The Evolution Revolution

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Elegant and graceful mathematics make a cool textbook cover, but the inside of those same books are usually dry cold engineering. It's important to mix the theory of innovation with the excitement of practicality, and through the composition of these elements we find innovation. In this talk, I'll show you from an engineering perspective how to explore, balance, and ultimately bottle machined success.

Robert Plummer
Robert Plummer
31 min
02 Jul, 2021

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Video Summary and Transcription

The Talk discusses the challenges of implementing software solutions and the need for abstractions. It emphasizes the importance of innovation and implementing once to avoid complexity. The use of Brain.js in machine learning research and its practical applications are highlighted. The talk also mentions the benefits of using JavaScript and GPU.js for graphics processing. Overall, the Talk encourages simplicity, efficiency, and collaboration in software development.

Available in Español

1. Introduction to Adam 2 and the Engineering Problem

Short description:

But first, a quick word about how you can start using React to write your own code. So here's a scenario. There's Adam, right, for machine learning for what do they call it? The trainer. You come up with this new idea trainer. It's called Adam 2. And it's revolutionary. And so you're like psyched about it. It represents an engineering problem. The implementations that we have to manage.

But first, a quick word about how you can start using React to write your own code. Probably the least qualified to be here. But that aside, I want to set up a scenario and we'll walk through it. And I want to propose something.

So here's a scenario. There's Adam, right, for machine learning for what do they call it? The trainer. There's various words for it. You come up with this new idea trainer. It's called Adam 2. And it's revolutionary. It's going to change everything with machine learning. And so you're like psyched about it. You want to get it implemented somewhere. And it's amazing because it represents innovation. And that's really what machine learning is kind of all about. We're innovating at breakneck speed.

And so we don't want things to hinder us in this arena. So here's our elegance and simple solution that represents Adam. And here's Adam 2. So, yeah, it's amazing. You're going to see it everywhere. But there's a problem even with just adding that one little number there. And that's this. It represents an engineering problem. The implementations that we have to manage. So, I personally write in Node. I'm in charge of a – or I'm rather the tech lead on the machine learning team. And for us to use this in Node, we would have to come up with translations in all these languages. And potentially more if we wanted it to execute in these environments. So, we've got JavaScript, WebGL, up-and-coming WebGPU, Wasm, native bindings, perhaps TPU, and even new languages.

2. Challenges of Implementation and Abstractions

Short description:

So, yeah, that's what you need to know to add that little two in there. And also, you don't want to forget about unit tests for each language because you want to make sure that your stuff stays together. Each implementation has various needs, and so they're going to need different abilities, different capabilities. And that problem compounds because of those bugs, you have to fix them. Each implementation that you add nearly squares the complexity of the original implementation. And all you wanna do is add it to. Abstractions take away our understanding of what we're trying to actually get at. It all slows innovation.

So, yeah, that's what you need to know to add that little two in there. And also, you don't want to forget about unit tests for each language because you want to make sure that your stuff stays together. And they don't tell you this when you're developing on the GPU or when you start getting really close to the hardware, that there are precision differences that have to be managed.

And that each implementation has various needs, and so they're going to need different abilities, different capabilities. And each one is going to eventually compound technical debt. That's just kind of how development goes. And that problem compounds because of those bugs, you have to fix them. The underlying languages are constantly being refined and changed, right? And they have to be managing to add that to that Atom function. You have to go into those languages and actually alter them. And then what if somebody creates Atom 3? What does that do to Atom 2? Did they extend Atom 2 with Atom 3 if they did? And they use some sort of class structure, or maybe they can't because they use functional, depending on the environment, and each one's different. And so that leads to diverged code, because each code is different. And each code being different has to be tested differently, and you have diverged management. Each one, because each language requires different strategies, different system to manage what you've implemented.

And then two, there's how you arrive at the math. That diverges. And so each implementation, think about this carefully. Each implementation that you add nearly squares the complexity of the original implementation. And all you wanna do is add it to. And I haven't even mentioned the worst one, which is abstractions. Abstractions take away our understanding of what we're trying to actually get at. The math, right? It makes it really hard to understand. And that's why we're so afraid of it. Like we talk about, you don't wanna have to worry about this underlying math, because if you do, you'll go nuts. There's so much to think about, because there's so many different levels of abstractions. And it all slows innovation. So our elegant and simple, Atom2, which you can see on YouTube. It's not really on YouTube, I made all that up. It doesn't stay elegant and simple, even though in theory, in the math, it doesn't stay that way. And so we have to kind of change our understanding of things. If we want to scale, right, and our elegant and our simple, our engineering, right? What was the main objective we wanna get out? Innovation.

QnA

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