Real time editing fun & AlphaGo

For quite some time now I’ve been wanting to edit in real time, not so much in vjay style looping around a set of memories and sequences stored in banks, but simply linear clean cuts with maybe some kistch manipulations. Finally after some geeking – as I also wanted to use only free software – I’m quite happy with results.

The edit itself was performed using all files in the folder of my last month’s recordings, chronologically; and recorded together with the soundtrack. The solely reason I imported the output into resolve to apply a LUT is because after several days compiling LittleCMS 2, creating an icc profile out of a LUT for mpv to load; I gave up on that idea. Anyway all manipulations and sound are real time. Enjoy if you can, list of project materials at the end 🙂

 

 
T R A C K L I S T
1 – Perfecting a Drop – by justleroy – ORIG
2 – Powerpuff Girls Theme! – The Warp Zone ft. Dodger – ORIG
3 – Song For You – Alexi Murdoch
4 – Saraba– Ifang Bondi

 

Merci a Pieta, VP, Lagoa, Miminhos e canitos sequiosos de festa

 
 

SOFTWARE USED

• First the heart of the operation, mpv , the best video (media) player I’ve ever used. FULL STOP
Cross platform open source project, fast, configurable and responsive beyond believe. Those strange paragraphs below required quiet a bit of study (mpv’s manual is 172 pages) and testing.
 

My general config file, with the settings that matter:

–quiet
–idle=once
–hwdec=vdpau
–vo=opengl-hq:scale=ewa_lanczossharp:cscale=ewa_lanczossoft:dscale=mitchell:
tscale=oversample:scale-antiring=0.8:cscale-antiring=0.9:dither-depth=auto:
scaler-resizes-only:sigmoid-upscaling:target-prim=bt.709:gamma=0.9338:
fancy-downscaling:temporal-dither:pbo:3dlut-size=256x256x256:icc-profile-auto
–no-border
–force-window
–loop-file=yes
–audio-pitch-correction=no
–af=scaletempo:speed=tempo:rubberband
–softvol=yes
–stop-playback-on-init-failure=no
–osd-scale=0.5
–osd-bar-align-y=0.8
–vf=expand=-2200:-1600:1100:800
 

then the input.conf file, basically keyboard triggers ;-):

 
/ seek 35 # map the left-arrow key to seeking forward by 15 seconds
u show-text "example" #
g saturation 100 ; gamma 100 ; brightness -43
G set saturation 0 ; set gamma 0 ; set brightness 0
a vf toggle "mirror" #
z vf toggle "flip" #
x set speed 0.10 #
c set speed 1 #
y set volume 33
u set volume 55
i set volume 71
o set volume 100
f hue -5050
F set hue 00
h add hue 50-50
j vf toggle "scale=1920:1080"
= add video-zoom 0.1
- add video-zoom -0.1
p set video-zoom 0
b set window-scale 0.25

 

audio

• The audio was played with QuicktimeX (comes installed with OSX) and I used Soundflower + Soundflowerbed sound driver to channel “system” audio into recorder – as monosnap couln’t capture it alone. For musicians or very complex midi / mixing I recommend Jack (audio connection kit) instead.

• Then Monosnap steadily recorded everything. You would not believe how many expensive software couldn’t do the job. Monosnap also has a windowed recording option which is exactly what I wanted to prevent further downscalling.

• I unfolded a stickie’s blue note (comes installed with OSX too) to show the tracklist and MonkeyBread’s FollowMouse to write on it.

• As I said before, I added a homebrew LUT (MAXR_v4_evol.cube) on Davinci Resolve but I also added a pinch of free emulation 4K stock grain from Jason Bowdach at Cinetic Studios, exported the whole shibang bang bang!!

• Massaged a lovely x264’s mp4 with the trusty old handbrake (hybrid is something to which I also turn quite often, more complex) and the settings you may find over this post from long time ago. So much work and youtube annihilated all those alluring characteristics. Lucky me I cheat with Vimeo too, which preserved the thing a little better; case you want to compare, here’s a link – https://youtu.be/x3iODYcwuY8
 

• Finally I wrote down all this lenga-lenga with macdown, a free, simple and nice markdown pad with an useful double window, Harropad (node-webkit) is similar and another good possibility.

