That doesn't mean we _understand_ them, that just means we can put the blocks together to build one.

▲ AdieuToLogic 5 days ago | parent | next [-]

> That doesn't mean we _understand_ them, that just means we can put the blocks together to build one.

Perhaps this[0] will help in understanding them then:

  Foundations of Large Language Models

  This is a book about large language models. As indicated by 
  the title, it primarily focuses on foundational concepts 
  rather than comprehensive coverage of all cutting-edge 
  technologies. The book is structured into five main 
  chapters, each exploring a key area: pre-training, 
  generative models, prompting, alignment, and inference. It 
  is intended for college students, professionals, and 
  practitioners in natural language processing and related 
  fields, and can serve as a reference for anyone interested 
  in large language models.

0 - https://arxiv.org/abs/2501.09223

▲ throwaway314155 5 days ago | parent [-]

I think the real issue here is understanding _you_.

▲ AdieuToLogic 5 days ago | parent [-]

> I think the real issue here is understanding _you_.

My apologies for being unclear and/or insufficiently explaining my position. Thank you for bringing this to my attention and giving me an opportunity to clarify.

The original post stated:

  Since LLMs and in general deep models are poorly understood ...

To which I asserted:

  This is demonstrably wrong.

And provided a link to what I thought to be an approachable tutorial regarding "How to Build Your Own Large Language Model", albeit a simple implementation as it is after all a tutorial.

The person having the account name "__float" replied to my post thusly:

  That doesn't mean we _understand_ them, that just means we 
  can put the blocks together to build one.

To which I interpreted the noun "them" to be the acronym "LLM's." I then inferred said acronym to be "Large Language Models." Furthermore, I took __float's sentence fragment:

  That doesn't mean we _understand_ them ...

As an opportunity to share a reputable resource which:

  .. can serve as a reference for anyone interested in large
  language models.

Is this a sufficient explanation regarding my previous posts such that you can now understand?

▲ throwaway314155 4 days ago | parent [-]

I'm telling you right now, man - keep talking like this to people and you're going to make zero friends. However good your intentions are, you come across as both condescending and overconfident.

And, for what it's worth - your position is clear, your evidence less-so. Deep learning is filled with mystery and if you don't realize that's what people are talking about when they say "we don't understand deep learning" - you're being deliberately obtuse.

===========================================================

edit to cindy (who was downvoted so much they can't be replied to): Thanks, wasn't aware. FWIW, I appreciate the info but I'll probably go on misusing grammar in that fashion til I die, ha. In fact, I've probably already made some mistake you wouldn't be fond of _in this edit_.

In any case thanks for the facts. I perused your comment history a tad and will just say that hacker news is (so, so disappointingly) against women in so many ways. It really might be best to find a nicer community (and I hope that doesn't come across as me asking you to leave!) ============================================================

▲ AdieuToLogic 4 days ago | parent | next [-]

> I'm telling you right now, man - keep talking like this to people and you're going to make zero friends.

And I'm telling you right now, man - when you fire off an ad hominem attack such as:

  I think the real issue here is understanding _you_.

Don't expect the responder to engage in serious topical discussion with you, even if the response is formulated respectfully.

	▲	throwaway314155 4 days ago \| parent [-]
		What I meant to say is that you were deliberately speaking cryptically and with a tone of confident superiority. I wasn't trying to imply you were stupid (w.r.t. "Ad Hominem"). Seems clear to me neither of us odd going to change the others mind though at this point. Take care. edit edit to cindy: =======================••• fun trick. random password generate your new password. don't look at it. clear your clipboard. you'll no longer be able to log in and no one else will have to deal with you. ass hole ========================== (for real though someone ban that account)

▲ cindyllm 4 days ago | parent | prev | next [-]

[dead]

▲ cindyllm 4 days ago | parent | prev [-]

[dead]

▲ AdieuToLogic 3 days ago | parent | prev [-]

>>> Since LLMs and in general deep models are poorly understood ...

>> This is demonstrably wrong.

> That doesn't mean we _understand_ them ...

The previous reply discussed the LLM portion of the original sentence fragment, whereas this post addresses the "deep model" branch.

This article[0] gives a high-level description of "deep learning" as it relates to LLM's. Additionally, this post[1] provides a succinct definition of "DNN's" thusly:

  What Is a Deep Neural Network?
  
  A deep neural network is a type of artificial neural 
  network (ANN) with multiple layers between its input and 
  output layers. Each layer consists of multiple nodes that 
  perform computations on input data. Another common name for 
  a DNN is a deep net.
  
  The “deep” in deep nets refers to the presence of multiple 
  hidden layers that enable the network to learn complex 
  representations from input data. These hidden layers enable 
  DNNs to solve complex ML tasks more “shallow” artificial 
  networks cannot handle.

Additionally, there are other resources discussing how "deep learning" (a.k.a. "deep models") works here[2], here[3], and here[4].

Hopefully the above helps demystify this topic.

0 - https://mljourney.com/is-llm-machine-learning-or-deep-learni...

1 - https://medium.com/@zemim/deep-neural-network-dnn-explained-...

2 - https://learn.microsoft.com/en-us/dotnet/machine-learning/de...

3 - https://www.sciencenewstoday.org/deep-learning-demystified-t...

4 - https://www.ibm.com/think/topics/deep-learning