
My path to OpenAI - craigkerstiens
https://blog.gregbrockman.com/my-path-to-openai
======
YeGoblynQueenne
>>I then pulled out the Google Translate app from my pocket, put it in
airplane mode, and demonstrated how it translates the text under the camera
directly on the image.

So I had a look at the google reseach blog cited [1] and it turns out that
Deep Learning is not used to do translation in the Google Translate app. It's
only used to do Optical Character Recognition- OCR. Note, that's not
handwritten digit recognition, I haven't seen any claims that the Google
Translate app can do that, so it most probably can't, and can only recognise
print characters.

OCR Is not something you absolutely need a deep network to do, in fact it's
one of those cases were you really don't want to deploy such an expensive
system because there are far cheaper alternatives, like the logistic
regression mentioned in the article.

The google research blog typically doesn't say anything about how the actual
translation is done, but it seems to me, from playing around with the app a
bit, that it does word-for-word translation, possibly with some probabilistic
heuristic to figure out the most common/likely such translation. That's very
reasonable, given the app has to run on possibly weak hardware but it's also
not as marketable as "Machine Translation with Deep Learning on Google
Translate App".

So this is not a very good example of the superiority of Deep Learning. It's
more a good example of the superiority of the Google hype machine.

[1] [http://googleresearch.blogspot.co.uk/2015/07/how-google-
tran...](http://googleresearch.blogspot.co.uk/2015/07/how-google-translate-
squeezes-deep.html)

~~~
nl
Google Translate uses the CNN to extract the text from the image. That's much
harder than usual OCR.

 _It 's more a good example of the superiority of the Google hype machine._

That's pretty unfair. They managed to get this working on a mobile, which is
where the real work was done.

~~~
YeGoblynQueenne
I totally agree that it's a delightful engineering feat to get all that to run
on a smartphone and I'm suitably impressed actually.

The hype I'm concerned about is the one about Deep Learning. The article is
careless with the technology details and makes it sound as if Google is
running deep neural networks _for machine translation_ on peoples' _phones_ ,
which would be, at this time, bigger news than AlphaGo beating Lee Sedol.

~~~
nl
Running a DNN on a phone isn't super hard. _Training_ one is, but running is
pretty efficient.

They definitely could run a DNN for translation on phones. For example the
example seq2seq implementation in TensorFlow[1] would run on a phone fine, and
that achieves close to state-of-the-art.

Google uses a DNN for offline speech recognition.

[1]
[https://www.tensorflow.org/versions/r0.8/tutorials/seq2seq/i...](https://www.tensorflow.org/versions/r0.8/tutorials/seq2seq/index.html)

~~~
YeGoblynQueenne
For example the example seq2seq implementation in TensorFlow[1] would run on a
phone fine, and that achieves close to state-of-the-art.

Lol no it wouldn't and it doesn't. Either you would have to strip it down too
much to fit on a phone, so it would lose performance (and it's far from state-
of-the-art anyway, being an example) or it would not fit on your phone, unless
you removed everything else from it (and even then).

Just to put things into perspective: there are large swaths of the global
population that do not even own _smart_ phones. They need translation also,
you know.

~~~
nl
_no it wouldn 't and it doesn't. Either you would have to strip it down too
much to fit on a phone, so it would lose performance (and it's far from state-
of-the-art anyway, being an example) or it would not fit on your phone, unless
you removed everything else from it (and even then)._

Yes, it is an example program. Nevertheless, to quote: "Even if you want to
transform a sequence to a tree, for example to generate a parsing tree, the
same model as above can give state-of-the-art results, as demonstrated in
Vinyals & Kaiser et al., 2014"

Language modelling isn't the same as translation, but it is closely related.
Seq2seq as an approach does achieve close to state-of-the-art for
translation[1].

Why do you think it won't fit on your phone?

A related seq2seq model in TensorFlow comes in at less than 500kb[2]. The
(much more complex) Image Recognition demo is _designed to run on Android_
[3].

The new TensorFlow quantization approach[4] will reduce that size and increase
performance even more.

[1] [http://arxiv.org/pdf/1409.3215.pdf](http://arxiv.org/pdf/1409.3215.pdf)

[2]
[https://github.com/cmusphinx/g2p-seq2seq](https://github.com/cmusphinx/g2p-seq2seq)

[3]
[https://github.com/tensorflow/tensorflow/tree/master/tensorf...](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android)

[4] [https://petewarden.com/2016/05/03/how-to-quantize-neural-
net...](https://petewarden.com/2016/05/03/how-to-quantize-neural-networks-
with-tensorflow/)

~~~
YeGoblynQueenne
>> Yes, it is an example program.