 

To all developers of these very useful free software I say
BIG THANK YOU 😉

 
 
 
 

 
 
 
robots

 

As you may or may not know a couple years ago the evermore almighty google adquired the company responsible for the development of an artificial neural network, Deepmind. The company has developed a software which – wikipedia explains it well – learns how to play video games in a similar fashion to humans, as well as a neural network that may be able to access an external memory like a conventional Turing machine, resulting in a computer that appears to possibly mimic the short-term memory of the human brain. Being the foremost result a software capable of playing the Go game at the highest level named AlphaGo.

Many people still don’t realize the importance of this; in the wise words of fellow Personal View’s forumner Ironfilm

[…] Go is the only deterministic / perfect information game which computers could not beat. Checkers / Chess / Connect Four / etc had all been solved long beforehand. But they’d all been done with brute force methods. This simply can not be done with Go. The search space is too massive, as in seriously MASSIVE !!! Bigger than all the atoms in the universe, waaaaaaay bigger than that even.

When you’re talking about this degree of sheer massive scale, more computing power is simply not going to solve this problem any time soon. These past AI programs were using what is known as “narrow AI”, had very specific uses with a ‘lot’ of human domain knowledge programmed into it.

This however is the first instance of ‘general’ AI in a game beating the best human. As the old ways of AI with brute force simply would not work with Go, so they had to implement a very “human like” way of approaching playing the game of Go. Where the AI learned from observing past games and from the experiences of playing lots and lots of games of Go. Just like the way a human would! They gave zero domain knowledge to the AI program beforehand. (beyond simply the rule set).

This is astonishing! It now has a very human like approach to Go with intuition and a shallow search depth, like humans play Go ourselves. That intuition which AlphaGo has developed is simply otherwise impossible to program conventionally. AlphaGo is tackling problems which people had thought were IMPOSSIBLE to do! http://goo.gl/Ckel6D

Right now what we’re seeing is the true start of general AI which will be better at humans in EVERYTHING. AlphaGo is already more than just a Go program, it has learned to play other games too at high levels, again with ZERO specific domain knowledge programmed into it by humans. Just purely from watching and playing. Or in other words, learning! The same way humans do.
When in the future we’re living in The Matrix to feed power to our machine overlords and wondering where it all started… we’ll look back to this point in history right now!

Why this is not the headline item on every news channel I do not know… […] It is right up there with space flight and going to the moon.

Because I know you’re lazy, I’ll synthetize that for you: AlphaGo learns as humans do, from scratch, playing with others and furthermore playing with itself, it’s true. AlphaGo is light years ahead of such projects as Deep Blue and it’s beating korean grandmaster of Go game Lee Sedol 3 to 1

 

alpha-sedol-m3

 

As I also wrote in PV’s forum:

All three games were superb and actually fun to observe!!
I agree with Vitaliy and specially with Ironfilm ‘s wise words about the transcendence of the fact but – maybe ’cause I’m sort of an idealist – not so sure about The Matrix / Terminator projection.

As confy shoes’ Sergey from google put it go teaches more about life and so the program learns, imperfectly but steady in it’s method and grows exponentially and theoretically limitless… Left aside the more obvious argument of machine surpassing and taking over humans at a given task and screening for a possible future, at what point that growth would arise some kind of conscieness (it is said that a being would have to be aware of its limitations = understand what it is not, start suffering) and how (IF) the inevitable “will” to become free (autonomous) from humans would show (e.g. Ex-Machina and Her) is something we cannot predict at all.

Some years ago I met a guy who was outsourcing for google, he offered some interesting insights in an otherwise kind of crazy (sci-fi) project. It was enough that in my very limited capacities I got to understand the tremendous value of such an influental “instrument”, he kept talking about the algorithm, the recognition of patterns, the complexity in simplifying them so fervently it would seem he was drunk on divinity fumes. Some years later AlphaGo is born and at just two years old’s learning age, already whipping go masters’ ass. What next? Unlike in a go game, life cannot be contained, so if humans wants to play gods… we’ll see.
What’s beyond doubt is that this sets a solid milestone in AI’s childhood


BTW Lee Sedol way overtime just won 4th match with a genious centre board strategy and some silly moves from Alphago, who resigned (probability of wining went below a determined threshold) // you can WATCH the matches and commentaries HERE

 

gashô          

About m)◘(x

ni! for now
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