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SKIN LAUNCHPAD PRO FOR UNIPAD - FREE DOWNLOAD - Duration: 2:26.
Say, people, beauty? This is Siber and This time I'm bringing you one more.
UniPad video on the channel and I will introduce for you the new Skin that I created
And I'm leaving you the link in the description for who wants to download
You will be redirected to google play and here in google play just you
install the application. How can you see here in the photos this Skin simulates the
LaunchPad PRO. Has all the buttons the sides and now I'll show you how it was at UniPad
Just enter the application
Tap here + on the settings in select skin
And select the Skin you've downloaded
In that case, just apply and restart the application and there it is already
It will be applied and you can play any song with this new Skin
Texts taken from the edition
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Please making subtitle contribution to your country
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Please making subtitle contribution to your country
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Poetic Lace - SoulTherapy (Official Music Video) (@PoeticLace) - Duration: 3:51.
♫
Soul Therapy
♫
You can judge me, Yes I've done wrong,
Yes I messed up, but, I'm still God's child.
You can judge me,
Yes I've done wrong,
Yes I messed up, but, I'm still God's child.
Dang, sittin' in a cop car, I had a dream I was focused as a hoop star,
Now I got cuffs on my wrist like they bracelets, The pain on my momma face I can't take it,
But it took this to understand pain, The guy sittin' by me yea he's covered up in stains,
Tatted up, he slanged dope to maintain,
And his daddy wasn't there to teach him how to be a man,
Raised by the streets, taught by the boys, Molested at ten so he thinks he's Satan's toys,
He struggles with his sex while he tries to look tough,
He runs from his life so he drinks and smokes blunts,
Please tell him what to do like this is you,
He's lost in the lies cause he wasn't taught the truth,
Young man, I know your life is looking wild, But it's time to raise up cause you're still God's child.
You can judge me,
Yes I've done wrong,
Yes I messed up, but, I'm still God's child
You can judge me,
Yes I've done wrong,
Yes I messed up, but, I'm still God's child
Soul Therapy, Lord lead the way, It took a whole year to come up with this word play,
It took broken hearts for babe to learn to pray,
It's sad it takes pain to follow what God say,
Sixteen posting pictures for love,
All she wanted from her daddy was an intimate hug,
But she never got it, so she went to the thugs, But even they mistreat her body now she thinks like a rug,
"Laying down is what I'm good for"
Is what says after sex walking through the door,
Baby girl you're a daughter of the Most High God yea you're royalty,
I can hear Jesus Christ saying come to Me,
The point of this song, yea they did wrong, but,
Hold their hands let them know they belong,
In a place in our hearts where the love conquers all,
We are still God's kids even after we fall...
*Female singing* You can judge me *Male singing* You can judge me
*Female singing* I messed up *Male singing* I messed up
*Female singing* I've done wrong *Male singing* I've done wrong
*Female singing* But I am, I am, I am, I am still His child
*chorus repeated* You can judge me, Yes I've done wrong, Yes I messed up, But I'm still God's child
I am, God's child
But I'm still God's child
But I'm still God's child
I am, God's child
But I'm still God's child
♬ *harmonies*
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DeepMind Has A Superhuman Level Quake 3 AI Team - Duration: 5:13.
Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér.
After having a look at OpenAI's effort to master the DOTA 2 game, of course, we all
know that scientists at DeepMind are also hard at work on an AI that beats the Capture
The Flag game mode in Quake 3.
Quake III Arena is an iconic first person shooter game and Capture the Flag is a fun
game mode where each team tries to take the other team's flag and carry it to their own
base while protecting their own.
This game mode requires good aiming skills, map presence, reading the opponents well,
and tons of strategy.
A nightmare situation for any kind of AI.
Not only that, but in this version, the map changes from game to game, therefore the AI
has to learn general concepts and be able to pull them off in a variety of different
previously unseen conditions.
This doesn't seem to be within the realm of possibilities to pull off.
The minimaps here always show the location of the players, each are color coded to blue
or red to indicate their teams.
Much like humans, these AI agents learned by looking at the video output of the game
and have never been told anything about the game or what the rules are.
These scientists at DeepMind ran a tournament with 40 human players who were matched up
against these agents randomly, both as opponents and teammates.
In this tournament, a team of average human players had a win probability of 43%, where
a team of strong players won slightly more than half, 52% of their games.
And now hold on to your papers, because the agents were able to win 74% of their games.
So the difference between the average and strong human player's winrate is 9%, and the
difference between the strongest humans and the AI is more than twice that margin, 22%.
This is insanity.
And as you see, it barely matters what the size or the layout of the map is or how many
teammates there are, the AI's winrate is always remarkably high.
These agents showcase many humanlike behaviors such as staying at their own base to defend
it, camping within the opponent's base, or following teammates.
This builds on a new architecture by the name For The Win, FTW in short, good work folks.
Instead of training one agent, it uses a population of agents that train and evolve from each
other to make sure that a diverse set of playstyles are discovered.
This uses recurrent neural networks, these are neural network variants that are able
to learn and produce sequences of data.
Here, two of these are used, a fast and a slow one that operate on different timescales
but share a memory module.
This means that one of them has a very accurate look at the near past, and the other one has
a more coarse look, but can look back more into the past in return.
If these two work together correctly, decisions can be made that are both good locally, at
this point in time, and globally, to maximize the probability of winning the whole game.
This is really huge because this algorithm can perform long-term planning, which is one
of the key reasons why many difficult games and tasks still remain unsolved.
Well, as it seems now, not for long.
An additional challenge is that the game score is not necessarily subject to maximization
like in most games, but there is a mapping from the scores into an internal reward, which
means that the algorithm has to be able to predict its own progress towards winning.
And note that even though Quake 3 and Capture The Flag is an excellent way to demonstrate
the capabilities of this algorithm, this architecture can be generalized to other problems.
I am going to give you a few more tidbits that I have found super interesting, but before,
if you are enjoying this episode and would like to pick up some cool perks like early
access, deciding the topic of future episodes or getting your name listed in the video description
as a key supporter, why not support the show on Patreon?
With this, you also help us make better videos in the future.
You can find us at Patreon.com/TwoMinutePapers and we also support Bitcoin and other cryptocurrencies,
the addresses are available in the video description.
And now, onwards to the cool tidbits: - a human+agent team has been able to defeat
an agent+agent team 5% of the time, indicating that these AIs are able to coordinate and
play together with anyone they are given.
I get goosebumps from this.
Love it.
- The reaction time and accuracy of the agents is better than that of humans, but not nearly
perfect as many people would think.
However, they outclass humans even if we artificially reduce their accuracy and reaction times.
- In another experiment, two agents were paired up against two professional game tester humans
who could freely communicate and train against the same agents for 12 hours to see if they
can learn their patterns and force them to make mistakes.
Even with this, humans had only won 25% of these games.
Given the other numbers we have, it is very likely that this unfair advantage made no
difference whatsoever.
How about that.
If there are any more questions, make sure to have a look at the paper that describes
every possible tidbit you can possibly imagine.
Thanks for watching and for your generous support, and I'll see you next time!
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