Indeed. I guess I thought you were saying that this tutorial example could
achieve state of the art results. Apologies for the misunderstanding.

>> Why do you think it won't fit on your phone?

The question is really why do _you_ think it will. You claim it does. You need
to show that's true. Can you show me a trained machine translation system
using deep neural networks running on a smartphone and having state of the art
performance?

Until you do (or anyone does) I will remain skeptical. As I should and as
should everyone. You can't expect to just claim state of the art performance
and have everyone praise your achievement.

>> The (much more complex) Image Recognition demo is designed to run on
Android[3].

That's a demo. I guess it might be state of the art in demos?

Regardless, here's a hint of how practical it is to deploy this demo on users'
devices. This bit I quote from the demo's github:

 _The TensorFlow GraphDef that contains the model definition and weights is
not packaged in the repo because of its size._

So maybe it will _run_ on your phone but good luck sending that to your users.
Unless you want them at your castle gates with torches and pitchforks.

>> The new TensorFlow quantization approach[4] will reduce that size and
increase performance even more.

Great news. I'm at the edge of my seat.

------
studentrob
> Before I finalized my decision to leave, Patrick asked me to go talk to Sam
> Altman. He said Sam had a good outsider’s perspective, had seen lots of
> people in similar circumstances, and would probably have a good
> recommendation on what I should do.

> Within five minutes of talking to Sam, he told me I was definitely ready to
> leave. He said to let him know if he could be helpful in figuring out my
> next thing.

Wow. Probably not what Patrick had in mind.

~~~
pc
Actually, it was. (Structurally speaking.) Greg and I had had plenty of
conversations about what he was looking for and I asked Sam to share his
honest assessment with him. It's no good for Stripe to have a CTO who wants to
be something else :-).

~~~
studentrob
That's not movie making material =(

------
studentrob
Best of luck. Do you have a hypothesis? More experienced ML folks don't seem
to be on board [1] [2]. I haven't seen anyone posit how to build a machine
that can set its own goals.

I'm sure you'll build and learn something cool nonetheless

Another organization that tried to build AI was called Thinking Machines [3].
They were good at parallel programming. A few of them would later form "Ab
Initio Software" [4]. Their motto is "from first principles," which Elon likes
to say. Socrates did too.

My first job out of college trained us on that software. It's easy to use even
if you're not a programmer, which was impressive for parallel software in
2004. I still get job emails for having it on my resume. The company is
private and does not share its training resources. Boston mentality, I guess.
Despite being so closed, they appeared to be quite successful in 2004 and have
managed to stick around.

[1] [http://hunch.net/?p=3692542](http://hunch.net/?p=3692542)

[2]
[https://www.facebook.com/yann.lecun/posts/10153442884182143?...](https://www.facebook.com/yann.lecun/posts/10153442884182143?__mref=message_bubble)

[3] [https://www.technologyreview.com/s/406781/thinking-
machines/](https://www.technologyreview.com/s/406781/thinking-machines/)

[4] [http://www.abinitio.com/](http://www.abinitio.com/)

~~~
joe_the_user
The question of how to create general AI is fascinating but I don't think
OpenAI is actually aiming for anything like a leap to full general
intelligence at the moment. Rather, it seems like their goal is to follow the
same incremental path mainstream AI is following but without having to hide
it's methods.

 _" OpenAI is a non-profit artificial intelligence research company. Our goal
is to advance digital intelligence in the way that is most likely to benefit
humanity as a whole, unconstrained by a need to generate financial return._"

 _" In the short term, we're building on recent advances in AI research and
working towards the next set of breakthroughs."_

\-- Which is to say, ordinary progress, not extraordinary progress.

[https://openai.com/about/](https://openai.com/about/)

~~~
studentrob
Preface: I wish OpenAI the best of luck.

That said, the quoted verbiage is vague and different from what Altman and
Musk said in interviews [0].

> Altman: The organization is trying to develop a human positive AI

> Musk: There’s two schools of thought — do you want many AIs, or a small
> number of AIs? We think probably many is good. And to the degree that you
> can tie it to an extension of individual human will, that is also good.

OpenAI's about page is also different from what was written in the AI open
letters signed by Musk and presumably other backers of OpenAI last year,

> Artificial Intelligence (AI) technology has reached a point where the
> deployment of such systems is — practically if not legally — feasible within
> years, not decades, and the stakes are high: autonomous weapons have been
> described as the third revolution in warfare, after gunpowder and nuclear
> arms. [1]

> The key question for humanity today is whether to start a global AI arms
> race or to prevent it from starting ... We therefore believe that a military
> AI arms race would not be beneficial for humanity [1]

Then, in another letter,

> We recommend expanded research aimed at ensuring that increasingly capable
> AI systems are robust and beneficial: our AI systems must do what we want
> them to do

It sounds like Musk either thinks we are approaching AI, or he is misusing the
term AI. The AI letter signees and backers of OpenAI may benefit from more
discussion with experienced AI researchers such as those I linked above. What
Altman and Musk said about the founding goals of OpenAI is not grounded in
reality.

