Trump said: Cuba is next.
And we are just sitting here watching world-wide unpopular, unloved, decadent criminals asphyxiate our friend. Shame on us!

Hence this is a moment where we ask: What The Actual Fuck?
Some Mexican journalist and experts had voiced their surprise and disappointment. I'm neither.
To me this is a surprise, yes, but not so much.
What would you expect from a president that planned to rely on "Near-shoring" for economy growth?

Cuba and Mexico had been life long friends. A maxim I grew up with, no matter what, no matter the presidential chair, Mexico will be there for Cuba. This maxim survived presidents and various international pressures. It was an indisputable truth. One should expect to be in a so-called leftist gov.

The Internet didn't build itself; we built it.
If we want better AI models, we need better training data.
To get better massive training data, we need better Internet content.
To get a better Internet content, we need a healthier society.

I posted this on LinkedIn, I wanted to share it here.

However, let us not forget that, with our "Internet data trained" AI models, we are being forced to look at ourselves in a mirror that, many times, enhances a reflection we have conveniently decided to bury; a reflection that scares us.

If you create and create and create mixed colors over and over and over again, and all seem to have a Red component, what can you conclude?
As biases in AI models came to light for the general public, experts' debates often focused on the implications of using these tools for society.

Moreover, our own pleasantness concept may not agree at all with the underlying pleasantness of the training set.

Remember a famous Time article about OpenAI hiring Kenyans to make ChatGPT 'less toxic' from 2 or 3 years ago?

Each time you give the model a prompt (a series of draws in our example), the model gives you a response (a magical mixed color).
We do not know the characteristics of the training data (base colors' pleasantness), but we may know some of the base colors (academic, formal, humorous, sexist, racist).

If all the mixed colors seem to have a 'red touch' (whatever that means), you could conclude that Red must have a high probability.

What does this mental exercise have to do with Grok and corporate AI in general?
It is an oversimplification of how dominant AI models work:

Once you mix colors, you cannot separate them, so you cannot determine the components.
If you are allowed to create as many mixed colors as you wish, but do not know Pp. Would you be able to make "good" guesses about Pp?

The unit is magical; it will refill with its color once it is inside the Main urn.
You cannot peek inside the Mixing urn, nor see which color you draw from the Main urn. Colors are drawn following Pp.
The Mixing urn will give the color resulting from mixing all the colors.

Imagine you throw "magical" 1-unit colors into an opaque Main urn.
You also have a second opaque urn that we will call "Mixing urn".
Each time you draw a color from the Main urn, you empty its contents into the Mixing urn and then return the empty unit into the Main urn.

To see why this was foreseeable, let's run a quick mental exercise.
Imagine you have 10 colors: C1, C2,..., C10.
Each color has the 'pleasantness' property. Ck.pleasantness is a number that reflects how pleasant Ck is.
Assume the pleasantnesses (what a word) make a probability function, call it Pp.

Press Conference: www.youtube.com/watch?v=pqzh...
Reuters Greenland (stats): www.reuters.com/graphics/GRE...
Gabriela Jiménez Telegram (spanish): t.me/rpnit

But guess what? there are TONs and TONs of natural resources in Greenland that, I'm sure the USA would love to "liberate". And as we already saw, Trump's USA care for international law a lot less than it care for its own.
Sources on the next post: