I'm seriously considering quitting my job and studying ML for a few months in a desperate attempt to get work in projects like this. I feel like I'm missing out but too dumb for traditional grad school.
Building high-level features using large scale unsupervised learning
41–50 of 196 posts
Re: Building high-level features using large scale unsupervised learning
#42Can we perhaps edit "singularity is near" out of the title? This sounds impressive, but having a bunch of racks able to classify the outline of a face is vastly disconnected from machine and humanity merging.
I put the singularity bit in to make it relevant for those who are non-technical. This experiment is significant because it shows that large artificial neural networks can be made to work. People have tried and failed at this for decades. This technigue was "discovered" by geoff hinton at the university of toronto in 2005. However, nobody at tried (or maybe got enough funds) to try it this scale. If this continues to…
"I put the singularity bit in to make it relevant for those who are non-technical." Yeah, I'm sure there's a lot of those on HN...I'd expect this kind of crap in something like Wired, but not here.
Re: Building high-level features using large scale unsupervised learning
#43People are missing the point here. Yes, 15% accuracy doesn't seem great. BUT the detector built its own categories(!). It managed to find 20,000 different categories of objects in Youtube videos, and one of these categories corresponded to human faces, and another to cats. Once the experimenters found the "face detection neuron" and used it to test faces THAT neuron managed 81.7% detection rate(!). Forget the singula…
> BUT the detector built its own categories(!).
It's not revolutionary. Clustering algorithms and neural nets are plenty.
Really, what differentiates this network is its scale.
Re: Building high-level features using large scale unsupervised learning
#44This isn't singularity material. While this may not be a bog standard neural network, it has no feedback. It cannot think, because thinking requires reflection. It is trained by adjusting the weights of the connections after the fact using an equation. Is it cool, and perhaps even useful? Yes. But don't confuse this research project for a precursor to skynet.
I put the singularity bit in to make it relevant to people who would otherwise not get the significance of this (which is that large scale neural nets can work - something people have been trying and failing at for decades).
Or you put it in as a cheap link-bait tactic.
Re: Building high-level features using large scale unsupervised learning
#45Is accuracy referring to recall, precision, or some other measure?
Re: Building high-level features using large scale unsupervised learning
#46Earlier quoted context omitted.
Wait until they become conscious and demand human rights. Then we can no longer exploit them and we're back to square one.
Seriously, a Phd thesis not far from now may have the title: "The limits of AI: how far can we exploit the machines before we are limited by machine rights"
And so is a profession of "machine rights lawyer". Not to mention "Corpsicle lawyer" for representing cryogenically suspended persons. Seriously.
Re: Building high-level features using large scale unsupervised learning
#47Earlier quoted context omitted.
Actually, what's significant about this work is that labeled training data was not required: "Contrary to what appears to be a widely-held intuition, our experimental results reveal that it is possible to train a face detector without having to label images as containing a face or not."
I replied by adding to my comment above as it wouldn't allow me to reply earlier. Reference that. tldr; High-level features generated by this unsupervised network are so high-level that one of them aligns with "has a face in the image", another to "has cat in image" (see the section "Cat and human body detectors") and so on. Note however that they select the "best neuron" for face classification -- the only way they…
But the difference is that you can show it 1 billion unclassified images, then show it 1000 images you know to be faces, analyzing how its neurons respond to the known inputs to use it to classify the rest of the images.
Strictly speaking, you do need to have some labeled data at the end in order to determine how the neural net views faces, but I think that obscures what's notable about this system.
The amount of human participation involved in training is potentially six or more orders of magnitude less. That's a breakthrough, and a change in kind, not just degree.
Re: Building high-level features using large scale unsupervised learning
#48Can we perhaps edit "singularity is near" out of the title? This sounds impressive, but having a bunch of racks able to classify the outline of a face is vastly disconnected from machine and humanity merging.
I put the singularity bit in to make it relevant for those who are non-technical. This experiment is significant because it shows that large artificial neural networks can be made to work. People have tried and failed at this for decades. This technigue was "discovered" by geoff hinton at the university of toronto in 2005. However, nobody at tried (or maybe got enough funds) to try it this scale. If this continues to…
Even so, the singularity bit is editorializing a link to a white paper on an equally significant scale. Nowhere in the link is the singularity referenced.
As for the point about it being for non-technical people, I don't understand where you're coming from. This is hacker news. If people don't understand it and don't upvote it, then that's their problem, not yours.
Re: Building high-level features using large scale unsupervised learning
#49Earlier quoted context omitted.
I put the singularity bit in to make it relevant to people who would otherwise not get the significance of this (which is that large scale neural nets can work - something people have been trying and failing at for decades).
Or you put it in as a cheap link-bait tactic.
The site is google research - I have no ads on it. This paper's been out for weeks but no mentions anywhere - thought i would give it a deserving push.
Re: Building high-level features using large scale unsupervised learning
#50I'm seriously considering quitting my job and studying ML for a few months in a desperate attempt to get work in projects like this. I feel like I'm missing out but too dumb for traditional grad school.
That's what I did a few months ago - quit my job and decided to go to a grad school to study AI (with focus on neural nets and ML).