It's great to have more funding in this field.

[0] [https://backchannel.com/how-elon-musk-and-y-combinator-
plan-...](https://backchannel.com/how-elon-musk-and-y-combinator-plan-to-stop-
computers-from-taking-over-17e0e27dd02a#.g33qtn9ni)

[1] [http://futureoflife.org/open-letter-autonomous-
weapons/](http://futureoflife.org/open-letter-autonomous-weapons/)

[2] [http://futureoflife.org/ai-open-letter/](http://futureoflife.org/ai-open-
letter/)

~~~
studentrob
Plus, Greg himself writes in this blog post,

> However, there was one problem that I could imagine happily working on for
> the rest of my life: moving humanity to safe human-level AI. It’s hard to
> imagine anything more amazing and positively impactful than successfully
> creating AI, so long as it’s done in a good way.

------
refrigerator
Would be interested to hear about his role at Open AI - I would have thought
you'd need at least a PhD in Statistics/CS to be able to contribute to AI
research, no?

~~~
gdb
In the earliest days, I was doing everything required to make sure everyone
else could focus on research: recruiting, operations, spinning up the cluster,
taking out the garbage; really whatever needed to be done.

There's a surprising amount of traditional engineering work needed to make AI
systems happen. Over the past month or two, I've focused largely on launching
OpenAI Gym:
[https://twitter.com/gdb/status/726099677716713472](https://twitter.com/gdb/status/726099677716713472).

That being said, I'm planning on working on a research project next. As Ilya
likes to say, deep learning is a shallow field — it's actually relatively easy
to pick up and start making contributions.

~~~
throwaway6497
Greg, I don't know you. It is pretty incredible reading the blog post and what
you have signed up to do. Kudos to Sama for the vision, and all the amazing
people you were able to get together for the shared mission. Making the org
non-profit is the right model for this. It is all the more surprising given
that the whole VC model is to make profit. I am curious to see what you guys
come up with. All the best.

~~~
throwaway6497
This comment at best should be neutral. Why the down voting. Just curious.

------
kovek
Commenting about the footnote.

It seems like significant credit is given to the backpropagation method. I see
backpropagation as a core mechanism in how neural networks learn. Are there
other ideas present (or that could be explored) for a different core mechanism
for learning? The backpropagation algorithm makes sense, but I still feel like
there are other core mechanisms through which natural intelligence has
developped.

These natural mechanism might not be applicable to artificial intelligence,
but I think we could learn from them.

~~~
deepnet
Stochastic Gradient Descent is a core mechanism for generalising from many
examples in (non-biological) Neural Nets.

~~~
gliptic
Note that Stochastic Gradient Descent is not an alternative to
backpropagation. They are typically used together.

------
grey-area
This is an illuminating definition of deep learning from the end of the
article:

 _The goal of supervised deep learning is to solve almost any problem of the
form “map X to Y”. X can include images, speech, or text, and Y can include
categories or even sentences._

Interesting to think about the implications when such systems become
commonplace.

~~~
tlrobinson
I know what they mean, but you could say the goal of any program is to "map X
to Y" where X are the inputs and Y are the desired outputs :)

~~~
chubot
One thing I've heard talked about is to use deep learning to re-learn
arbitrary programs from their inputs and outputs... Then you can throw away
the program and just run the neural network.

I'm sort of a skeptic, so I think that would just make the programs massively
less efficient (usually), more incorrect, and harder to maintain, but I guess
it's a research direction. Maybe programmers need to be put out of a job :)

~~~
alphydan
> arbitrary programs

It would be really fun to see a web app built on those principles! User clicks
on /login/ link -> neural network {"hum ... I wonder what she's after ...
let's send her a login form"} -> response.

Trained on millions of MechanicalTurkers ... optimizing the function "which
user can you keep engaged the longest". :)

------
astazangasta
>It’s hard to imagine anything more amazing and positively impactful than
successfully creating AI, so long as it’s done in a good way.

Really, what? This is horseshit of the highest order. Why would you think this
is true? The vast majority of automation tasks don't require advanced AI. The
vast majority of human work can be removed without recourse to AI.

In addition, AI produces enormous problems, especially in the colossally
power-imbalanced society we live in. It's overwhelmingly likely that an AI
would be used first for surveillance and oppression; this is probably already
happening with the most sophisticated intelligences we have available to us.

Finally, there is a superabundance of "intelligence" available to us already,
in the form of human intelligence. These intelligences are already highly
capable of understanding and solving problems that an AI would take
generations to appreciate on human terms (if it ever could)

To me, the most positively impactful thing that we can do as human beings is
to enable the full application of human intelligence to solving human
problems, currently going to waste in most corners of the globe.

