Earlier quoted context omitted.
Disclaimer: I have vested interest in Deep Learning having built a distributed deep learning framework[1] and building a business around it. Deep Learning is actually worth the hype though. It has 2 main merits that are interesting. 1. Auto Trend Discovery 2. Plays very well with parallelism The main problem, which I'm hoping to fix, is feasibility and ease of use. Neural nets to the untrained eye can be a black box…
Please post that as a Show HN when you feel you're ready. (Email hn@ycombinator.com if you want clarification of what that means.) Edit: In case that seems foreboding at all, what I mean is: this looks really cool and will probably make an awesome Show HN, whenever you think it's ready for a post of its own.
Meet the algorithm that can learn “everything about anything”
71–80 of 81 posts
Re: Meet the algorithm that can learn “everything about anything”
#72One of the hallmarks of bad science is overly grand claims paired with aggressive marketing. Bad times are coming for AI again.
It's easy to demonstrate that this is a huge advance in AI. All these guys need to do is start entering Kaggle competitions and win, and win, and win, and win... That said, given that Google et al. are scraping every corner of the bottom of the barrel in search of advances in search, I think the good times for AI peeps will continue for some time...
Re: Meet the algorithm that can learn “everything about anything”
#73Here is the actual paper for those interested http://levan.cs.washington.edu/ngrams/objectNgrams_cvpr14.pd...
Summary of the paper for those who don't want to read it: So basically there are two categories of "learning" involved in this sort of research, supervised and unsupervised. In supervised learning, someone gives the computer a long list of concepts and their attributes ("frog", "green frog", "jumping frog") and a set of pictures to go with each item, and feeds them into a visual-recognition algorithm. In unsupervised…
I wonder if this has anything to do with the fact that we can jump too. That we can translate the frog's position into something we do as well.
of course one can argue that we can do the same for non-anthro-moprhic things as well. What i think is that we dont directly relate pictures, as the software is taught. What we do is translate that 2D picture into something we'd see in the 3d world. And that 3d "vision" isn't just another image. It represents an object in our world. something that has shape, existence etc. something which we can observe from other senses as well. For us a picture doesn't always represent an abstract thing, an arbitary pattern of colours. It usually represents something concrete. Something about which we have tons of other pieces of knowledge as well.
So we relate pictures by checking if they map to the same real-world object. And here that "object" is a sort of nexus of many pieces of information we have on it which is a product of many direct and indirect human experiences.
So i don't really think that we are in a position to teach a computer to do anything like that.
Re: Meet the algorithm that can learn “everything about anything”
#74Earlier quoted context omitted.
Which means it is illegal for a practitioner to read about this work, and it is best left ignored by the scientific and technical community
illegal? No.
I am not saying that i agree with him, i am just trying to clarify his point
Re: Meet the algorithm that can learn “everything about anything”
#75Earlier quoted context omitted.
Look up Thinking Machines :) My point is that mixing business goals and scientific truth is dangerous if not handled carefully. That said all the best with both your goals.
Sure! You're absolutely right. There are just a lot of myths about deep learning that I like clearing up, that being: it's a real world algorithm with actual merits, not just some marketing hype. Marketing hype and machine learning really does make things convoluted. That being said, what DOESN'T the press exaggerate? The track record for AI has definitely been over promise and under deliver. I think the hardware is…
I thought that this was an early, encouraging sign that the models being used were fundamentally valid. It's entirely consistent with the track record for natural intelligence.
Re: Meet the algorithm that can learn “everything about anything”
#76Earlier quoted context omitted.
Sure! You're absolutely right. There are just a lot of myths about deep learning that I like clearing up, that being: it's a real world algorithm with actual merits, not just some marketing hype. Marketing hype and machine learning really does make things convoluted. That being said, what DOESN'T the press exaggerate? The track record for AI has definitely been over promise and under deliver. I think the hardware is…
> The track record for AI has definitely been over promise and under deliver. I thought that this was an early, encouraging sign that the models being used were fundamentally valid. It's entirely consistent with the track record for natural intelligence.
Even the emergence of neural nets in the past few years has been due to hardware increases.
Re: Meet the algorithm that can learn “everything about anything”
#77Earlier quoted context omitted.
The fundamental problem humans will have with strong AI is that it won't be able to properly rationalize it's actions. Sure you'll have a statistical model for why it did what it did but that means about as much to anybody as a numerical reification of the quantum states of every particle of a person's mind at the time they made a decision.
When you say "rationalize its actions," are you referring to some mechanism in the inner workings of the brain/software? Or are you just talking about saying things that help humans understand why an action was chosen? I maintain that there is no distinction between the two, and it's quite conceivable that an AI could take an action then say something that helps humans understand why that action was chosen.
Re: Meet the algorithm that can learn “everything about anything”
#78Earlier quoted context omitted.
When you say "rationalize its actions," are you referring to some mechanism in the inner workings of the brain/software? Or are you just talking about saying things that help humans understand why an action was chosen? I maintain that there is no distinction between the two, and it's quite conceivable that an AI could take an action then say something that helps humans understand why that action was chosen.
If an AI can speak it can tell humans why it did what it did. But it's impossible to look through it's brain and see for yourself. To see if it's telling to truth or exaggerating or rationalizing.
Re: Meet the algorithm that can learn “everything about anything”
#79Earlier quoted context omitted.
If fictional media is anything to go by, the single defining aspect of human intelligence is love. This is the last bastion of human understanding that is incomprehensible to evil, machines and aliens.
Depends on your choice of fictional media; try reading Peter Watts, some time when you're already not in a good mood.
Re: Meet the algorithm that can learn “everything about anything”
#80Earlier quoted context omitted.
Depends on your choice of fictional media; try reading Peter Watts, some time when you're already not in a good mood.
Thanks, I'll place Blindsight by Peter Watts in my queue.