181–190 of 227 posts
Hey everyone this is OP, was a really nice surprise seeing my article generating so much discussion. I unfortunately don't think I'll have time to answer everyone but feel free to reply to this comment if you'd like to ask me anything. It seems like the article was a bit polarizing, some of the comments made me realize I made a few imprecise statements and I'll fix those. Other comments didn't approve of my tone and…
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That's unlikely. There are many machine learning applications that have been shown to be good enough for commercial use and they aren't going anywhere. The worst case for the field is that progress slows down, people realise that their expectations were unrealistic and the hype inevitably dies down. Which has to happen eventually. So even if ML isn't the hottest thing or a massively growing field, it will still be us…
> There are many machine learning applications that have been shown to be good enough for commercial use and they aren't going anywhere. If you could name three of them I'd be really grateful. Serious question; everything surrounding ML seems to be only good for (non-monetizable) art projects. As art it is amazing, not going to lie, but "commercial use" seems like a huge stretch.
Speech recognition + basic NLP for automatic customer support triage. None of these are great to use as a customer, but they seem to be effective enough to continue using and save companies lots of money.
Automatic "offensive" content detection for social media. I'd bet they use ML to do a first-pass on uploaded content to make sure it doesn't contain porn, gore, etc. Probably lets these companies save money.
Automatic defect detection in factories. Instead of training humans to detect subtle issues in manufacturing issues. I think companies like Samsara are experimenting with offering this tech as a service.
Facial recognition/tracking for law enforcement/defense. Ignoring the ethics of it for a moment, it seems like governments would be willing to pay a good amount of money for this tech. Could be used to automatically search through hours of footage to find which frames (if any) contain a target.
Hey everyone this is OP, was a really nice surprise seeing my article generating so much discussion. I unfortunately don't think I'll have time to answer everyone but feel free to reply to this comment if you'd like to ask me anything. It seems like the article was a bit polarizing, some of the comments made me realize I made a few imprecise statements and I'll fix those. Other comments didn't approve of my tone and…
Where did you find the memes?
The writing process for this article was meme first, content second
Is there a citation for this?
Much I would like it to be true, from the little I've read it seems that plenty of universities/colleges were originally religious in nature and were intended to defend orthodoxy, even to combat specific heresies. Definitely not to take intellectual risks.
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There isn't a scientific field where every single paper is groundbreaking. It's a Brownian motion of small incremental innovations, until eventually we stumble upon something big (like deep learning). In no way is machine learning unique in this. Except ... while machine learning is great, has made important and significant strides, it's not a yet science. It involves essentially a series of sophisticated, mathematic…
>ing is great, has made important and significant strides, it's not a yet science. It involves essentially a series of sophisticated, mathematically informed recipes for feeding data to giant algorithms and having them create something useful (maybe very useful but still). So... scientists need to do scienceing until it is. This is what happened with Biology over the last 50 years after 2000 years of pinning things o…
So I'd claim we're really at the situation right now, at the exploring new ideas, the "crisis of science" phase where essentially people have to start brainstorming (and not all ideas are good here either but they need to be somewhat original).
All this is using Thomas Kuhn's Structure Of Scientific Revolutions model very roughly.
Some good points in the article, but I disagree with the tone and the conclusion. > we’ve rewarded and lauded incremental researchers as innovators, increased their budgets so they can do even more incremental research There isn't a scientific field where every single paper is groundbreaking. It's a Brownian motion of small incremental innovations, until eventually we stumble upon something big (like deep learning).…
> Academics sacrifice material opportunity costs in exchange for intellectual freedom. Most academics I’ve come across only think they’re doing this. My perception is they are too insecure about their self-worth to pursue material opportunities. I admit, the number of academic types I know is not vast so maybe it’s too small a subset to make any judgments
I went a very uncommon route in my career: I started phd school after 14 years of industry. My motivation was I was tired of creating new and interesting things in industry only to have them be killed by politics. So, my idea never saw the light of day san a few dozen people. My thought was that if i have a phd, I am rewarded in my career for publishing these thoughts. If they are published, maybe some organization s…
This article is dead-on, but I think it is missing a fairly large segment of where ML is actually working well: anomaly detection and industrial defect detection. While I agree that everyone was shocked, myself included, when we saw how well SSD and YOLO worked, the last mile problem is stagnating. What I mean is: 7 years ago I wrote an image pipeline for a company using traditional AI methods. It was extremely chall…
> However, places where it isn't stagnating are things like vibration and anomaly detection. This is a case where https://github.com/YumaKoizumi/ToyADMOS-dataset really shines because it adds something that didn't exist before, and it doesn't have to be 100% perfect: anything is better than nothing. This is a link to a dataset, unless I'm missing something it's not about anomaly detection. I looked into this area a f…
Astronomy has entered a period of great stagnation. More and more grad students are investing huge amount of time and building larger and larger radio telescopes just to learn more about black hole formation and the properties of pulsars. Little consideration is given to how to more efficiently use inexpensive consumer telescopes purchased at Walmart! Where are the big discoveries? New planets in our solar system? A…
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>We've been in an exciting deep learning craze for a while, but it's silly to expect it to last forever. Back to the grind now. This was effectively my response to hardmaru when this topic came up on reddit [1] Basically 2010-2018 was an open field for ML/DL research with old(ish) methods being rapidly applied to low hanging fruit and large datasets with newly cheap compute. Deepmind and others are actually making ne…
Those other approaches you mentioned don’t get much love because they don’t work. Their advocates worked on them for many years and have nothing to show for it.