~~~
tangled_zans
Very wisely said.

People in AI try to replicate the "power of the human brain" when there are
billions of "human brains" that are surviving at a subsistence level or
working in mind-numbing menial tasks.

I can't understand the cognitive dissonance that makes a person marvel at how
"amazing the brain is" while simultaneously ignore the suffering of billions
of those brains.

~~~
tariqali34
>I can't understand the cognitive dissonance that makes a person marvel at how
"amazing the brain is" while simultaneously ignore the suffering of billions
of those brains.

I can.

Humans are good at coming up with brilliant ideas (such as, say, the concept
of translation). But they are absolutely poor at executing them effectively
and "at scale" (translating arbitrary works from one language to another). So
"AI advocates" can talk about how amazing the brain is in coming up with ideas
(as if ideas were all that were needed), but what they really want are
mechanical brutes that are able to execute those ideas quickly and
effectively.

At least some people hope that the proliferation of AI labor could mean a
reduction of human labor, potentially reducing the "suffering of billions of
those brains". This, of course, hinges on whether if the gains of productivity
can get redistributed fairly (or if they just accumulate to those who already
have capital). And then, there's all the social turmoil that occurs during the
transition phase: humans may not want to be obsolete, humans may actually like
working, AI accidentally becoming an 'exisitenal threat' due to human error,
etc. The brains will suffer more in the short-term, in the faint hope that
they will suffer less in the future.

EDIT: "The vast majority of automation tasks don't require advanced AI. The
vast majority of human work can be removed without recourse to AI."

I would argue that when you get to the point when we have automation tasks and
human work unnecessary, that we already have AI. We may never reach the stage
of Strong AI because it turns out that what we actually _do_ is not
intelligent enough to require Strong AI.

~~~
tangled_zans
I like your analysis, thanks.

> We may never reach the stage of Strong AI because it turns out that what we
> actually do is not intelligent enough to require Strong AI. That's
> interesting. If we find a rigorous enough definition of "Strong
> Intelligence", would humans necessarily qualify as one?

~~~
tariqali34
I'm not sure. That's why we need to come up with a good, rigorous definition
that doesn't elevates humanity, but is instead an objective, reasonable
definition of intelligence that humans can agree upon. I'm doubtful that we
can ever find that definition though.

Right now, humans consider intelligence to be "whatever machines haven't done
yet" (Tesler's Theorem), but as machine capabilities increase, then there is a
real possibility that humans may believe that intelligence doesn't exist at
all (after all, if machines can do everything, and if machines are not
intelligent, then nothing requires intelligence). [Source:
[https://plus.google.com/100656786406473859284/posts/Yp83aFwF...](https://plus.google.com/100656786406473859284/posts/Yp83aFwFJEr)]

I do think that intelligence does actually exist and that current AI can
already do intelligent things, but that the stuff that current AI can do won't
match my vague understanding of the term "strong". If current trends continue
indefinitely, then, of course, we won't ever have Strong AI, but we still have
machines that do everything. At least, that's one possible way of thinking
about intelligence.

But that's the thing, we don't have a good definition of intelligence at all
(and I don't have one either) so we don't really know what's going on. We
could invent Strong AI and never even recognize it, and maybe even dismiss it
because it doesn't resemble what we think of as intelligence (much less
"strong intelligence"). There's just so much that we don't know that talking
about it is very difficult. AI is not just a field where you get to write
pretty algorithms. It is also a philosophical field, and it is a shame that
the philosophical and the practical aspects of AI are disconnected.

~~~
astazangasta
What I think is the crucial missing component is: how does your intelligent
system define goals?

Right now goal-setting is something intelligences do not, and cannot, do for
themselves. Humans must define the bounds of a problem carefully before a
robot brain can perform useful work (some kind of numerical optimization).

The preliminary problem, then, is: how do humans define goals?

And the final problem: construct an intelligence that is able to efficiently
set and achieve goals that are broadly in line with human goals.

I think this statement of the problems neatly sums up my difficulty with the
notion of "strong AI", or "AGI", or Robot God or what-have-you and the
possibility that it might be somehow useful in the world.

Because the way humans set goals, I think, is through vague heuristics that
are represented as narratives carried by culture and society; we hold these
narratives and pass them back and forth to each other, through various tongues
and modes of fashion.

