Ask HN: Is deep learning obsession in college ill founded?

Spot on.

On top of that we have 'AI' models getting fooled over adversarial attacks which just involve a single pixel change. As long as these issues are not tackled or not researched well enough, then we'll be pretty much be heading into another AI Winter and the hype cycle will go through its trough of disillusionment phase. Being unable to inspect the black-box nature of such deep-learning systems is why highly regulated industries involving danger to life such as healthcare or other safety critical industries label deep-learning solutions as unsafe for them.

Sure, all you see right now are other students and startups 'applying' deep learning everywhere, but they are hardly advancing the field unlike DeepMind and OpenAI are. In terms of learning, it's something good to learn as a student at college, but creating a AI startup now requires using Google, Amazon or Microsofts data center's for training which is clearly not sustainable anyway.

Security related projects and research are always where it's at.

The vast majority of us are users. We massage the data to be in a certain shape, then feed it through a machine that someone else created. We can change the parameters. We can change the data. But few of us are going to look in to the code of a random forest function.

I've switched tracks and started doing web development. Playing with the hyper parameters in machine learning is no different than changing the feel of a drop down by changing the colors, fonts and other things to fit a certain aesthetic.

I could be wrong, but I have yet to meet anyone that has done anything besides use packages created by others to call themselves data scientists. I think that opens it up to becoming just another tool no different than Excel.

Neural networks performance on a problem is a benchmark of its real difficulty. It gives us insight, a new perspective.

In millennia of deliberations what have philosophers have discovered about the nature of intelligence? And then .. a neural net beats us at all board games, another can solve differential equations, another can translate, another can see, and so on. Have we really not learned anything by these inventions?

Another advantage of DL is that it frames the problem of intelligence in mathematical concepts and rigorous evaluation.

I, for one, have reconsidered all my spiritual beliefs after learning about the agent-environment-reward model of reinforcement learning. A new way of framing the agent and life, so parsimonious and powerful. And it does not require a soul, or a god, or anything outside out real environment, and yet can explain so much.

The whole machine learning paradigm is another powerful concept through which we can understand how we might function. Previously you might wonder how emotion, thought, sensation, imagination and will relate to each other. Now we can understand how they might be implemented and wired together, and what principles support their function.