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Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy [video]

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Re: Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy [video]

#81
post #51
post #47

Earlier quoted context omitted.

What's ridiculous about stats? A Tesla with autopilot or FSD is more than 10x less likely to be in a collision, based on various US agencies stats. Average chance of collision is 1 in 366 for every 1000 miles driven. Something like 1 in 20000 of those result in fatalities. Now, take millions of Teslas equipped with any assisted-driving tech, multiply by miles driven, include the above averages, and divide by ten. Doc…

Can you cite "various US agencies stats"? I say this as someone who used FSD Beta for a full year and Autopilot (with Navigate on Autopilot) for over 3 years now. It would actually help the conversation here to cite it, though I never knew third-parties evaluated Tesla's claims/statistics.

There's also a strong selection bias to a bulk stat like the one parent mentioned. Teslas are expensive cars and I'm guessing their drivers are likely to be older, more affluent, and more educated that average - this all correlates to lower accidents/fatalities regardless of FSD[1][2].

[1] https://pubmed.ncbi.nlm.nih.gov/24103823/

[2] https://personalinjurylawyersaustintx.com/blog/education-lev...

Re: Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy [video]

#82
post #6

Earlier quoted context omitted.

rough steps: 1. collect a very large dataset, see: https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla... . scrape, de-duplicate, clean, wrangle. this is a lot of work regardless of $. 2. get on a call with the sales teams of major cloud providers to procure a few thousands GPUs and enter into too long contracts. 3. "pretrain" a GPT. one common way to do this atm is to create your own exotic fork of Megatron…

I was so confused by the saltiness until I saw the username. I'm sure you've earned it. I got into deep learning because of your char-rnn posts a while ago -- it inspired me to do an undergrad thesis on the topic. I read arxiv papers after that and implemented things from the ground up until a startup liked my work and hired me in a neural network engineer position. Fast forward a few years and I was enamoured with m…

> I was so confused by the saltiness

I was confused by what you thought was salty. I don't see it remotely.

Re: Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy [video]

#83

Earlier quoted context omitted.

We don't know how to define the essence of intelligence, so all we can do is knock all the strawmen we traditionally attribute to intelligence, until we're either left with the essence of true intelligence, or we knock off everything and we find intelligence was just a bunch of tricks after all.

I don't think the essence is especially elusive. It's the ability to make novel, meaningful, and useful discoveries from precepts that don't immediately "obviously" lead to those discoveries. Observing the sky and nature leading to a mathematical formulation of gravity is an absolutely amazing leap. In part because of what was done, but perhaps even more so for even beginning to imagine it was something that could be…

> I don't think the essence is especially elusive. It's the ability to make novel, meaningful, and useful discoveries from precepts that don't immediately "obviously" lead to those discoveries.

But that's trivial simply by enumerating all Turing machines that reproduces the observed outputs. The point of intelligence is that it somehow "filters" the space of possible theories in some specific, computable way.

The ideal model of this is Solomonoff induction, which orders Turing machines by Kolmogorov complexity, but that ordering is not computable. So intelligence is some computable approximation of this, but discovering the specifics of how that works is non-trivial.

Re: Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy [video]

#84

I am a simple man. I see a video post by karpathy, I upvote and watch. I discovered Andrej very recently and I am a huge fan. Kudos to this whole effort! Two ideas -- 1. While these explainers are outstanding -- I can think of supplementary material/presentation that can nicely complement these explanations if they are presented visually. Especially the concepts of multidimensional tensors. Something like what 3B1B (…

fastai uses its forums to manage discussions for specific chapters. It wouldn't be hard setting it up, but the instructor would have to declare it in the video or description to actually send students to the forum.

fastai forum : https://forums.fast.ai/

Re: Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy [video]