This means that human desire is the product of a constantly-shifting stream of
socialization, which we are all drinking from and pissing into at once. The
only meaningful way to accurately represent this, I think, is for engagement
in it. You must participate in culture to "get it". Where this participation
breaks down ("let them eat cake") we get strife.

Where does this leave the poor robot mind? It can only be "intelligent" in the
way that we want when it can appreciate the horror of losing its daughter to a
prison camp, when it can come to feel the memory of an inherited tragedy as
both burr and serious weight. At this point we're just raising children again.

At any other point it's simply a dumb slave, doing exactly what we tell it -
or a capricious, self-serving monster to be fought.

~~~
tariqali34
And since we don't know how humans decide their own goals (because knowing
that would be a very revolutionary discovery that would immediately be used in
a variety of other fields, including politics and advertising), we can never
really establish a road map to building "strong AI"/"AGI"/"Robot Gods" (or
even recognizing if we have built one by sheer accident). Clever. I like that.

There are probably ways to "cheat" your criteria though by having AI simulate
the idea of discovering goals and acting on them, such as building a bot that
searches Tweets on Twitter and then writing Tweets based on those Tweets it
discovers. But these are "cheats" and won't be universally accepted. We could
argue for instance that this bot really has a higher-level goal of finding new
goals and carrying them out, and is only coming up with "lower-level" goals
based on its initial "higher-level" goal. So, again, you're probably right. We
don't know how to have AI create goals on its own...we can only give it to
them.

I would say that "dumb slave[s]" or "capricious, self-serving monster[s]" are
still threats to worry about though. Just because robots do what we tell them
to doesn't mean that they will do what we _want_ them to. Bugs and edge cases
can exist in any exist system, and the more complex the system, the more
likely it is for those bugs and edge cases to slip by unnoticed. These
bugs/edge cases could lead to pretty catastrophic results. Managing complexity
when programming AI would be a good place for "AI Advocates" to focus on.

------
infyr
"Very few people today would have the audacity to explicitly try building
human-level AI."

Hmmm, there are a considerable number of people/groups who have this
audacity... Have for decades... and they have been explicitly trying with much
incremental success.

One thing such people speak about is ridding the space of outlandish buzz
words/promotions that mask the true nature of how things function. This 'hype'
creates barriers to contribution, learning, and progress.

Furthermore, the difficult efforts have been overshadowed by statistically
mapped input/output flow models currently being called "A.I".

There is no mystery w.r.t how deep learning/etc work I.M.O.

You have inputs 'X'. You take a known solution space 'Y' (supervised learning)
or you create an arbitrary one (unsupervised learning) 'Z'.

You break apart the input space and map it to nodes in a graph. You break
apart the output space and map it to nodes in a graph.

Input flows are decomposed into minimal component parts and recomposed into
higher orders of correlation. This is then compared (via flow restricted
weighting) to increasing orders of the output space.

How does this magical 'piece apart and and piece back together' process work
during active flows? It works based on guided encoding of 'importance' weights
on the partial information represented by individual nodes in the flow graph
network. Thus why under/over fitting can occur if you have too many/too little
nodes.

How are the weights codified? By encoding the partial derivative (partial
contribution) a node has w.r.t to the accuracy of the solution ... Error
function (Desired - Actual). Curve fitting.

It's essentially distributed brute force statistical gradient descent which is
why you have to beat on it, tune it, anneal it, and cram hoards of data
through for it for it to yield anything of value. "throw enough dirt and it
will stick"

Frankly, there is nothing to understand ... NNs/etc are distributed optimizers
guided by partial objective information. The resultant network/weights are a
spaghetti jumble of 'whatever gets the right output out the other end'.

You're throwing a bunch of 'agents' at a solution space and having them
gradually combine their results to a final solution. This was previously known
as constraint optimization before it got the silicon valley treatment of
buzzwords :

Distributed constraint optimization...
[http://mirlab.org/jang/matlab/toolbox/machineLearning/image/...](http://mirlab.org/jang/matlab/toolbox/machineLearning/image/gdDemo01.png)

This is not A.I. I don't feel anyone who has a grain of integrity ever thought
it was.

It's very slimmed down version of cortical Algorithms with lots of missing
pieces at best.

Strong A.I is being developed far from such thinking and is a totally
different animal. People who work in this space are necessarily guarded and un
open as there is a lack of appreciation, value, and funding for their
'audacious' efforts.

Of course, once more solid systems are developed, I'm sure you'll hear from
them again in the form of 'blackbox' capability presentations.

Currently, the spotlight and money are being thrown at PHDs and names as no
one has a clear understanding of what they're looking for. Namely because no
one wants to spend the time/money on defining that. People are moreso
interested in getting products/results out the door.

".... lets get the best minds, throw them in a room, throw money at them and
hopefully a solution will come about" Seems very similar to distributed brute
forcing of a problem space with a made up objective function...."throw enough
dirt and it will stick"

Most of the time should be spent on defining what were after ... The method is
:
[https://en.wikipedia.org/wiki/Philosophy_of_science](https://en.wikipedia.org/wiki/Philosophy_of_science)
not cramming mathematical formulas and PHDs into white-papers.

There are a lack of generalist being brought into these A.I labs and efforts
as they are perceived to have little value. Yet, its the 'generality' and
'fuzzy' stuff that underlies our very intelligence. From general to specific
or specific to general...

So, the industry wants to brute force this w/ money/PHDs/Buzzwords/industry
names...

The more complex and disjoint a problem space is, the harder it becomes to
brute force....

Time will tell. All roads eventually lead to Rome. Though, some will take
considerably longer.

