[tiagoespinha]

Interesting idea, but to be honest I think it will never take off in a large scale.

If you want to write a vague and generic piece of code that can figure out by itself what it is that its outputs ought to be with regards to its inputs, you are, with effect, creating artificial intelligence.

This, in turn, requires teaching.

What if suddenly you have new pages on your website? Your AI program thingy wouldn't be able to serve them until you taught it what these pages are and in what situations the end user might be interested in viewing them. Imagine having a 5 year old kid handling your shop's cashier. And now a new product arrived and you have to explain to him/her that there's this new product, what it's called, the price and which types of people are allowed to buy it (e.g., alcohol couldn't be sold to people younger than X years old). If you're really teaching your application with natural language, like you would with a five year old, then the effort it'd take to get that info into your system would defeat the purpose of actually using a computer to do it (computers are good at storing and looking up stuff in large data sets, better than humans for really large data sets).

This whole hype of machine learning is suitable for situations where you have large amounts of data and you want them crunched according to some basic pre-established and non-changing logic without relying on actual human labor. If the logic evolves, you'll always require humans to sit at the helm and steer the right and wrong.

My 2 cents.

[sapphireblue]

3 years ago many systems that exist today would be regarded as "scifi" or "maybe 10 years into the future". One of the most striking examples is Visual Question Answering with dynamic memory networks [1]. The model is given raw pixels and encoded question in natural language, it should output a natural language answer. See how it works on data it hasn't seen: http://i.imgur.com/074G9pg.jpg

Do not underestimate what good ML model can learn given enough data. And in case of online shopping there usually is more than enough data, and big companies like Amazon have been using ML (context bandits) for quite some time.

1. https://arxiv.org/abs/1603.01417

[argonaut]

Then would know that visual question answering performs quite poorly. It makes for a nice demo, but it's trivial/quick/easy to find entire areas of questions where the models completely. The questions have to be worded very carefully. The models are quite finnicky and dumb. For example, dynamic memory nets aren't the state of the art - the state of the art for these datasets doesn't use anything nearly as complicated as memory, etc.

[argonaut]

*completely fail

[orasis]

I disagree with your conclusion.

In my app, 7 Second Meditation, I am constantly authoring new content. It uses improve.ai to show that new content to some small number of users and learn how they react to that content.

The learning happens from the users themselves. In my particular case, within about 300-500 views the system can tell if a new piece of content is total crap and will basically stop showing it. If the content is amazing, within 100-200 views it has a strong idea and will immediately prioritize this content.

The results speak for themselves, before I started using machine learning I was getting 4 star ratings and usage was flat. Now I'm getting 5 star ratings and the app is growing.

Will it ever take off at large scale? I have no clue, but I've seen such strong benefits from the machine learning that I'm now working on developing improve.ai full time.

[arekkas]

Your example isn't even real statistical/machine learning, it's basic A/B testing. This has nothing in common with orchestrating code.

[orasis]

I would contend that bayesian reinforcement learning is statistical machine learning.

The difference between A/B testing and reinforcement learning is that reinforcement learning does not use a control and does not wait for high confidence to continue to improve. This allows much more data-efficient learning than A/B testing because at every step it is making its best guess as to whether to continue to explore or exploit what its already learned.

As to it having nothing to do with orchestrating code:

Most code is logic reacting to data. Reinforcement learning replaces hand-coded logic with goal-driven logic. If you can use a declarative approach to specify your goals, much of the logic can be moved to reinforcement learning.

[cLeEOGPw]

My thoughts exactly. AI isn't something that can replace "hard computing". That would be the equivalent of saying that we could replace computer with human doing stuff manually - all we need to do is train that human to not make mistakes most of the time.

I'd be more interested in other way of using AI to automate coding - make it to actually write code. Train it on existing code and let him figure out how to write a piece of code like a programmer would. I think it has much more potential than AI doing the calculations itself.