In addition to Aezora's response, extrovert vs introvert being a description of your attitude to socializing is only a colloquial use of the term. I am a shy extrovert. I do not get social energy by being alone, like an introvert does, and I have problems talking with new people and even with friends prefer a back seat in the conversation.
Most people seem to fit into more clear buckets, if you believe the marketing, but that doesn't make the buckets the definition.
Which end? The main story is just a narrative device, in fact you shouldn't really obey the narrator at all. Calling any end "The End" doesn't make sense in the context of the game, really. Unless you just broke out of the mind control facility three times then called it quits? That end is supposed to be non enticing so that you try literally anything else before putting it down. I think the going insane end sticks with me the most. Although the game dev commentary in the recent release is fun.
Well drat. I suppose this post is going to fade away soon.
Just kitten.
True. Can we get it to 420 since we overshot 69?
I wanted to upvote this, but the score was too nice to change it...
A lot of drugs cause permanent problems when abused, and are still prescribed. Testing is needed to figure out if there's safe dosing and whatnot. Worse, safe dosage for one person may be incredibly unsafe for another, just like with depression meds which can permanently cause mental issues (in addition to depression) at normally prescribed and "safe" dosages. This is why honest discussions and ongoing check ins with your doctor is vital in any prescription change. Hell, penicillin almost killed my mom, and that's relatively safe unless you have an allergic reaction.
Definitely hard to test with drugs that offer non medical and very obvious side effects. Hopefully there is an interesting breakthrough in understanding mechanics so we can make safe PTSD helping meds, but something so drastically painful to the person having it may not have a safe cure because the systems that go haywire are so ingrained in the preservation systems of our brains.
Brains are weird. Any tampering is possibly dangerous.
Science is pushing the bounds of human knowledge. Science is only science if it propagates, otherwise it's just someone's discovery. Science has to be built upon, even if it's disproven, that means it was documented well enough to be built upon. That's not to say everything that's disproven is science, because crackpot theories don't often push the bounds of human knowledge.
I hope the brilliant students get their knowledge out there. (But that is unfortunately hard in academia. Despite us living in what should be a post knowledge scarcity society, we clearly aren't.)
This is why the machine learning community will go through ArXiv for pretty much everything. We value open and honest communication and abhor knowledge being locked down. This is why he views things this way. Because he's involved in a community that values real science.
ArXiv is free and all modern science should be open. There were reasons for publications in the past, since knowledge dissemination was hard, and they facilitated it. Now the publications just gatekeep.
This is a fair question. But also, we're talking about one of the most influential minds in deep learning. If anything he's selling himself short. He's definitely not first author on most of them, but I would give all my limbs to work in his lab.
Autoincorrect.
I've noticed a lot of things that are considered autistic in the states specifically may be normal practice in various cultures, having worked with people in Germany, and from a large swath of Asia.
It interests me a bit, but I think the takeaway is that autism tends to manifest in a number of quirks, and the ones that don't align with the current culture the autistic person is in are the ones that are paid attention to. That and there tends to be a bit more obsession over said quirks than in those cultures, sometimes to the detriment of the autistic person or their social life.
This actually is a symptom from the sort of "beneficial" overfit in Deep Learning. As someone whose research is in low data, long tails, and few shot learning, there's a few things that smaller networks did better in generalization, and one thing they particularly did better (without explicit training for it) is gauging uncertainty. This uncertainty is sometimes referred to as calibration. Calibrating deep networks can yield decent probabilities that can be used to show uncertainty.
There are other tricks for this. My favorite strategies prep the network for learning new things. Large margin training and the like are a good thing to look into. Having space in the output semantic space (the layer immediately before the output or earlier for encoder decoder style networks) allows for larger regions for distinct unknown values to be separated from the known ones, which helps inherently calibrate the network.