~~~
Houshalter
>There is no mystery w.r.t how deep learning/etc work I.M.O.

Good, there shouldn't be. Being mysterious doesn't make something better, and
simplicity is desirable.

>Frankly, there is nothing to understand ... NNs/etc are distributed
optimizers guided by partial objective information. The resultant
network/weights are a spaghetti jumble of 'whatever gets the right output out
the other end'.

Basically yes. But that's not only incredibly effective, it's quite possibly
how real brains work too. A lot of people do believe it is a path to AGI.

>You're throwing a bunch of 'agents' at a solution space and having them
gradually combine their results to a final solution. This was previously known
as constraint optimization before it got the silicon valley treatment of
buzzwords :

That's not an accurate description at all, there are no "agents". In fact your
whole description of NNs sounds off.

And backprop wasn't invented or named in silicon valley. In fact it's been
around since the 80's. But whatever.

>Strong A.I is being developed far from such thinking and is a totally
different animal. People who work in this space are necessarily guarded and un
open as there is a lack of appreciation, value, and funding for their
'audacious' efforts.

Every "AGI" project is a bunch of pseudoscience. They have no idea how to
build an intelligence. They have no idea how the brain works. They have no
results to show with their algorithms, they aren't beating benchmarks. The
theories are always vague and ad hoc and include a million special cases to
make their systems do anything.

~~~
infyr
Basically yes. But that's not only incredibly effective, it's quite possibly
how real brains work too. A lot of people do believe it is a path to AGI.

> It's only incredibly effective in a static world. The world is not static
> nor is the human brain. Nor is the interplay between a human brain and the
> world and A.I. It's a very disjoint, dynamic, and interdependent
> relationship with far more complexities than could ever be represented in a
> statistical flow map much less in the incomplete mathematics and statistics
> that underly them. I have no doubt that people believe they can make a
> statistical map of the world. It wont be the first nor will it be the last
> time people try to make an 'effective' one. The dynamics of the world will
> change and they will be invalid as they in no way are structured based on a
> true understanding of what's going on. Nor is there any awareness of what's
> going. Aren't the overly complex and flawed risk models that no one could
> explain what caused the crash in 2007/2008? You think the dynamics in the
> world are less or more? So, I say to people subscribed to this provenly
> flawed thinking : Good luck.

That's not an accurate description at all, there are no "agents". In fact your
whole description of NNs sounds off. And backprop wasn't invented or named in
silicon valley. In fact it's been around since the 80's.

> Agents/node.. Tomato/Tomato .. they are partial computation nodes receiving
> and instantiating fed back partial derivatives based on computing an error
> between expected/actual. Where's the intelligence? Hindsight is 20/20 ...
> You're brute forcing the partial elements that contribute to a desired
> answer by slamming a cheese grater (NN) in forward and reverse flow ...
> hoping the important stuff sticks somewhere. Don't try to make it seem any
> more complex than that. Curve fitting at its finest. Constraint
> optimization. Gradient Descent. Regression. statistics all packaged up with
> fancy buzzwords.

[https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Re...](https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/Regression_pic_assymetrique.gif/300px-
Regression_pic_assymetrique.gif)

The Backpropagation algorithm is used to find a local minimum of an error
function. Flashback to grad school where there were a laundry list of methods
in constraint optimization courses. There's nothing special about it. What is
special is the thinking behind it.

You rightfully stated, most of these methods were developed in the 70s' 80s'.
While people are off copying and instantiating the works of that time and
relabeling it with buzzwords, there is little attention being paid to the
thinking that yielded those methods. That's what matters .. the actual
intelligence and thinking.. not what pops out the other end.

With little focus/money being put into expanding upon the thinking of that
time period, those focused on it are not going to get any further than they ..
Even worse, you maintain no understanding as to why they did what they did.
Which is why no one can tell you how NN works. The intelligent people who
defined them are dead.

Every "AGI" project is a bunch of pseudoscience. They have no idea how to
build an intelligence. They have no idea how the brain works. They have no
results to show with their algorithms, they aren't beating benchmarks. The
theories are always vague and ad hoc and include a million special cases to
make their systems do anything.

> People in the weak A.I space may have the lay person fooled by slapping
> buzzwords and A.I on everything. However, anyone who has spent anytime doing
> grad work in this area before it took on fancy names knows better... It's
> distributed gradient descent. The objective function is broken down into
> partial forms and instantiated at the distributed computational points in
> the gradient descent flow path defined by a NN. You slam it in forward and
> reverse and eventually enough stuff gets jammed into the lines for future
> flows.

I recall something named Genetic algorithms/evolutionary programming that were
supposed to be the keys to the future...

So, Strong A.I ... AGI. I'm thinking those who have the best shot at it are
people who know how NNs work on down to the mathematics and statistics,
theory, philosophy and pseudoscience. Given this understanding, they have the
ability to formulate new math/statistics/computational models and frankly
whatever else it takes to represent a true form of intelligence.

I imagine they are working hard at doing that very thing in the shadows while
others busy themselves trying to beat cooked benchmarks and fight over coin
and the spotlight.

So, you go down your path and they will seemingly go down their path... But
don't for a second think they don't understand exactly where your path is
likely to lead you.. Many of them have gone down it and found nothing of value
at the end. I guess the new crop of individuals who have no understanding of
the thinking behind these algorithms they're copying/instantiating have to
take this journey for themselves.

Call AGI a bunch of pseudoscience and foolishness. I have no doubt a good
number of people will be praising and following it like religious zealots much
like the work of those 'crazies' from the 70s'/80s that everyone laughed at
but now can't wait to copy/relabel and call their own.

*Cheers and enjoy the journey

