AI will enable predictive design in creatives

AFAIK Netflix generates movie posters based on users data. I think this is an example of an AI based advertisement that we have today.

I can echo that, which is why I doubt that neither advertising nor design can become fully automated by AI

I could see 2 things happening in this space:

1) AI may make ordinary advertising more accessible for smaller budgets

It'd be similar to bootstrap or material design, in that they help non-design-driven products with not looking obviously cheap. But after adoption of said frameworks, products will look more alike. Hence the need for differentiation to gain more attention.

2) Given the said need for differentiation most revenue lies in the combination of inputs by humans and AI.

For example when given the fundamental parameters that define a brand's design language, an AI could iterate on that to churn out new designs that continue this language with the human fine tuning the direction.

An AI then could also deliver options of where to steer the design language in general, e.g. for car-designs:

a) add an aston-martin-like front grill (go with the general trend)

b) do the exact opposite of a)

c) allow just small changes to current design language and never mind the competitors

etc.

We have to options. Optimise something existing, which as you said a lot of competitors try to optimise again your side or the other option is to create new value/assets. The bet here is to create new value, iterate and pre-test with "A.I" models before conducting a time-consuming test, as a usability test, and last go on production.

I can’t believe it’s 2019, people still don’t know how to estimate LTV. The paper my company used for accurate LTV estimation needed 3 days of data, not 60. And it was written in like, 1987?

What do you think Facebook uses to optimize creatives so quickly?

Sure, for big businesses.

But small businesses just need to have a good enough design, not beat their opponent by a few percent.

And isn't that a big enough market to start with ?

Pretty much all AI algorithms seek to classify their data (us) into a smaller more manageable number of categories. AI methods work best when each of us 'consumers' fits neatly into one of these classes (whether predefined or emergent; each class's origin doesn't matter). Upon implementation, you shall become a label, so that future interaction with the AI can assume you to be a "Class 137" and lock you into a simplified model (with all your features having low value eigenvectors conveniently excluded). Deviation from this norm will not be tolerated, simply because there's less profit in it.

Blah. If anything, better AI models of the world need to capture more subtlety leading to better user-driven customization, not less. For Predictive Design not to become yet another Big Brother, it needs to reflect each individual's unique constellation of attributes, not the single dumbed-down class they were labeled into.

Seven billion classes. That's the ticket.

We see generic and bland work from marketers and designers quite often, to be honest. With predictive designing, we want to step up everyone's game.

If you create unique and engaging content, then for sure no A.I. can judge you. In fact, if you create extremely unique content you will be an Outlier in the prediction probably and will not score high enough. But think of banners on websites.

You have to gather the attention of the user, that will last not even 2 secs, and you have to make your creative memorable in order to increase the brand recall in your company. This is a case study were A.I. could rank your design in terms of memorability.

Do you somehow not look at this sort of massive data mining, this sort of optimization for psychological assault upon people ill-equipped to defend themselves--and this is effectively a given, because the systems you are building are designed to exploit their brains and will by design optimize for maximal exploitation 'cause that's what makes your KPIs bigger--and ask whether this is a good idea?

Do you want to be downstream of this psychological onslaught?

Doesn't this horrify you?

Isn't there something better--for yourself, and for the world at large--that you could be doing with such obvious talent?

You can have personalisation a priori or a posteriori. You can have ML logic to adapt to your user's interactions like yours, that is a posteriori approach.

Or design and have predictions about the user's interaction/attention/memorability and concepts like this which is a priori approach.

Now for personalisation, the concept lies on the data mining approach we are taking as A.I. architects. If you are designing creatives, you cannot have a posteriori approach. Therefore, it is reasonable to mine data, create the prediction models, enable of course personalisation in a higher level and then let the user define the personalisation filters (like Grammarly did with quantification of the text). For the mobile usage, the posteriori approach seems to work, as you say with boundless.ai and seems promising. And I like the terminology of AI co-designer.

"AI-Personalized, real-time UX interventions proven to rewire user behavior and drive your KPIs."

'Excitement' doesn't exactly describe my response.

An easy to grasp example from a while back in NLP is word embeddings.

We started out with vectors for each word in the training corpus, which meant we had no information for words outside of the training set, like you say.

Then people came up with "character n-grams", which built vectors for substrings, which allowed us to encode information for words outside of the training corpus based on the vectors of the constituent substrings.

You could imagine a system that could deliberate across levels of abstraction to find lines of analogical reasoning to connect things together in novel ways that people would find surprising and interesting in similar ways to human creative output, since it's all computation either way, but who knows how far away we could be from making such a system that could actually generate reasonable, surprising outputs.

If anything, this is an underestimated problem in art, music, and engineering. Digital tools make it much easier to crank out generic junk, which adds a lot of noise to the output space, which makes it harder for truly original work to be noticed or valued.

AI feels like it will be the natural limit of that process. The SNR will drift towards zero. Everything will become noise.