Inductive biases are not a new topic in ML. Also your examples have little to do with inductive biases as inductive biases are about generalization on out-of-sample data through human based restriction of the model hypothesis space.

This is total horseshit. The supporting evidence for this argument is a bunch recent public software failures that the author says 'must have been due to complexity' but then offers no proof. Sure it could have been due to complexity but it could have due to many other things such as mismanagement.

Then at the end the author says:

> Docker and Electron are the most hyped new technologies of the last five years. Both are not about improving things, figuring out complexity or reducing it.

That's not why these are 'hyped' technologies. These things solve real problems. Electron solves the problem of a cross platform GUI with permissive licensing where there are an abundance of cheaper frontend devs vs QT experts.

Sure, Johnathan Blow said we could just copy programs between computers in 1960s but that's because every computer in the lab was the same back then. But that isn't reality today. Today, if you want to ship some server applications to different business customers you'll find yourself having to maintain N different install scripts for the N different distros your customers have. Or you could simplify and use docker and have a consistent environment for your application.

> They do not need to know, exactly, how X is built, why it was built that way, or how to write an alternative X from scratch.

Why on earth would they? I want them to build Y not build me an X from scratch. But I assume they are competent engineers that can solve novel problems and they would research and build me an X if tasked to it. After all most software engineers got their engineering knowledge from trying to solve problems they were tasked with in their career, not in a classroom.

There is no intelligent discourse in this piece. The author could have looked at why these technologies were created and adopted, maybe have made the argument that their adoption is unwarranted due to X. Supported X with good examples and proof.

Those benchmarks are ridiculously out of date. See https://wrosinski.github.io/deep-learning-speed-vol1/ for updated benchmarks by the same person. PyTorch beats Tensorflow on every single model.

Still waiting for it to support basic, but crucial functionality like tolerations and node affinities. And no, using admission plugins like PodNodeSelector is not viable on managed clusters like GKE.

That's because octave is using doubles. You can do the exact same thing in PyTorch by passing in dtype=torch.float64 into torch.randn.

If I get the horse galloping in a circle its legs end up crossing and clipping through each other.

Yeah, we had to do retargeting of the original wolf skeleton to the skeleton of the dog model. That was kind of tricky, and introduced this front leg crossing artifact...

> It's looking at real life

Collecting observations aka data.

> deriving the mathematical laws that govern what you see

Fitting a model.

> Your neural network doesn't tell you what features make songs distinct

It literally learns better features that you could ever come up with by hand. This is why CNNs do better in computer vision that hand engineered filters.

> I guarantee you they'd do a better job, and their models would work on a commadore64, with real time training.

LOL if you think that a room full of people can listen to TBs of audio data, decide what mathematical functions when combined together are better descriptors of that data than a DL model learning its features.

You don't have the slightest clue what you're talking about.

Yeah neither of those metrics are by which we measure the effects of carcinogens and endocrine disruptors.

People lost their lives terribly, that much can be understood without watching it. What more do you really gain by watching the video? Do you have some morbid curiosity that you need to satisfy? Do you think we should disregard the feelings of the survivors or those who lost someone close? How do you think they feel about the video being shared? How would you? Do you think they deserve respect and solidarity? If so, how does sharing the video contribute to that?

If someone wants to watch it, regardless of the intent, they should be allowed to. Disrespect should not be illegal. That's silly.

That's not even an argument. So people should be allowed to watch child porn?

Plenty of parallels between the two. People are hurt in the filming of both. It's damaging to the survivors. It feeds into the fetishes of those who would watch such a thing (it's a snuff film). In the case of this film I could argue that potential perpetrators could study the film for their own plans.

In a forum discussion on the shooting, I found a link. I clicked the link, realized it's the right video from descriptions and asked myself 'WTF am I doing? If I keep watching, that's going to have consequences for life and stopped.'.

You'll find plate models, PGM junk, etc in modern papers on explicit density generative models and factorizing latents on such models.