
Machine Learning with TensorFlow Enabled Mobile Proof-Of-Purchase at Coca-Cola - zyngaro
https://developers.googleblog.com/2017/09/how-machine-learning-with-tensorflow.html
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tw1010
Man, software engineering at the cutting edge is getting harder and harder by
the day. Not only are you expected to master coding but also math heavy ML and
economics. I guess a consequence of software eating the world is that more and
more fields of human knowledge is folded into the engineering world. The
consequence being that engineers really aught to be very broad in their
reading if they want to take advantage of all the low hanging fruit the
octopus arms stumbles into.

~~~
samfisher83
I think software engineering is easier now than back in the day. I have heard
from people who used to have to write the code by hand change it to assembly
and then program their chip. Debugging was a pain. Everything was a pain. We
have also these crazy tools now. We don't need to know mnemonics.

~~~
pdkl95
While the _very_ early pre-assembly, "enter the boot code sizeof(WORD) bit
toggle switches at ta time on the front panel[1]" days were very difficult,
working with bare metal hardware could be _tedious_ , but usually no more
difficult than today's similarly-sized projects. Debugging was sometimes
easier with a logic analyzer watching values move across the CPU's data pins;
it became a _lot_ harder when you also have to worry about cache coherency,

> We don't need to know mnemonics.

Every specialized discipline has jargon and technical terms. Today everyone
memorizes Javascript frameworks instead.

> We have also these crazy tools now.

Yes, but we also have crazy tools that rapidly _create_ complexity. The
popularity of using tools we don't understand, didn't (directly) write, that
we cannot meaningfully inspect/audit (e.g. modern machine learning) is rapidly
adding unknown, interdependent complexity. This complexity is already[2]
spinning out of control. The only reason it _seems_ easier today due to most
of the problem being ignored.

[1]
[https://en.wikipedia.org/wiki/Front_panel#Booting](https://en.wikipedia.org/wiki/Front_panel#Booting)

[2]
[http://geer.tinho.net/geer.source.27iv17.txt](http://geer.tinho.net/geer.source.27iv17.txt)

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fredley
This is undoubtedly incredibly impressive as a feat of engineering. Detecting
codes on those sample images with >95% accuracy is no mean feat. No doubt
Google uses its muscle for many worthier things than this, and the learning
from this project will be applied to many more 'serious' problems. I think
what this blog post triggers for some people is just a further step down the
road from Google as it was - a tech first solutions later company that solved
interesting problems because they were interesting - to Google of today/the
future - a big corporation that acts pretty much like any other, but with a
few neat tools in its toolbox.

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vog
_> No doubt Google uses its muscle for many worthier things than this_

I hope this doesn't sound too negative, but since Google's main revenue is
ads, "worthier things" ultimately means "improved ad targeting", doesn't it?
In that case, it is not so different from the demonstrated application.

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corporateslave3
Pay no attention to the man behind the curtain...

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amelius
Judging from the comments section, Google needs to improve their ML for scam-
detection.

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jj12345
It's a hard problem. One person's scam is another person's business
opportunity. I, for one, want to learn more about this amazing spell-casting.

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candiodari
This is where the current state of the art in industry is in machine learning
: do "modern" things like scanning documents/codes/... on old processes
without modifying the processes.

Say you want to do the famous quality check in factories (is the dogfood box
closed properly ?), you can just do that with a convnet.

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rahimnathwani
Really nice.

tl;dr:

\- Customised text recognition of codes printed on goods

\- UX flow designed to (i) help users correct errors, and (ii) gather
additional labelled images

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davidkuhta
Also worth mentioning:

\- Fast: Needed a one-second average processing time (product-code iamge ->
OCR pipeline)

\- Accurate: Goal was to achieve 95% string recognition accuracy (with
improvement via active learning)

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adamzd
Why not just use QR codes and Google Vision API? Kinda seems like reinventing
the wheel here..

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flak48
Changing bottle printing machines / label designs across the world vs updating
an app

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Sujan
AI at Coca-Cola... and it actually makes sense. Nice!

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shp0ngle
Late stage capitalism

~~~
vog
Why "late stage"?

Nobody knows how long the current capitalism will stay alive. This might as
well be "early stage" ... in the sense of "one of the many experiments of
companies to find the best form of market segmentation".

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pennaMan
It's just a meme people like to spew when capitalism gives us some heavy
irony. For example a fossil fuel company pivoting to climate change themed
toys. It is irrelevant to the article at hand.

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JustFinishedBSG
Congrats google on finally inventing OCR on fixed fonts.

~~~
AndrewDucker
On curved surfaces, under varying lighting conditions.

If you want to show that standard OCR can cope with the images they have on
that page, then go for it. But I suspect you'll have issues.

~~~
nerdponx
Still good to have a healthy level of cynicism towards Deep Learning Hype
articles.

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tanilama
As useful as Jian Yang's hot dog detector.

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speps
Kind of ironic that 2 comments on that article are spam... Maybe spend some
time fixing that too.

