
Major advancements in Deep Learning in 2016 - sameoldstories
https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/
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drcode
The big question with all this stuff to me is whether we've just figured out a
couple of new tricks (primarily around neural nets processing 2D data and word
sequences for translation) and are now going to hit a new plateau in machine
learning- or whether "this time it's different" and we're going to similar
improvements year after year for the next decade or more...

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tshadley
> "this time it's different"

It may be different for other reasons but the main difference I note today is
the number of opensource AI/ML platforms that are trivial to install, use,
play, experiment at pretty much near the peak computing capacity of the
hardware we use today. Exploring the vast search space of reality has never
been easier and faster than today.

~~~
amelius
Perhaps we're on a plateau with good real-life applications then :)

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paulbaumgart
Probably true for the "internet scale" applications. But my impression is that
there's still lots of medium-sized opportunities to build interesting things
around the edges, in fields outside of the mobile/PC ecosystem.

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deepnotderp
This is literally just "whatever looked cool"... Where is alphago, neural arch
search through RL, learning to play in a day, wavenet, and pixelcnn/rnn?
That's just off the top of my head....

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habitue
Actually, I think alpha-go would be more in the "looked cool" category. It was
an applied engineering task, not so much a fundamental approach change like
adversarial networks. That's not to take away from the monumental nature of
the feat, but it seems like this blog post was more about higher level
developments. Similarly, pixelcnn and wavenet were "what can you do when
computing power is no object".

I would have liked to see something on RL^2 / "learning to reinforcement
learn" which do seem like huge developments to me, but maybe are too new to
see the impact of yet.

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gwern
I don't know. I see a lot of papers which are getting great results on tough
domains like program writing using the idea of tree search guided by NNs which
may be very old but AlphaGo brought to everyone's attention by demonstrating
what modern NNs could do in it. Any domain which is tree or DAG structured and
you have a simulator for can be tackled much more effectively now.

~~~
feral
I agree with this; I've seen people writing off AlphaGo as if it was very
little new conceptually over MCTS (but just with more hardware). But the power
of the NN was surprising.

There's a paragraph from the AlphoGo paper that I think speaks to this:

"We also tested against the strongest open-source Go program, Pachi, a
sophisticated Monte-Carlo search program, ranked at 2 amateur dan on KGS, that
executes 100,000 simulations per move. _Using no search at all_ , the RL
policy network won 85% of games against Pachi."

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d_burfoot
I can't understand why people keep saying "AI has been democratized", when all
the big research is being done by highly credentialed Phd scientists working
for rich American tech companies.

~~~
chronolitus
Today, I downloaded the model weights for a state-of-the-art R-CNN
architecture, ran some object recognition on some pictures. Took me 15 minutes
to install the dependencies (bunch of one-liners), modify the code a little,
and get it running on my data; all the internals are explained in the papers,
and can be modified in the scripts. Just saying, could be worse.

To be fair, I agree that the big papers mostly come from google & MS, and I
wish research was a real democracy.

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taneq
Go and do some cutting edge research, and publish it. Nobody's stopping you.

~~~
skeletonjelly
I imagine the need to pay bills is what's stopping most people

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ifdefdebug
> "In order to be able to have fluent conversations with machines, several
> issues need to be solved first: text understanding, question answering and
> machine translation."

The article makes it sound like "text understanding" was just around the
corner, maybe next year...

I doubt that because understanding (arbitrary but meaningful) text requires
real intelligence, and AI is far away from that.

And if it really happens one day, then our jobs are all gone. Because programs
are text, and with proper training programs are way easier to understand than
arbitrary prose - a much smaller subset of concepts contained in a clear
structure, instead of almost infinite concepts or even new ones, to be
structured however the author sees fit.

So prior to understanding language, AI should be able to understand
programming language, because programming language is just a a small subset of
language.