~~~
Houshalter
>It's only incredibly effective in a static world. The world is not static nor
is the human brain

Neural nets aren't static. And yes they aren't great at online learning yet,
but they are better than anything else and there is research into improving
that.

>Where's the intelligence?

I'm not claiming a purely feed forward NN is intelligent, on it's own. But I
do believe it could be extended and built upon to create one.

And just because an algorithm is simple, does not mean it's not intelligent.
There is zero proof that intelligence requires complex algorithms. It's just
all the simple ones we've tried haven't worked, yet.

>You're brute forcing the partial elements that contribute to a desired answer
by slamming a cheese grater (NN) in forward and reverse flow ... hoping the
important stuff sticks somewhere. Don't try to make it seem any more complex
than that. Curve fitting at its finest. Constraint optimization. Gradient
Descent. Regression. statistics all packaged up with fancy buzzwords.

Yes and it's super effective. What's your problem? Many, many intelligent
people have tried to come up with more effective algorithms. Besides minor
tweaks and variations, nothing has done better. But by all means, invent one
yourself if you can.

>Which is why no one can tell you how NN works. The intelligent people who
defined them are dead.

Almost anyone can tell you how an NN works these days. And that's simply not
true, many of the early researchers in NNs are now very respected and run
their own labs. They are far from dead, they are publishing more research than
ever.

>So, Strong A.I ... AGI. I'm thinking those who have the best shot at it are
people who know how NNs work on down to the mathematics and statistics,
theory, philosophy and pseudoscience. Given this understanding, they have the
ability to formulate new math/statistics/computational models and frankly
whatever else it takes to represent a true form of intelligence.

Oh I don't disagree. And I'm very familiar with how NNs work, I've even
written code for them from scratch. And I don't believe AGI will be _just_ a
big regular NN, there need to be more insights into how intelligence works.
But I believe NNs will be a big part of it.

~~~
giardini
It isn't mere intelligence that we seek but _human_ intelligence. NN research
will more likely than not culminate in something akin to, say, dog or horse
intelligence or features of intelligence share by all species, rather than the
(desired) human intelligence.

I see nothing in NN research that is quintessentially human (although there
may be circuitry that is unique to humans that has not yet been revealed, this
will most likely be uncovered by brain science rather than NN research IMO)
and so I believe NNs are not the right level of approach to AI.

------
brendyn
I'm also interested in getting into AI, as it is dream of mine to work on a
problem of gread importance. I'm not sure where to start though. I have seen
many Neural Network posts on HN but they end up taking me to some Github repo
full of code with no documentation at all. Is there some resource that starts
from zero and codes up a learning algorithm? Currently I'm reading through
some Juder Pearl and E. T. Jaynes books because I'm fascinated by the theory,
but I'm none of this show me how to get hands-on coding up an algorithm. If
anyone here could recommend something to help get this newbie's foot in the
door, I'd be grateful.

