marshallp

If it reshifted to data science from hardware, it has the most cash of any tech company and could grow even larger. Steve Wozniak recently talked about smartphones having AI so this might be already in the works (focus on improving siri rather than stylishness).

why

what?

both

It's great if you're a kid, but when you grow up you should work on solving cancer or making spaceships.

Great, another a site with ruby on rail kids discussing shiny web design.

yeah, exactly

How about

- make your classifier get to 100% accuracy (or close enough).

Also, do it automatically

- use a unsupervised learning stage - then a simple a classifier (so you don't overfit). Repeat, growing the size of your unsupervised, followed by simple classifier, until you get 100%.

really interesting comment about life extension on reddit http://www.reddit.com/r/Futurology/comments/yswxw/what_techn...

The point no one asks is - why has a federated model such as rss been replaced with a single point of failure monopoly - twitter.

There is a way for doing same day, or even same hour shipping everywhere. Combine pre-ordering with gps.

A website where people can express they want to purchase something at a certain maximum delivery charge. As soon as enough people want to purchase things in a certain geographic area (based on a calculation of delivery route), they service kicks in - someone picks out all the goods (this would most likely be a grocery store) and then drives and delivers it on that route.

(I didn't downvote) His point is that economic growth in developed countries requires advances in technology (to get beyond the 0-4% gdp growth) while in developing countries copying can do (that's why china has 10% gdp growth). i.e. the average chinese can earn a living just by importing technologies (in the short run), while an american need to invent new technology to earn a living.

you can raise funds on kickstarter and get it done

?

Have you watched the video? Have studied the subject?

A combination of deep learning (as in the recent google brain) and reinforcement learning can create AGI according to Professor Itamar Arel. (singularity summit 2009 video)

The amazing thing about this algorithm is that it works for any data type (my other comment talks about speech and nlp).

With more computers and more data it will keep getting better - i've thought a lot about this and following this stuff for a couple of years - so it's not just some idle speculation. (I broke this recent story on hacker news a few days ago, and from here it got picked up by the new york times and spread everywhere else).

The algorithm they used did not exist before 2005. Also since 2005 this algorithm had been mostly trained using just 1 computer at time (usually with gpu's). The supercomputer setup is novel for this.

Most of the machine learning community had been disregarding this algorithm since 2005, until the past couple of years it started making dramatic showings, such as

- Microsoft Research speech recognition system in 2011 (done by one of geoff hinton's students interning there)

- NEC labs, ronan collobert - real-time natural languaege parser - first real-time parse ever - 2010 (trained on one computer for 3 months using wikipedia)

- Google Research 16,000 node billion connection network - december 2011

Your wrong about most conventional algorithms being able to get better results just by increasing image size.

First, theyre isn't a dataset suitable for only supervised learning (the final supervised stage they - imagenet - only has a relatively small amount of images of each category).

Second, higher resolution can make things worse in conventional supervised learning, you have to get more and more images to keep up with higher resolution (extra things in the image start confusing the algorithm, and even humans - more identifiable things in the photo mean you have to think more about what is the most important thing in the image).

The unsupervised algorithm gets around the both problems. There existed unsupervised/clustering algorithms before, but none that worked well until geoff hinton's breakthrough in 2005 (for which he deserves a turing award).

Face recognition usually uses a hand-coded layer followed by a machine learning algorithm. This technique automatically devises that hand-coded layer. It also did this for 20,000 other categories and can also be applied equally well to audio or any other data type. Huge difference.

The person who invented the boltzman machines - is - the inventor of this technique. He invented boltzman machines in the 80s and spent over 20 years trying to get them to actually work on difficult tasks.

Your rant about this not being compression or whatever you're trying to say is completely off the mark. You don't seem to understand what this work is about.

The netflix challenge is a supervised learning challenge. You have lots of 'labeled data'. This technique is about using 'unlabeled' data.

(Side note: At one point, Geoff Hinton and his group using this technique had the best result in the netflix challenge, but were beaten out by ensembles of algorithms.)

Cyc has nothing to do with this and is huge failure at AI.

tldr; You don't seem to be knowing what you're talking about after having reading your comments, and seem to readily discount the some of the most prominent machine learning researchers in the world today. You're obscuring important results that newcomers might have found interesting to follow up on.

I think you guys are getting a little serious.

Also, this paper is about 20,000 object categories, not just 1 (faces). And the neural network is not the standard type but of the deep learning variety which has only existed since 2005 (invented by geoff hinton, who was also big in neural net circles in the 80s so he's not some newcomer who hasn't done his literature search). One of the couthors of the paper is andrew ng, head of the stanford ai lab, so he's pretty legit.

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.

google app engine. set the request delay in admin console, architect for scalability with datastore (or use less scalable cloud sql). choice of python, go, java (and jvm languages like clojure).

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 work at larger and larger scale, this would be a machine learning technique that can work accurately on tasks that are hugely important to society

- accurate speech recognition - human level compuer vision (make human manual labor redundant)

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).

That's true. Let's say in machine learning then.

If the algorithm keeps increasing in accuracy as you scale up computation and add more unlabeled data that is pretty amazing. You might get something that matches human performance on vision/speech recognition etc.

They just have to scale it up - more computers, more days, and the accuracy level should increase accordingly (that is my intuition and hope on this, though i could be wrong).

It does this for 20,000 different objects categories - this is getting close to matching human visual ability (and there are huge societal implications if computer reach that standard).

This is the most powerful AI experiment yet conducted (publicly known).

It automatically creates a set features that you can then use a final layer of machine learning to get what you want.

In machine learning, normally you have to create a set of features (call feature engineering - basically think algorithms to better represent your data). The amazing thing about deep learning is that the computer does this for you!

You just need a few 10s/100s face/nonface images - same for 20,000 other objects - this is called fine-tuning.

For more, andrew ng, geoff hinton, yann lecun have given talks on this at google and they are up on youtube.