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A non-technical explanation of deep learning

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Re: A non-technical explanation of deep learning

#51

Post by lxe »

zvmaz wrote: post #31

I have met people who think they understand a particular topic I am versed in, but actually don't. Similarly, I am often wary that I get superficial knowledge about a topic I don't know much about through "laymen" resources, and I doubt one can have an appropriate level of understanding mainly through analogies and metaphors. It's a kind of "epistemic anxiety". Of course, there are "laymen" books I stumbled upon whic…

I've noticed that the learning curve stary fairly flat when it comes to understanding weights, and layers, and neural networks, heck, even what gradient descent is for... but then when it comes to actually understanding why optimization algorithms are needed, and how they work, things just spiral into very hard math territory.

I do think that maybe it feels inaccessible because we transition from discrete concepts easily digestible by CS grads into some complicated math with very terse mathematic notation, yet the math might not be as hard if presented in a way that doesn't scare away programmers.

Re: A non-technical explanation of deep learning

#52

Post by flangola7 »

SnooSux wrote: post #32
jstx1 wrote: post #23

Does stuff like this help anyone? I still haven’t forgiven CGP Grey for changing the title to his 2017 ML video to “How AIs, like ChatGPT, learn”. The video is about genetic algorithms and has nothing to do with ChatGPT. (or with anything else in modern AI)

I've barely forgiven him for explaining genetic algorithms and acting like they have any relevance to contemporary ML research. The footnote video was an alright explanation of backprop. If that were part of the main video that would have been reasonable. I really like his history/geography videos but anything technical leave a lot to be desired. And don't get me started on Humans Need Not Apply.

Humans Need Not Apply is one of the most phenomenal videos on YouTube, what do you think is wrong with it?

Re: A non-technical explanation of deep learning

#53

Post by [deleted] »

mach1ne wrote: post #18
amelius wrote: post #8

The problem with deep learning is opposite. You can understand most of it with just high school math. Advanced math is mostly useless because of the dimensionality of neural nets.

Yes, it’s really rather like alchemy in some sense. Stuff works, and often nobody knows exactly why.

[deleted]

Re: A non-technical explanation of deep learning

#54

Post by nomel »

joe_the_user wrote: post #43
sainez wrote: post #38

Earlier quoted context omitted.

I find the best way to learn technical topics is to build a simplified version of the thing. The trick is to understand the relationship between the high level components without getting lost in the details. This high level understanding then helps inform you when you drill down into specifics. I think this book is a shining example of that philosophy: https://www.buildyourownlisp.com/ . In the book, you implement an…

I find the best way to learn technical topics is to build a simplified version of the thing. The trick is to understand the relationship between the high level components without getting lost in the details. This high level understanding then helps inform you when you drill down into specifics. I agree but that's a good guide to build a technical understanding of a complex subject, not sufficient-in-itself tool set f…

> constant annoyance to me whenever such topics come here.

I'll say that thinking about things at the edge of my understanding, where "Eureka!" moments are low hanging fruit, results in the highest dump of dopamine out of any other activity. Having silly fun speculating (and I make it clear when I am) my way through some thought process is literally the most fun I can have. Seeing those types of conversations, full of genuine curiosity, thoughtful speculation, and all the resulting corrections/discussions/insight, etc, is why I love HN so much, and I hope it's always a place to nerd out.

One mans trash is another mans treasure, I suppose. :)

Re: A non-technical explanation of deep learning

#55

Post by teruakohatu »

_gmax0 wrote: post #45

The most concise and intuitive line of explanation I've been given goes along the lines of this: 1 - We want to model data, representative of some system, through functions. 2 - Virtually any function can be expressed by a n-th order polynomial. 3 - We wish to learn the parameters, the coefficients, of such polynomials. 4 - Neural networks allow us to brute-force test candidate values of such parameters (finding opti…

> Whereas prior, methods (e.g. PCA) could only model linear relationships,

Prior methods also allowed modelling of non-linear relationships, eg. Random Forests.

Re: A non-technical explanation of deep learning

#56

Post by cscurmudgeon »

wrs wrote: post #15
pringk02 wrote: post #12

Earlier quoted context omitted.

Doesn't that mean it supports the Chinese room argument? I'm not sure I follow your reasoning. (also, popular conciousness forgets that technically the Chinese Room argument is only arguing against the much narrower, and now philosophically unfashionable, "Hard AI" stance as it was held in the 70s)

I understand the Chinese Room argument to be that because the human in the room doesn’t understand Chinese, the system doesn’t understand Chinese. In this case, none of the humans can recognize cats, but the collective can.

Thats not the Chinese Room argument. The argument says just because a system processes X doesn't imply it has consciousness of X.

Re: A non-technical explanation of deep learning

#58

Post by pedrosorio »

_gmax0 wrote: post #45

The most concise and intuitive line of explanation I've been given goes along the lines of this: 1 - We want to model data, representative of some system, through functions. 2 - Virtually any function can be expressed by a n-th order polynomial. 3 - We wish to learn the parameters, the coefficients, of such polynomials. 4 - Neural networks allow us to brute-force test candidate values of such parameters (finding opti…

Mentioning polynomials is a pretty poor way to explain it for two reasons:

- It requires some mathematical understanding so will exclude some part of the non-technical audience

- It is the incorrect analogy. Non-linearities in neural networks have nothing to do with polynomials. In fact, polynomial regression is a type of linear regression, and for the most part, it sucks.

Also, as someone mentioned, all the “serious” alternative ML methods prior to the deep learning revolution allow modeling non linearities (even if just through modification of linear regressions, like polynomial regression).

Re: A non-technical explanation of deep learning

#59

Post by Maultasche »

onikolas7 wrote: post #7

Funny. In the game black&white you would slap or pet your avatar to train it. The lead AI programmer on that was Demis Hassabis of deepmind fame.

The description made me think of Black & White as well. I still have memories of smacking my creature around every time he ate someone.

Re: A non-technical explanation of deep learning

#60

Post by gnicholas »

jstx1 wrote: post #23

Does stuff like this help anyone? I still haven’t forgiven CGP Grey for changing the title to his 2017 ML video to “How AIs, like ChatGPT, learn”. The video is about genetic algorithms and has nothing to do with ChatGPT. (or with anything else in modern AI)

I read this to see if it would be useful to share with my 9 year old. After reading it, I think it is not any more useful (alone) than watching the 3b1b video on this topic. The video is longer, but has more visualizations.

I think that perhaps reading this description after watching the video might make the process more memorable. My guess is that if I had my daughter read this first, it wouldn't do much to make the video easier to parse. Reading this real-world example after watching the video could help solidify the concept.

Disclaimer: I don't know a lot about AI/ML, so it's possible that I am 100% wrong here!

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