I wanted to discuss a bit the product risk iteration path of my lean canvas, both to get more familiar with the tool and to better states some motivations behind it.
A simple analogy of current AI market would be to compare it to the market that put items into orbit.
You have the big professionals that have been there for long, paved the way at the day 0 and are capable of producing large and expensive rockets, though fighting with millions the technical difficulties.
You have the small entrepreneurs; taking a segment of the market, trying to make a specific solution way better and cheaper than the big providers, some might even try to become direct concurrent with them.
Then you have the amateurs: tips and tricks, discussion online; this is really small budget but a lot of passion.
But the analogy breaks apart on a decisive point; the metric is clear when you want to put something into orbit. It is not when you try to make AI.
The way to measure a successful AI is done on standard dataset; the expectations to be fit are therefore the data analyst responsibility.
How an AI could multitask if it doesn’t even have the responsibility of fitting its expectations ?
Data is even more important than that;
What digs the gap between amateurs and professionals in rocket technology is the fuel cost. The more massive your rocket is, the more fuel you need to carry. The more fuel you have to carry, the more massive your rocket becomes. It is therefore really expensive to put rockets in orbit for amateurs.
In AI, data is the fuel. It needs to be diverse, realistic, adapted to given case, capable of encompassing user behavior, labelled (deadly important if you do supervised learning), etc. But, most important of all, the computing power to train in a realistic time over those huge dataset to extract rules general enough.
A good promise for the latest is the trend over transfer learning. It’ll help take networks as complex as alpha go zero, that requires dedicated and expensive hardware to train, and make “low resolution” copies of it.
It’s a bit like, if NASA improves greatly its rockets, amateurs will be able to create cheap almost-as-good copies of them. It’s great for hobbyists, but it doesn’t propel innovation.
Couldn’t we find a way that enables modular and diverse AI? Like embedded spaces as standards that can be spread and connected in diverse ways, a bit like we orchestrate docker containers in modern application.
How could we move from a rocket market to a fish and bread market?
This is quite a haunting question. At first, it seems idiotic seeing the amount of data, expertise, computing power, and so on required to train a useful AI by today standards.
But, unlike rocket science, we can easily build tools that get us a bit closer to orbit. Though, as measure of orbit is fuzzy in AI, so are the tools we use to get there.
It means there are no standard way to put your product out in AI, which seems even harder for people that are out there with a simple high-level business process they’d like to implement and, at some point, requires face detection.
So what about the business prospective? To move away from a rocket market, we need to render large and specialized companies developing AI services obsolete.
One way could be to empower medium companies to become as efficient to provide AI services. AI tools, both community and GAFAM provided, are getting to a point where creating and training the deep neural networks is trivial. Architecture, data analysis, data sets and KPI are much more of a concern today.
This is still a challenge today, and it’s a lost cause to provide such requirements to mainstream users. Another approach is standardized trained tools: like Facebook fastText or Google SyntaxNet Parsey McParseface.
Those are unspecialized steps towards orbit: just like Bootstrap for HTML, Spring for Java, Boost for C++,… it provides you with already trained tools to build on top of.
But could we make it a thing to keep building on top of ? Could we make those tools abstract modules to be used in BPM development ?
In fact, could we make those modules as simple and abstract as they become standard pieces of any development and widen the territory of medium companies and amateurs ?
On my own, I’m also deeply curious about how far we can go with a vector representation and could we build up a new kind of algebra that handles things way more complex than empty set numbers ?