI concepts for automating tedious stuff. Things like automatically triaging support requests based on how angry the customer sounds, or based on keywords and an analysis of earlier requests; off-the-shelf NLP algorithms can do this today (and Google uses it that way for their own support tools, but doesn't make it widely available in that form, though Inbox has some of that kind of tech working in it). All you need is training data and some familiarity with Python. My brainstorming exercise goes something like this: Append "with spooky powers" to a bunch of common things until one of them seems cool and useful to me. So, "forum notifications bot with spooky powers", "IRC bot with spooky powers", "twitter bot with spooky powers", "customer relationship management with spooky powers", "analytics with spooky powers", "server monitoring with spooky powers", "log analysis with spooky powers", etc. And I try to think of what I would use such a thing for, if it existed. Then, I sit down and see if I can make it real. The Yahoo NSFW image detection announcement reminded me of ideas I had and tinkered with a decade ago when I worked on a content filtering system for schools...the difference is that now we have the horsepower, the data sets, and the algorithms to actually make it work (but, I haven't worked in that field in a decade and never really liked being a purveyor of censorship tools, even if only for children, anyway). Anyway, the possibilities are kinda huge and wide open. Many of these ideas will fizzle out, even the ones that look promising, but as with the Internet a lot of millionaires are going to be made by people saying, "It's like X, but with AI." just as people used to say, "It's like X, but on the Internet."

ww520 10y ago

It's hard to just have a product centered around AI, but you can add AI to one of the components in your product. E.g. drawing a box around the face in a photo using face recognition technique is fairly approachable. It just means you need to call a library. A lots of the AI are well understood and encapsulated.

hinkley 10y ago

If someone proves that deep learning is applicable to most software categories (and I honestly don't think it is, at least not for a very long time), then someone will sell a solution you can plug into your app. We already have a disturbing quantity and variety of user metric apps for the web. Realtime feedback just requires someone to bring engineering discipline to bear on the space and produce a functional and efficient version. Instead of a bunch of code monkey asshats making my beautiful, fast web app soul crushingly slow because my bosses said yes to one more tracking addon.