#85
post #6
post #5

I might be too new to this area -- but is this actually explaining how to create like a small version of the actual trained model -- not like "using the trained model for X"? like I can imagine in the future people won't start from pure scratch, there will be building blocks that everybody starts from, but mostly just wondering like how hard is it to actually replicate what openAI has done if you had the money to pay…

rough steps: 1. collect a very large dataset, see: https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla... . scrape, de-duplicate, clean, wrangle. this is a lot of work regardless of $. 2. get on a call with the sales teams of major cloud providers to procure a few thousands GPUs and enter into too long contracts. 3. "pretrain" a GPT. one common way to do this atm is to create your own exotic fork of Megatron…

[dead]

Re: Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy [video]

#86
post #6

Earlier quoted context omitted.

rough steps: 1. collect a very large dataset, see: https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla... . scrape, de-duplicate, clean, wrangle. this is a lot of work regardless of $. 2. get on a call with the sales teams of major cloud providers to procure a few thousands GPUs and enter into too long contracts. 3. "pretrain" a GPT. one common way to do this atm is to create your own exotic fork of Megatron…

I would love to know what your thoughts are on how software engineering (and jobs in general) will change over the next 10 years and what we lowly developers can do to keep up & maybe even be involved in that change

Andrej wrote an interesting post some years back titled Software 2.0 about the direction he saw software engineering going. It's more about changes in software than the changes in the job market, but I suspect you'd still find it interesting. https://karpathy.medium.com/software-2-0-a64152b37c35

Re: Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy [video]

#87
post #51

Earlier quoted context omitted.

Can you cite "various US agencies stats"? I say this as someone who used FSD Beta for a full year and Autopilot (with Navigate on Autopilot) for over 3 years now. It would actually help the conversation here to cite it, though I never knew third-parties evaluated Tesla's claims/statistics.

There's also a strong selection bias to a bulk stat like the one parent mentioned. Teslas are expensive cars and I'm guessing their drivers are likely to be older, more affluent, and more educated that average - this all correlates to lower accidents/fatalities regardless of FSD[1][2]. [1] https://pubmed.ncbi.nlm.nih.gov/24103823/ [2] https://personalinjurylawyersaustintx.com/blog/education-lev...

You are absolutely correct - Tesla's own report acknowledges this, but it also highlights how this safety stat is dwarfed when autopilot is active: https://tesla-cdn.thron.com/delivery/public/image/tesla/5f93...

Reference: https://www.tesla.com/VehicleSafetyReport

Re: Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy [video]

#88
post #69

A bit off topic but, the power of GPT (and DL in general) is in the data. Yet, we’ve allowed private enterprises to control what should be distinctly public goods. I don’t know where we took the wrong turn within the past decade but we desperately need to correct this mistake.

How can we really stop this though? I feel like Microsoft is doing what Microsoft does yet again. They piggybacked on the whole "open source" angle but this time, rather than be "software vendor lock-in closed source assholes", they're now being "IP theft open source" assholes.

It's not just Microsoft. Google probably has models at least on par with OpenAI.

Facebook probably too.

Re: Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy [video]

#89
post #6

Earlier quoted context omitted.

rough steps: 1. collect a very large dataset, see: https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla... . scrape, de-duplicate, clean, wrangle. this is a lot of work regardless of $. 2. get on a call with the sales teams of major cloud providers to procure a few thousands GPUs and enter into too long contracts. 3. "pretrain" a GPT. one common way to do this atm is to create your own exotic fork of Megatron…

You can skip to step 4 using something like GPT-J as far as I understand: https://github.com/kingoflolz/mesh-transformer-jax#links The pretrained model is already available.

GPT-J I think hasn't gone beyond 20B parameters, and while it is not the most obvious I think the original question is asking about the full 180B parameter+ kind of model. :) :thumbsup:

Re: Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy [video]

#90
post #86

Earlier quoted context omitted.

I would love to know what your thoughts are on how software engineering (and jobs in general) will change over the next 10 years and what we lowly developers can do to keep up & maybe even be involved in that change

Andrej wrote an interesting post some years back titled Software 2.0 about the direction he saw software engineering going. It's more about changes in software than the changes in the job market, but I suspect you'd still find it interesting. https://karpathy.medium.com/software-2-0-a64152b37c35

Thanks!
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