Comment by SwellJoe | Hacker News Reader

Comment by SwellJoe | Hacker News Reader

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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.

Could you provide an example of a project you'd develop? :-)

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."

That's a really neat way to brainstorm. Let me add one more thing.

The big companies have an advantage in hardware and research. But they dont care about niche applications of their tech, because prizes worth less than $1B don't matter at their scale. That's where I try to focus on.

The key challenge is data. Too many AI startups get stuck in the "give us your data and we'll do some awesome stuff." That almost never works. [This](http://mattturck.com/2016/09/29/building-an-ai-startup/) talk does a really good job explaining why. The trick is figuring out how to get enough initial data to deliver value upfront.

Good slide deck, and I agree with most of it (and what I don't agree with is probably my own ignorance of the field).

I suspect there will be very few "pure AI" startups, and a ton of regular old tech startups that figure out how AI fits into their business faster than their competitors or figure out how to use it for a business advantage or to deliver a service that couldn't exist in that way before AI. With the early "like X but on the Internet" startups, the ones that succeeded in the biggest way (Amazon, for example) were the ones that built a great X that leveraged the internet to make it an order of magnitude better X.

So, Amazon was the best book store because they got everything right about being a regular bookstore (good prices, good service, efficient sales channel, solid relationships with publishers) and had damned near every book and could serve customers everywhere; a thing that is only possible on the Internet.

So, the best "X except with AI" company will be a great X company, and then AI will allow them to do some kind of force multiplier to push them to the top of the heap. That means we need to look for opportunities that currently require a lot of resources (say, people, or vehicles, or ) and can have AI added to it to make it produce 10x value given the same inputs. Even 2x value could be a big enough difference to beat your competitors at market, but the real out-of-the-park success stories probably need an order-of-magnitude boost from AI, even if it starts out slower because AI is still clumsy and most of the small companies are starting with tiny data sets (relatively speaking).

Anyway, mostly I think it's cool to play with. I think I see some ways to provide value and make some money with it, but it'll be as much an experiment as a business plan in the short term.

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