Understanding Deep Learning • Hacker News Board

Re: Understanding Deep Learning

#101

Post by mnky9800n » Wed, Nov 29, 2023, 10:05 AM UTC

whatever_yeah wrote: post #89

mnky9800n wrote: post #19

Earlier quoted context omitted.

Which drama of last week are you referring to? The one about the openai guy saying it's all just the data set? Or something else?

I must have missed the "dataset" news you're referring to, could you elucidate?

i suppose its not news, but there was a long thread about it but it came from this post from june:

https://nonint.com/2023/06/10/the-it-in-ai-models-is-the-dat...

Re: Understanding Deep Learning

#102

Post by Breza » Thu, Dec 07, 2023, 6:10 PM UTC

martingoodson wrote: post #86

gardenhedge wrote: post #76

Earlier quoted context omitted.

Would you see these as analogous? The people who create the models and the people that use them. The people who create the programming languages and the people that use them.

I think because it's a relatively 'younger' field, there is a bit more need to know about the foundations in AI than in programming. You hit the perimeters a bit more often and need to do a bit of research to modify or create a model. Whereas it's unlikely in most programming jobs you would need to do any research into programming language design.

I agree with you. Being a corporate department head, I've led exactly one project that's had me digging through my DS&A textbook. But it's much more common to need to go beyond the limits of an off-the-shelf deep learning algorithm. Plus many of the cutting edge deep learning advancements have been fairly simple to implement but required serious effort to create, and being able to understand an Arxiv paper can have a direct impact on the job you're currently working on, whereas being able to read all of TAOCP will make you a better coder, but in a more abstract way.

Re: Understanding Deep Learning

#103

Post by Breza » Thu, Dec 07, 2023, 6:20 PM UTC

GeneralMayhem wrote: post #41

nsxwolf wrote: post #9

As someone who missed the boat on this, is learning about this just for historical purposes now, or is there still relevance to future employment? I just imagine the OpenAI eats everyone's lunch in regards to anything AI related, am I way off base?

The most important thing to learn for most practical purposes is what the thing can actually do. There's a lot of fuzzy thinking around ML - "throw AI at it and it'll magically get better!" Sources like Karpathy's recent video on what LLMs actually do are good anti-hype for the lay audience, but getting good practical working knowledge that's a level deeper is tough without working through it. You don't have to memor…

Terrific explanation, and it matches my experience running a data science team. I encourage my team to start with the simplest possible approach to every problem, which requires understanding how different algorithms work. Does this project require a t-test, XGBoost, a convolutional neural network, something else? What if we recode the dependent variable from numeric to binary?