I saw many a Muslims quote Quran on how all Jews are evil and such.

And this is so rampant.

I see it this way- a poor guy and a rich guy both get cancer. If they can get the same medicine and the same/similar doctors, then I am fine. The poor guy might be sharing the cabin with three other people, and he is going to go home on public transit. I am okay with the rich guy having a room to himself, 8k UHD TV on the wall, and driving home using a Porsche.

I am okay with inequality, as long as all the people have the basics fulfilled.

I don't want everyone to be equally wealthy. What am I? A Communist?

Someone who could qualify to go to both Harvard and MIT will be better at anything they set their mind to than the regular grad with four year of education after the said four years.

Someone who boots up something like Worldcoin cannot be fundamentally wired to care about AI safety at all.

With all the wisdom that I have gathered in life, I can tell you that achievement- more often than not- is mostly the product of circumstances.

(That doesn't mean I believe in equal opportunities leading to equal outcomes.)

Really improved my energy. I have done this for years- on and off. During the "on" periods, there is significant and noticeable improvement in energy throughout the day.

You can also see a link in the 404 page towards the end of the page.

Who cares about benchmarks?

GP here is out of their depth.

You don't need graph theory to understand NNs at all.

You need Linear Algebra and Differential Calculus, though.

You do need graph theory to understand and do Graph Neural Networks. But that's a subfield of modern AI and many AI researchers don't know/study/research Graph NNs at all.

You don't actually need to know graph theory to do RL, Vision, ANNs, NLP, etc. unless explicitly needed in your research/job.

It's going to burst for sure, but people will be using ML/AI anyway.

If that's your concern, then you can safely do ML/AI.

But, don't hold your breath on getting a presrigious role after PhD.

Because, there are very few real AI companies. And their hiring is skewed to Stanford, MIT, UCLA-B, Oxbridge, UToronto, ETH, etc. And getting into these schools in AI was always competitive, but now it is crazy-like because all the prep school kids are eyeing this since basically high-school or even before.

So, there is someone like me, who didn't know about modern AI until last year of college in a different major, then learning on my own and getting research jobs in small time companies with shit pay, and then there are people with tiger parents who are white collar or even academics who helps their children get into a really prestigious school, and there they do AI projects with top professors, who write them recommendations, and who also do summer internships in DeepMind, and then they use that to get a job in a proper ML company. At this point they have 2-4 publications in top tier conference. Then they work in BigTech AI lab for 3-5 years and get at least 4/5 more papers (no upper limit). And these are the people who are going to Stanford PhDs. Not people like me. And then these PhDs will be Research Engineers and such in DeepMind, OpenAI, etc.

So, before deciding to do a PhD from non elite institutions, think hard.

Because of AI hype, the situation is very bad for the rest of us. Because all of the prep school types in STEM want to make it in AI.

But it shows intent for junior roles.

People have no idea how many people just put "AI enthusiast" on LinkedIn profile and start to seek ML roles.

I have had people only with Excel skills apply to ML roles.

And, true thing is every company’s ML tooling/stack/procedure is wildly different. One has to learn on the job.

100% happened to me once. Wasted hours of my time.

> Some companies are just tacking irrelevant ML and AI stuff onto job descriptions.

Some of them do this deliberately. I have seen this practice in companies targeting junior roles and fresh out of college grads. They hire them with shit pay and promise them ML experience, and then make them do non ML stuff.

Take some courses and get some certifications. And also make some serious projects where you demonstrate your capabilities with cutting edge tools.

This is more focused on tools and use of said tools.

Take some trained models, and demonstrate how well you can use them.

Some ideas:

1. Take a cats vs. dogs model, deploy it online. Design an API around it. Document the API well. Create a mechanism to show confidence score, and store low confidence score examples in a database that you can later manually label and retrain the model with.

2. Take a smallish LLM, design a VS code extension that documents your functions based on docstring.

Just demonstrate your basic knowledge in ML, and really good software engineering skills, learn the vocabulary well, and then start applying for jobs. It's much better if you have a CS/EE degree.

I know cause I did it.

And I knew the math beforehand. I was a Physics major in college with a CS minor.

If something is already done, i.e. a model is available for your exact use case (which is never), then for using and deploying that can be done by a good SWE and any ML/AI specialist is not needed at all.

To solve any real problem that is novel, you need to know a lot of things. You need to be on top the progress made by reading papers and be a good enough engineer to implement the ideas that you are going to have iff you are creative/a good problem solver.

And to read those papers you need to have solid college level Calculus and Stats.

If this is so easy, then why don't you do it, and get a job at OpenAI/Tesla/etc?

If he really did take those and did all the assignments himself and understood all the concepts, that still puts him at least in the 95th percentile among ML job seekers.