zlacker

>>medler+(OP)
If Altman gets to return, it’s the goodbye of AI ethics within OpenAI and the elimination of the nonprofit. Also, I believe that hiring him back because of “how much he is loved by people within OpenAI” is like forgetting that a corrupt president did what they did. In all honesty, that has precedent, so it wouldn’t be old news. Also, I read a lot of people here saying this is about engineers vs scientists…I believe that people don’t understand that Data Scientists are full stack engineers. Ilya is one. Greg has just been inspiring people and stopped properly coding with the team a long time ago. Sam never did any code and the vision of an AGI comes from Ilya…Even if Mira now sides with Sam, I believe there’s a lot of social pressure for the employees to support Sam and it shouldn’t be like that. Again, I do believe OpenAI was and is a collective effort. But, I wouldn’t treat Sam as the messiah or compare him to Steve Jobs. That’s indecent towards Steve Jobs who was actually a UX designer.

>>mariaa+Xf1
> I believe that people don’t understand that Data Scientists are full stack engineers.

What do you mean by "full stack"? I'm sure there's a spectrum of ability, but frankly where I'm from, "Data Scientist" refers to someone who can use pandas and scikit-learn. Probably from inside a Jupyter notebook.

>>d-z-m+mo1
Machine learning, data science, Deep learning= backend

Plotting, Charting ,visualization, = frontend

>>v3ss0n+Wv1
This is proving the point of the parent comments.

My view of the world, and how the general structure is where I work:

ML is ml. There is a slew of really complex things that aren’t just model related (ml infra is a monster), but model training and inference are the focus.

Backend: building services used by other backend teams or maybe used by the frontend directly.

Data eng: building data pipelines. A lot of overlap with backend some days.

Frontend: you spend most of the day working on web or mobile technology

Others: site reliability, data scientists, infra experts

Common burdens are infrastructure, collaboration across disciplines, etc.

But ML is not backend. It’s one component. It’s very important in most cases, a kitschy bolt on in other cases.

Backend wouldn’t have good models without ML and ML wouldn’t be able to provide models to the world reliably without the other crew members.

The fronted being charts is incorrect unless charts are the offering of the company itself