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Florin_Andrei
Seems like a matter of hierarchy. Current NNs are flat and tiny. We're only
simulating small chunks of the base levels. There is no executive level that
integrates from multiple minions. You need that hierarchy to even start
considering "meaning". What we're doing now is making bricks for that future
building.

Of course, then there's that other school of thought that predicates the true
grasp of meaning on the participation of consciousness. But that's another can
of worms for another time and/or another forum.

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Eridrus
Not all neural nets are flat, e.g. Recursive/Tree Neural Nets.

~~~
argonaut
Florin_Andrei is clearly including recursive/tree NNs when (s)he says current
NN's are flat (and I think depending on how you define "flat" I think this is
accurate).

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mmastrac
Has there been any advancement in an "AI executive" lately? ie, a layer that
sits above a number of other networks and drives it towards structured goal
seeking like reproduction, food, pain avoidance?

~~~
Florin_Andrei
I think when we finally start building hierarchies, when NNs "go meta", that's
when we'll start seeing truly mind-boggling results.

There can be no AGI without hierarchical, "meta" NNs.

~~~
gipp
By most definitions, people have been building hierarchical NNs of many
different kinds for years.

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rough-sea
No mention of the PixelCNN WaveNet VPN ByteNet line of research?

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visarga
And AlphaGo. I especially liked "Decoupled Neural Interfaces using Synthetic
Gradients" because it seemed to open up training of complex models on multiple
machines, but also because it provided an unexpected insight into how local
learning can be done.

~~~
gwern
I'm still waiting to see synthetic gradients get applied anywhere else. When
it came out, I thought it would at the very least revolutionize training of
RNNs by giving them better BPTT, but it seems to've sunk without a trace.

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state_less
Wow, amazing!

I like the idea of optimizing for efficiency too. Make the Neural nets scoring
function a little more meta, how well did you score and how much energy
(operations or neurons) were consumed.

Good for the folks at home with smaller systems and frees up resources for
more...what else, neural networks.

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turingbook
There is a similiar post(and several discussions) on Reddit:
[https://www.reddit.com/r/MachineLearning/comments/5gutxy/d_t...](https://www.reddit.com/r/MachineLearning/comments/5gutxy/d_the_most_relevant_advancements_in_deep_learning/)

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WhitneyLand
Self driving cars, beating Go masters, but still no one has solved Ms. Pacman?

It's not like they haven't tried. There are multiple research papers on it, AI
contests held for it, even Deepmind tried and failed.

I think the best score I have seen is around 40,000. The best human players
can get 800,000.

~~~
taneq
I think "tried and failed" is unfair unless they've actually announced they're
no longer trying.

~~~
WhitneyLand
Fair enough - but they have been working on video games for a few years now.

It seemed especially ironic that DeepMind is a media golden child and they
received a ton of press for beating video games. At the time they didn't hide
the fact that Ms. PacMan was not yet successful, but not one article mentioned
it.

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iainmerrick
Speaking as an interested techie without any direct experience with deep
learning techniques, this is a terrific overview. Thanks!

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ckcortright
Interesting how deep learning is evolving. Agreed that adversarial models are
a huge advancement; it will be interesting to see how they progress over the
next couple of years.

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sdenton4
Yeah, gan's are huge. Mimicry plays a huge part in human learning; especially
in language learning, where native fluency is more or less defined as
'difficult to distinguish from the real thing'. I expect we'll see a lot more
coming from adversarial models in the future.

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swframe
The CuriousAi TAG and LADDER research looks interesting. They claim same
accuracy with much fewer labeled data.

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ilaksh
What are the advancements in grounded deep learning for agents that integrates
multiple senses and abstraction across domains? Or did anyone even try to do
that?

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estrabd
Whoa, those images kind of weirded me out.

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bdamm
Indeed. Sort of a new form of computer generated art. Some of them I found
quite unsettling.

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treehau5
And people complained/are complaining about javascript fatigue / churn... ha!

