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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. |
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?