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Machine Learning Is Still Too Hard for Software Engineers

nyckel.com

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Re: Machine Learning Is Still Too Hard for Software Engineers

#31

Post by anon291 »

thanhhaimai wrote: post #30
carom wrote: post #16

As a software engineer, I disagree. Caveat, I haven't studied traditional ML and just went straight to DL. There is a lot of jargon and you do have to sit down and learn how things work, but once you do, deep learning is fairly simple. One thing that actually really bothers me is how much libraries (e.g. huggingface) are just config files masquerading as programming. It is just a class with 50 parameters and takes ab…

> There is a lot of jargon and you do have to sit down and learn how things work, but once you do, deep learning is fairly simple. This is my realization too. I think ML for SWE courses should focus on "translation" first. Like by "kernel" they mean this specific thing, not the normal meaning of kernel. This is similar to other fields like finance (which I'm working on). After you learn the language, it's actually no…

What's the normal meaning of 'kernel'?

EDIT: Hacker news won't let me respond, but the answers below all seem to be because the original meaning has been lost on everyone.

In English, the word 'kernel' means 'core'. An OS kernel is the core of an operating system. In linear algebra, the kernel of a matrix (or a linear transformation, same thing) is the set of vectors it maps to zero, which is also in a sense the 'core' of the mapping (in so far that zero can be seen to be at the 'core' of the vector space / number line).

So actually, the definition is the same, it's just that the word kernel is rather rare, despite having a well understood meaning. Nevertheless, it is the kernel of many common English idioms such as a 'kernel of truth'.

Going to ML kernels... the etymology is a bit convoluted. I believe they come from operator theory and support vector machines. But nevertheless, the 'normal' meaning of kernel works out because kernels are typically the core fundamental operations supported by a machine learning framework atop which the other operations are built. In that sense, regardless of the etymology, the name actually fits.

Re: Machine Learning Is Still Too Hard for Software Engineers

#34

Post by shortrounddev2 »

bluish29 wrote: post #28
AznHisoka wrote: post #2

For me the hardest part of learning ML was getting over imposter syndrome. It felt like I needed a PhD and hardcore math skills. That’s what made me so hesitant in learning it. I thought: there’s already so many people much more smarter and advanced for me. Why even bother? It wasn’t until I was “forced” to learn it to solve a problem I was facing, that I realized ML is just like any other engineering topic - whether…

> It felt like I needed a PhD and hardcore math skills I don't know if this helps more or make it worse. But I have both and getting the same feelings all the time. But basically you just need good statistics and linear algebra knowledge, and you will be fine (on the math side). > there’s already so many people much more smarter and advanced for me. Why even bother? That's the very definition of imposter syndrome put…

99% of the time people say linear algebra is required for something, they mean knowledge basic operations and properties of tensors more than actual "algebra". I found this when doing computer graphics. Is that true as well here?

Re: Machine Learning Is Still Too Hard for Software Engineers

#36

Post by jzombie »

carom wrote: post #16

As a software engineer, I disagree. Caveat, I haven't studied traditional ML and just went straight to DL. There is a lot of jargon and you do have to sit down and learn how things work, but once you do, deep learning is fairly simple. One thing that actually really bothers me is how much libraries (e.g. huggingface) are just config files masquerading as programming. It is just a class with 50 parameters and takes ab…

> We just need some core work horse libraries.

Do you mean like PyTorch, TensorFlow, or the others?

Re: Machine Learning Is Still Too Hard for Software Engineers

#37

Post by senseiV »

valine wrote: post #18
esafak wrote: post #7

ML is a broad topic, and it keeps getting wider, and deeper. Even ML specialists don't try to keep up with it all. Be comfortable with not knowing everything. Machine learning engineers are software engineers, and they exist, so the title is wrong. I suppose it is in Nyckel's interest to claim otherwise.

The industry has been consolidating around transformers. I'd say it's getting deeper, not wider.

NLP is not the industry, and a lot of research still goes into other things, like RL

I've worked with several transformers competitors, and it def wont stay centralized on them

Re: Machine Learning Is Still Too Hard for Software Engineers

#38

Post by calebh »

corethree wrote: post #10

ML is one of the easiest fields out there. When I learned it I was actually turned off by how simplistic the concept was. Of course let me preface to say that it's hard to develop the intuition and skill in the same way learning to skateboard is hard. But conceptually it's easy and very possible for almost anyone. The whole thing is just curve fitting. Literally finding some best fit curve across a series of points.…

What are ML jobs about? I have this vague notion that you spend a lot of time gathering/cleaning data and throwing things at the wall, but maybe that's not accurate.

I've always been stronger at discrete type math/programming, which is why I tend to shy away from statistics-based stuff like ML.

One thing to note is that LLMs are indeed feed forward, however the generation of the text (from my understanding) is recursive in that you feed each output token to another forward pass of the neural network.

Re: Machine Learning Is Still Too Hard for Software Engineers

#39

Post by [deleted] »

anon291 wrote: post #31
thanhhaimai wrote: post #30

Earlier quoted context omitted.

> There is a lot of jargon and you do have to sit down and learn how things work, but once you do, deep learning is fairly simple. This is my realization too. I think ML for SWE courses should focus on "translation" first. Like by "kernel" they mean this specific thing, not the normal meaning of kernel. This is similar to other fields like finance (which I'm working on). After you learn the language, it's actually no…

What's the normal meaning of 'kernel'? EDIT: Hacker news won't let me respond, but the answers below all seem to be because the original meaning has been lost on everyone. In English, the word 'kernel' means 'core'. An OS kernel is the core of an operating system. In linear algebra, the kernel of a matrix (or a linear transformation, same thing) is the set of vectors it maps to zero, which is also in a sense the 'cor…

[deleted]

Re: Machine Learning Is Still Too Hard for Software Engineers

#40

Post by anon291 »

graphe wrote: post #21
corethree wrote: post #10

ML is one of the easiest fields out there. When I learned it I was actually turned off by how simplistic the concept was. Of course let me preface to say that it's hard to develop the intuition and skill in the same way learning to skateboard is hard. But conceptually it's easy and very possible for almost anyone. The whole thing is just curve fitting. Literally finding some best fit curve across a series of points.…

https://en.wikipedia.org/wiki/Moravec%27s_paradox Moravec's paradox is the observation in artificial intelligence and robotics that, contrary to traditional assumptions, reasoning requires very little computation, but sensorimotor and perception skills require enormous computational resources. The principle was articulated by Hans Moravec, Rodney Brooks, Marvin Minsky and others in the 1980s. Moravec wrote in 1988, "…

> Moravec wrote in 1988, "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility".

The resolution to the paradox is so simple I must be missing something. The amount of data in datasets for 'mobility' is basically zero. You would have to manually construct such a dataset. Whereas, humans have for thousands of years been trained to symbolically encode their reasoning processes in a way that has been incredibly accessible to computers (prose).

If I understand correctly, the scaling laws for mobility are the same for language and reasoning. We need more data.

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