Because he was ceo of a company in a critical position to define the future of economy. Currently the tech field is the biggest and most influential of all economic fields. And by tech here we talk about digital world. There’s absolutely no comparable sector at the moment for importance, not even pharma.

It literally defines the modern economy. In the field, openai is an incredibly important company for future relative success and power of big tech companies.

This is why it is so important for world economy

It refers to the fact that feelings are not a reflection of the outside reality, but a reflection of one’s perception of it. According to OP, this is proven by how feelings completely change by simply changing the way the brain perceives reality, via a psycotropic compound, while actual reality remains unchanged.

This is a well known scientific and philosophical fact, that OP has only come to know recently thanks to personal experience with psycotropic drugs

Such epiphany resulted in the shower thought we are commenting.

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In the easiest example of a neuron in a artificial neural network, you take an image, you multiply every pixel by some weight, and you apply a very simple non linear transformation at the end. Any transformation is fine, but usually they are pretty trivial. Then you mix and match these neurons to create a neural network. The more complex the task, the more additional operations are added.

In our brain, a neuron binds some neurotransmitters that trigger a electrical signal, this electrical signal is modulated and finally triggers the release of a certain quantity of certain neurotransmitters on the other extreme of the neuron. Detailed, quantitative mechanisms are still not known. These neurons are put together in an extremely complex neural network, details of which are still unknown.

Artificial neural network started as an extremely coarse simulation of real neural networks. Just toy models to explain the concept. Since then, they diverged, evolving in a direction completely unrelated to real neural network, becoming their own thing.

No, what you describe is a basic decision tree. Let’s say the simplest possible ML algorithm, but it is not used as is in practice anywhere. Usually you find “forests” of more complex trees, and they cannot be used for generation, but are very powerful for labeling or regression (eli5 predict some number).

Generative models are based on multiple transformations of images or sentences in extremely complex, nested chains of vector functions, that can extract relevant information (such as concepts, conceptual similarities, and so on).

In practice (eli5), input is transformed in a vector and passed to a complex chain of vector multiplications and simple mathematical transformations until you get an output that in the vast majority of cases is original, i.e. not present in the training data. Non original outputs are possible in case of few “issues” in the training dataset or training process (unless explicitly asked).

In our brain there are no if/else, but electrical signals modulated and transformed, which is conceptually more similar to the generative models than to a decision tree.

In practice however our brain works very differently than generative models

There is not a single if/else in a neural network. You are confusing it with decision trees that are used for classification

“Life is long, I’ll have plenty of time to do it in the future”. Spoiler: life is short

Have you thought asking support from a counselor? It might help to cope

You are most probably wrong. How old are you?

Time really cures such feelings. In 20 years you’ll watch back to these events with a very different perspective

That requires user profiling, something most lemmy users are vocally against.

It also requires a data science team and a bunch of additional legal stuff.

Not even a pure mckinsey type of company value kpis over stakeholders’ feedbacks. If a company is purely kpi driven, it is a bad company, as kpi cannot catch everything, but have limited and specific scope. Your managers should go back to their MBAs, and revise their stakeholder management skills. If a manager get a feedback that one of their team members is jeopardizing a project and the relationship with clients due to taking responsibilities and tasks for which they have no competency, it is extremely bad. In this case is even proved by the fact that the company must spend resources lowering the clients expectations. Managers should absolutely act. If this doesn’t happen, the managerial side of your company is pretty broken

Lemmy user here, I don’t see it either… The guy has bad settings

Best practice is to clearly state that PM here is not competent for its job, either he finds a solution himself (e.g. he manages expectations of clients without admitting he fucked up) or he has to be replaced.

This kind of situation is very dangerous. PM shouldn’t take similar decisions, nor promising anything

Why do you have a project manager discussing technical solutions? That’s kind of… very wrong. Most PMs nowadays have a just a slightly better technical background than a secretary…

In all fairness, Jesus was probably an historical figure. Everything built around his life though…

It is partially. But if someone uses excel professionally their requirements are pretty high. Tbf, I don’t know the details because I never use it, but calc is behind excel for professional use as far as I understand

Depends on the task. It is much better for ml, ai, scientific computing and high performance computing in general, developers…

But I use libreoffice only for cover letters and cvs.

If excel is needed, Linux is a problem

I don’t see anything wrong in this picture. ML == funny!

“AI Casino” vs “old school nights banging heads on books”

Edit. Guys, it was a joke, do I really need to put /s?

Because someone else might need to work on something on or from my branches. And I don’t want garbage in my history. There are cases I might not be able to squash merge, so all my history will be in the project history. I want each commit to be clean. It is not a lot of effort, and forces me to increase code quality, because I review my code more often.

Rules for all projects I manage: never rebase published branches and always publish clean code (even implementation is unfinished).

From experience following these simple rules make the whole project management easier and more effective

Our ci/cd pipelines build also feature branches. I do push often, clean code. I don’t push when I am tired enough that I can’t trust my judgment that the code I am pushing is over my personal quality threshold. I add meaningful, concise commit messages. These are my rules.

I would lose max 3 hrs of work that I already know how to re do. I can live with that. I don’t want to publish too much unfinished/unpolished work. There is always the chance someone might need the branch.

Even if drafts under development, I like to publish something that reaches the standard of my “best” me, not my “Friday evening” me