~~~
allenleein
1\. Tool: 31 Resources to Learn AI & Deep Learning, From Beginner to Advanced
[https://medium.com/humanizing-
technology/tool-31-resources-t...](https://medium.com/humanizing-
technology/tool-31-resources-to-learn-ai-deep-learning-from-beginner-to-
advanced-ed41b3fc1ae#.301fm9kdf)

2\. Getting Up to Speed on Deep Learning [https://medium.com/life-
learning/getting-up-to-speed-on-deep...](https://medium.com/life-
learning/getting-up-to-speed-on-deep-learning-20-resources-
efec21e0aaf9#.hgct30hbs)

3\. What are the best talks/lectures related to big data/algorithms/machine
learning? [https://www.quora.com/What-are-the-best-talks-lectures-
relat...](https://www.quora.com/What-are-the-best-talks-lectures-related-to-
big-data-algorithms-machine-learning)

Hope it help dude :)

------
YeGoblynQueenne
> Mapping images to categories, speech to text, text to categories, go boards
> to good moves, and the like, is extremely useful, and cannot be done as well
> with other methods.

 _[citation needed]_

~~~
nl
Which part do you need the citation for?

Images to Categories: [http://image-
net.org/challenges/LSVRC/2015/results](http://image-
net.org/challenges/LSVRC/2015/results)

Speech to Text:
[https://en.wikipedia.org/wiki/Speech_recognition#Deep_Feedfo...](https://en.wikipedia.org/wiki/Speech_recognition#Deep_Feedforward_and_Recurrent_Neural_Networks)
gives a good set of citations of different benchmarks

text to categories: [https://papers.nips.cc/paper/5782-character-level-
convolutio...](https://papers.nips.cc/paper/5782-character-level-
convolutional-networks-for-text-classification.pdf) (Note that DL methods only
improve on manual feature engineering on the more difficult tasks here)

go boards to good moves: The recent DeepMind games?

------
deepnet
A Hinton inspired speculation about a Robot that plans & communicates.

I posit that general A.I. must be embodied to develop, it must learn from the
real world by interacting with it and learn by trial and error.

Deep learning is a huge leap for AI. It learns best from raw data. Babies
learn motor control & input interpretation first[1]. Moravec's paradox
proposes learning these basics are the hard foundation that higher levels of
reason build on.

Abeel and Levine's work[2] where robots learn robust functions to map from
pixels to motor torques for domestic tasks show solutions built on features
learnt from raw data are more resilient than hand-engineered ones. These
trajectories through motor-torque space to produce a desired goal are
intriguingly like planning.

Mikolov's[3] Word2vec & Radford's Image Vectors[4] produce semantic spaces
where vector math is akin to reasoning. Higher level control could be achieved
through vector algebras deriving actions and things directly from the robots
internal vector space of motor-torques and raw-pixels

Thus internal vectors are like thoughts[5] which can interpret, reason, plan,
and control.

Karpathy shows internal vectors can bridge modalities from words to
pictures[6]. If the higher level control vectors of actions and things could
translate to the modality of language as verbs and nouns, perhaps the robot
could discuss its plans, and receive orders.

[1] - Thelen's Infant Development
[http://www.ncbi.nlm.nih.gov/pubmed/17107442](http://www.ncbi.nlm.nih.gov/pubmed/17107442)

[2] - Levine, Finn, Darrel, Abeel
[http://arxiv.org/abs/1504.00702](http://arxiv.org/abs/1504.00702) &

[2b] - Distributed Grasp Learning
[http://googleresearch.blogspot.co.uk/2016/03/deep-
learning-f...](http://googleresearch.blogspot.co.uk/2016/03/deep-learning-for-
robots-learning-from.html)

[3] - Mikolov, Chen, Corrado, Dean
[http://arxiv.org/abs/1301.3781](http://arxiv.org/abs/1301.3781)

[4] - Radford, Metz, Chintala [https://github.com/Newmu/dcgan_code#arithmetic-
on-faces](https://github.com/Newmu/dcgan_code#arithmetic-on-faces)

[5] - Hinton
[https://youtu.be/IcOMKXAw5VA?t=32m55s](https://youtu.be/IcOMKXAw5VA?t=32m55s)

[6] - Karpathy
[https://youtu.be/ZkY7fAoaNcg?t=38m31s](https://youtu.be/ZkY7fAoaNcg?t=38m31s)

