Licenciado en Física (UG). Data Scientist (Citibanamex). Estudio un MicroMasters en Data Science (MITx). Me gustan las mates aplicadas y el machine learning.
AI is not some sort of natural phenomenon that will just emerge and become dangerous.
*WE* design it and *WE* build it.
I can imagine thousands of scenarios where a turbojet goes terribly wrong.
Yet we managed to make turbojets insanely reliable before deploying them widely.
The question is similar for AI:
"do we think there exists at least one design of an AI system that is simultaneously safe/controllable, and can fulfill objectives in more intelligent ways than humans ?"
If the answer is yes, we'll be fine.
If the answer is no, we won't build it.
Right now, we don't even have a hint of a design of a human-level intelligent system.
So it's too early to worry about it.
And it's way too early to regulate it to prevent "existential risk."
1. Un sistema que fue desarrollado en el Shared Task Open Machine Translation de @AmericasNLP
Puede traducir del español a las siguientes lenguas:
Quechua
Wixarika
Shipibo-Konibo
Asháninka
Raramuri
Nahuatl
Otomí
Aymara
Guarani
Bribri
Pruébalo: https://t.co/rdEwqHJOwr
College ML usually have major holes.
Static courses are rarely up-to-date in a field moving this fast, plus they usually don’t teach the practical skills industry expects you to have.
Here are 5 changes I’d make to every college ML program:
XGBoost has proved many things, but one that flies under the radar:
It's not just how accurate the model is. It's also how easy/fast it is to use.
My training code for deep learning can be >500 lines, easily.
My XGBoost training code (w/o feature engineering) is usually <100.