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https://www.ukshipregister.co.uk/news/uk-ship-register-signs-its-first-unmanned-vessel/

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UK Ship Register signs its first unmanned vessel

Published: 13/11/2017

The UK Ship Register has signed its first ever unmanned vessel to the flag, showing how it is adapting to the changes of the maritime industry  

ASV’s C-Worker 7 will be used for work such as subsea positioning, surveying and environmental monitoring. It can be used under direct control, semi-manned or completely unmanned.

 

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RUSSIaANDCHINeSEKILLERROBOTSSETTORULETHEWORLDANDDOmINATETHEUS!!!!!!1!!1!!!!!

 

https://www.dailystar.co.uk/news/world-news/664332/russia-china-artificial-intelligence-superpower-vladimir-putin-pentagon


I for one welcome the chance to test my American metal again't pinko Soviet-ChiCom commie murder bots. Only then will we realize the folly of .556 poodle-shooter rounds. And the folly of 6.5/6.8mm intermediate rounds, all of which lack the power to stop these commie murder bots.

 

Long live the .30 caliber master race!

 

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https://www.artnome.com/news/2018/3/29/ai-art-just-got-awesome

When AI drink too much before painting

 

AI Generated Nude Portrait #1

Robbie Barrat AI Generated Nude Portrait #1

 

AI Generated Nude Portrait #2

Robbie Barrat AI Generated Nude Portrait #2

 

More in the link.

 

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   A paper just came out last December called Progressive Growing of GANs. A GAN is basically two neural networks that compete. There is the generator and the discriminator. The generator tries to make images to fool the discriminator, and the discriminator's whole job is to tell the difference between generated images and real images.

 

   So the discriminator is always comparing the images the generator sends it with pictures in the data set, and it is trying to return a value of "fake" or "real". The generator gets feedback from the discriminator on how well it is performing. It uses that feedback to adapt and to try and generate more and more realistic images that will fool the discriminator into saying "this is real".

 

   So I fed the GAN with 10,000 nude portraits and I let it have at it, and the two networks try to fool each other. And as they start off, they are terrible and their generations might as well just be noise. But as time goes on they get better and better at imitating what is inside the data set. 

 

   So what happened with the Nudes is the generator figured out a way to fool the discriminator without actually getting good at generating nude portraits. The discriminator is stupid enough that if I feed it these blobs, it can't figure out the difference between that and people. So the generator can just do that instead of generating realistic portraits, which is a harder job. It can fall into this local-minima where it isn't the ideal solution, but it works for the generator, and discriminator doesn't know any better so it gets stuck there. And that is what is happening in the nude portraits.

 

For comparison, previous works done by this AI system

AI Art Generated with Generative Adversarial Networks (GANs)

Robbie Barrat AI Art Generated with Generative Adversarial Networks (GANs)

 

Spoiler

Robbie Barrat AI Art Generated with Generative Adversarial Networks (GANs)

 

Robbie Barrat AI Art Generated with Generative Adversarial Networks (GANs)

 

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   I work at Nvidia, and they have absolutely insane GPU cluster supercomputers. It actually took me two weeks to train this on their supercomputers. I tried to do this with both the portraits and the landscapes a while ago, but I wasn't able to get it past 128 x 128 pixels' resolution. That's, like, horrifically small. But the fact that I have access to these supercomputers now and this paper that just came out in December on progressively growing of GANs really helps. It works by starting out with a really small GAN and it will grow in layers as the generator and the discriminator get better and better. That lets it generate super high-resolution stuff, but it takes super long to do it I don't think anybody outside of Nvidia has been able to train the model.  

 

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Anyone want to see AI fail at playing sonic? Of course you do.

 

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Retro Contest: Results

June 22, 2018

The first run of our Retro Contest — exploring the development of algorithms that can generalize from previous experience — is now complete. Though many approaches were tried, top results all came from tuning or extending existing algorithms such as PPO and Rainbow. There's a long way to go: top performance was 4,692 after training while the theoretical max is 10,000. These results provide validation that our Sonic benchmark is a good problem for the community to double down on: the winning solutions are general machine learning approaches rather than competition-specific hacks, suggesting that one can't cheat through this problem.

https://blog.openai.com/first-retro-contest-retrospective/

 

Results are here:

https://contest.openai.com/leaderboard

Just click on any of the scores, and it'll take you to the mp4 of the run. The best AI had a reward function that motivated it to travel as far right as possible, and it shows (AIs at the bottom of the board really struggle with this, so it was a good idea)

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