[DoingIsLearning]

As someone not in ML but curious about the field this is really interesting. Intuitively indeed it would be natural to aim for some sort of inspectable composition of models.

Is there specific tooling to inspect intermediate layers or will they be unintelligible for humans?

[wegfawefgawefg]

The unending quest for "Explainability" has yielded some tools but has been utterly overrun and outpaced by newer more complicated architectures and unfathomably large models. (Banks and insurance, finance etc really want explainability for auditing.)

The early layers in a vision model are sort of interpetable. They look like lines and dots and scratchy patterns being composited. You can see the exact same features in L1 and L2 biological neural networks in cats, monkeys, mice, etc. As you get deeper into the network the patterns become really abstract. For a human, the best you can do is render a pattern of inputs that maximizes a target internal neurons activation to see what it detects.

You can sort of see what they represent in vision. Dogs, fur, signs, face, happy, sad, etc, but once its a multimodal model and there is time and language involved it gets really difficult. And at that point you might as well just use the damn thing, or just ask it.

In finance, you cant tell what the fuck any of the feature detectors are. Its just very abstract.

As for tooling, a little bit of numpy and pytorch, dump some neurpn weights to a png, there you go. Download a small convnet pretrained network, amd i bet gpt4 can walk you through the process.

[DoingIsLearning]

Ok since we are at it, in your opinion:

Is it feasible for someone with a SWE background with fair amount of industry years to transition into ML without a deep dive into a PhD and publications to show?

I am considering following the fastAI course or perhaps other MOOC courses but I am not sure if any of this would be reasonably taken seriously within the field?

[wegfawefgawefg]

It is reasonable. If you have time and are willing to put in the effort I can forcefeed you resources, and review code and such. I've raised a few ML babies. Mooc are probably the wrong way to go. Thats where i started and I got stuck for a while. You really need to be knee deep in code, and a notebook.

As for getting jobs I cant help you with that part. You'll have to do your own networking, etc.

[e-mail] Shoot me an email if your serious lets schedule a call.

[DoingIsLearning]

Just emailed you. Thank you.

[jeremiahbuckley]

I asked a friend of mine @ google about what-next in ML the other day, and they recommended this post from a friend of theirs. I'm not sure I'd follow it end-to-end (like many things chatgpt it's an unknown 70-90% on target) but it's definitely identified some resources I didn't know about. https://www.linkedin.com/feed/update/urn:li:activity:7150542...

wegfawefgawefg - I bookmarked this and worked through it more carefully when I had time, I appreciated the learnings.