konsalexee

Nice @dmraptis thanks for sharing!

Thanks is highly appreciated in our community

Thank you for the input I will practice!

Clients may know but the thing is that the language we are using at our startups affect our conversions for example. A good copy may convert way more users!

> I believe this can quite easily be done. The only issue I can see is adoption.

I believe the adoption will be from tech-oriented platform let's say for example Codepen, Brave and other if you can demonstrate the value proposition to the devs which are developing currently concerns about privacy. Privacy Invasion is real.

> But, some users will want services to use the auth solution before they create an account.

This is true and in order to kickstart this, an OSS community could help a lot.

> The OpenID spec specifies that an id_token needs to contain the following, based on scope: - email: email, email_verified - profile: name, family_name, given_name, middle_name, nickname, preferred_username, profile, picture, website, gender, birthdate, zoneinfo, locale, updated_at

I understand the need of an email and other data, and that they can work as PKs in the Web/Mob apps' databases. But also those data are a medium to communicate with the actual user.

So again I am curious about why ProtonMail doesn't provide a mechanism like this. They have all the data, the brand and the credibility to run a service with the following business model.

> In general, it's hard to optimize problems which involve others optimizing against you.

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 think the most important part of predictive design is personalization

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.

> more bland, generic work

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.

> When it's their brand guidelines vs your black box optimization function which is backed by data, their brand still has priority.

We recently had the biggest telecommunication provider in Greece as a client, and we conducted an Eye Tracking study on their web services. We found that their branding guidelines in web elements like buttons were hurting the performance.

A few creative employes of this company, when they mentioned this problem their supervisors ignored it. But when you are data-driven like we provided Eye Tracking evidence, they actually hear your opinion and you eliminate the subjective factor, or as the UX designer of the company told me the HIPPO effect.

https://www.forbes.com/sites/bernardmarr/2017/10/26/data-dri...

I would love to see that paper too. Actually, I didn't know about LTV estimation papers to be honest.

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

That means that the big players with large budgets will need to step up their game. As I see it, automation enables right now smaller budgets, so A.I. and in general machine learning can step up the "rich" guys game.

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

I wrote in the article the definition of Predictive design.I quote the definition below.

"In Predictive Design, data is collected, a statistical model is formulated, predictions are made, and the model is validated (or revised) as additional data becomes available."

That means that A.I. in design will not work without designs created by humans and in general curation by humans. Unsupervised Designing is actually something I believe will never happen. You stated correctly that most revenue lies in the combination of inputs by humans and AI, cannot agree more!

To be honest I hate this too, but I have extremely more visibility than my personal blog.