I'm building in robotics. Setting up a new 3d camera today. I found that the 10m active USB C cable that I bought transfers power in both directions, but only transfers data in one direction, it turns out to be some weird video USB variant. Next I needed to plug a gripper into a modbus controller. That uses an M8 8-pole 20cm cable. The controller manufacturer recently decided to switch from male to female connector, so now the cable needs to be male-male. After searching online for hours, I believe that is impossible to find as everyone only sells male-female cables.
I'm continuously surprised by how difficult it is to plug things together and how non-descriptive cable "standards" are about the actual capabilities of cables and connectors.
Is there anything like a linter to force you to stay within some subset of c++? I like the language, but it is hard to avoid language constructs that are outdated or enforce a single (or a few) ways of doing things. A c++ subset could be nice.
The goal of automation is not necessarily reduce personnel, it could be increased reliability, reduced risk to life, reducing waste from scraps, faster lead time, etc
Do you have some references for the pick and place and other reconfiguration things you mentioned. I've been out of this space for a while but last I checked these were still incredibly challenging things to get right.
No amount of fine-tuning can prevent models from doing anything. All it can do is reduce the likelihood of exploits happening, while also increasing the surprise factor when they inevitably do. This is a fundamental limitation.
I have a background in program analysis, but I'm less familiar with the kind of kernels you are optimising.
- Can you give some more insight on why 12 ops suffice for representing your input program?
- With such a small number of ops, isn't your search space full of repeat patterns? I understand the will to have no predefined heuristics, but it seems that learning some heuristics/patterns would massively help reduce the space.
This heavily depends on share classes and preferences. Surely the new investor wants better terms. The issue isn't so much dilution as a preference but added risk of never even getting a payout at all.
This is very cool. I'm wondering if some of the templates and switch statements would be nicer if there was an intermediate representation and a compiler-like architecture.
I'm also curious about how this compares to something like Jax.
I think perhaps this could be done in other ways that don't require interval arithmetic for autodiff, only that the gradient is conservatively computed, in other words carrying the numerical error from f into f'
I'm building a tool to make ml on tabular data (forecasting, imputation, etc) easier and more accessible. The goal is to go from zero to a basic working model in minutes, even if the initial model is not perfect, and then iteratively improve the model step by step, while continuously evaluating each step with metrics and comparisons to the previous model. So it's less ml foundation research, and more trying to package it in a user friendly way with a nice workflow, but if that's interesting feel free to reach out (email in profile).
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