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Comment by cscurmudgeon | Hacker News Reader

One of the hallmarks of bad science is overly grand claims paired with aggressive marketing. Bad times are coming for AI again.

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Nah, I think you're overreacting. It seems like every pop-sci article on CS research reads like this one (e.g. "breakthrough in cryptography", "scientists solve quantum computing", etc.).

Usually it's just weak journalism rather than researchers trying to overinflate.

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...

Most of the Kaggle competitions (if not all) do not allow any use of external data, as far as I know.

And it is

    [patent pending]

See http://levan.cs.washington.edu/?state=show_about

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 think what he is trying to say is that if you read about it, it would influence you. And you may end up building something similar (possibly remotely) to it. And then you can be sued for it.

I am not saying that i agree with him, i am just trying to clarify his point

Judging by the amount of "deep learning" submissions to HN bad times for AI are already here although maybe they never left.

In defense of the LEVAN thing, though, I didn't see any claims that this is science at all, more like an exploratory application illustrating an algorithm.

I think the increase in deep-learning links in this particular community is partly because Google recently dropped a huge pile of money on a deep-learning startup (Deep Mind), which gave it more visibility in the startup scene. Though in that case, are we talking about a separate AI frenzy, or is it just an appendage of the current startup/acquisitions frenzy?

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 that takes a really long time to train with little to no reward.

The hype isn't all for naught either. I'll elaborate if asked, but won't bore you guys otherwise.

The results coming out from different tasks are currently blowing away many of the old school algorithms in tasks like sentiment analysis, speech to text, object recognition, among others.

[1] http://deeplearning4j.org/

Wow, this is very cool! I take it your business model is basically consulting?

Yes. I'll have a cleaner website with that going up next week. I just got done running a 20 node cluster on amazon training a few different datasets, it hummed ;).

The overall idea is a support, services, training model.

Support: Install/Infrastructure

Services: Help with data etl/onboarding, tuning

Training: How to use deep learning, how to think about it, and how to run/setup clusters. This will be run through the machine learning academy I teach at[1]

[1]: http://zipfianacademy.com/

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 getting there where we can start making real progress though.

In this case, google is putting its money where its mouth is.

Thanks for the wishes, it's been a great ride so far. My overall goal with this is to bring deep learning to everyone else. I see real merits in auto discovery of trends to automate some of the worst parts of machine learning and would like others to see these benefits as well. Seeing cool apps built with it is another side goal of this as well.

> 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.

Yes for sure! Just like anything it has progressed over time. I think as computers get better we will be capable of doing more and more robust models.

Even the emergence of neural nets in the past few years has been due to hardware increases.

DL4J is fascinating! thanks for sharing, I'm definitely going to do some playing around with this framework.

Thanks! I will be publishing a comprehensive setup of an AWS cluster later this week. Here's the rough of it now: http://deeplearning4j.org/dl4jcluster.html

Still needs a bit of work in terms of linearity, but running it on a 20 node cluster was easy. Not going to benchmark it more than that right now, but it's really starting to come to life.

Seem to be getting some disagreeing with me. Could you elaborate as to why? I'm not pretending to be an all knowing person who can predict the future here. I'd especially love to hear from others in the field.

I think it's worth clarifying a few points here - this is a fairly naive view of deep learning being put forward.

In particular -

1) what does 'automatic trend discovery' mean? We've been able to do change-point detection, linear regression, etc. for hundreds of years. If you're talking about automatically learning a feature representation, then there are other algorithms that can do this, in a much simpler way. If you're arguing that it produces better representations, then make that argument.

2) This is almost _completely_ false, and indicates a substantial lack of experience of ML beyond deep learning. Other machine learning algorithms (SVMs, LR, even some decision tree algorithms) are _much_ easier to train in parallel - this is (partially) because your objective function has certain nice properties that allow you to combine partial solutions together that are produced in parallel (convexity, separability). When you're using gradient-based methods on an incredibly ugly non-convex function from a multi-layer neural network, you're in a completely different world.

Granted, there have been techniques coming out for training in multiple address spaces, but these are _hacks_ to get around the ugly structure of the problem, not the principled approaches that exist for other algorithms.

I don't have any perspective on your deeplearning4j library, but I'm skeptical of its utility given the existence of existing well-tested deep learning libraries written/contributed to by renowned experts in the field (e.g. cuda-convnet, caffe, torch). This stuff is a) very tricky to get right, b) very tricky to debug, and c) very performance sensitive. Just a quick pass through shows zero references to CUDA/GPGPU programming, so I'd suspect performance is going to be significantly worse than the aforementioned libraries.

1. In this case, we are talking about the pretrain part of neural nets. Automatic trend discovery comes down to doing feature extraction for the user. That being said, if I was inexperienced I wouldn't be teaching this stuff[1]. Am I the best machine learning practitioner out there? No. A lot of us aren't. I am all about making other people's jobs practical though.

Yes, I am talking about learning better representations. See hinton's deep autoencoder work as a prime example of this comparing PCA to RBM based methods for topic detection[2].

2. Google and people way smarter than I am seem to be doing just fine with this[3]. That being said, I didn't say that random forest (with whole companies built on this parallelism[3]) or any of the algorithms WEREN'T friendly. I would say one of the main appeals for deep learning is the scale of data with which it can benefit from.

Feel free to be skeptical all you want, if the researchers want to take the time to write a full stack distributed framework, I welcome others in to the game. The problem with the packages out there right now, (being matlab, python) are training times, and integrating in to an actual ecosystem. I'm addressing this this year at 2 different talks[5][6].

Replying to your last point, I use blas underneath for all of the matrix calculations, I will be adding GPUs later this year, and yes you're right,this stuff is hard to make. I also wouldn't be publicizing it if I wasn't already using it in production applications. Frankly right now though, I use cpu matrices right now, because I can fire this up on AWS (without the limit of GPU RAM), and it's practical for hadoop deployments. Honestly whether we like it or not, GPUs take a lot to get right. NVIDIA[7] and AMD[8] are going to make my job pretty easy though.

To end, if I was afraid of every little obstacle, why do anything in the first place? While you're hiding behind a throw away account, I'm actually trying to put this in the hands of people who don't have the time to learn every little thing about neural networks. At the end of the day, I follow the papers very closely and enjoy what I do. I also work on all sorts of different techniques for different problems combining different machine learning algorithms for different tasks (just like anyone else would). This framework is my way of getting this out to everyone else. If you have a deep learning framework, I'd love to see it, maybe I could learn a thing or 2.

[1]: http://zipfianacademy.com/

[2]: http://www.cs.toronto.edu/~fritz/absps/esann-deep-final.pdf

[3]: https://bigml.com/

[4]: http://static.googleusercontent.com/media/research.google.co...

[5]: http://hadoopsummit.org/san-jose/schedule/

[6]: http://www.oscon.com/oscon2014/public/schedule/detail/33709

[7]: http://www.jcuda.org/jcuda/jcublas/JCublas.html [8]: http://developer.amd.com/tools-and-sdks/heterogeneous-comput...

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.

Yes of course. Thank you!

FWIW, the paper is much more modest and honest than the gigaom article. It's certainly a hot topic and getting wide coverage, but most scientists are being fairly conservative about their claims.*

* Grant applications and future work sections excluded :)

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