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#41
Zaphod

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#42
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#43
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Google DeepMind could invent the next generation of AI by playing Starcraft 2

The announcement at BlizzCon 2016 that met with the most muted response was arguably the most revolutionary.
While new content for the likes of Hearthstone, Heroes of the Storm, Overwatch, and Diablo III drew appreciative roars from the Blizzard faithful, the news that Google’s DeepMind branch—which is dedicated to developing sophisticated Intelligence—would be teaming up with the makers of Starcraft 2 to further its research on AI elicited more of a murmur.
Perhaps the lack of enthusiasm was down to taste. After all, why would the plans of AI scientists be of interest to Starcraft 2 players? As it turns out, if the collaboration between DeepMind and Blizzard is what its developers hope it could be, players will see very tangible benefits—and so will many others outside the video game space.
 
AI has been an integral part of video games for quite some time now, and as Starcraft 2’s executive producer Chris Sigaty points out, the AI that DeepMind hopes to develop won’t be the first to play the Blizzard RTS. Currently the games uses a scripted AI. It's created by a team of designers and engineers that have an understanding of the way the game plays, with their expertise placed into a script and fed a list of conditions. For example, if X amount of resources is collected, the AI should spend the value of Y on Z units.
"They’re elaborate and interesting to play against; there’s some random choice and specific choices that happen," says Sigaty. "They can expand on their terrain, gather resources—basically do the things you need to do in Starcraft 2. But these [AI] are scripted. They’re predictable and ultimately, unless it cheats, there’s not an AI out there who can beat a human."


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#44
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Overcoming catastrophic forgetting in neural networks

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on the MNIST hand written digit dataset and by learning several Atari 2600 games sequentially.


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#45
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Google’s AI Can Read Your Retinas to Prevent Blindness 

IN BRIEF

  • Google DeepMind created an AI that matched or exceeded the performance of experts in identifying a common form of blindness and grading its severity.
  • Researchers trained the AI algorithm using a set of 128,000 retinal images, and tested its performance on 12,000 images.
RETINAL SCAN COMPLETE
Google DeepMind, the artificial intelligence (AI) research subsidiary of Alphabet, has had considerable applications in the field of medicine and medical research through DeepMind Health. One of its more recent achievements is an eye-scanning AI algorithm that can detect one of the most common forms of blindness.
This algorithm uses the same machine learning technique that Google uses to categorize millions of web images. It searches retinal images and detects signs of diabetic retinopathy — a condition that results from damaged eye blood vessels, and leads to gradual loss of sight — like a highly trained ophthalmologist.
According to computer scientists at Google, and medical researchers from the U.S. and India, the algorithm was originally developed to analyze retinal images and wasn’t explicitly designed to identify features that might indicate diabetic retinopathy. It learned this on its own, after having been exposed to thousands of healthy and diseased eyes.
The algorithm was exposed to 128,000 retinal images classified by at least three ophthalmologists as a training data set. Its was then tested on 12,000 retinal images, where it successfully identified the disease and how severe it was, matching or even exceeding the performance of experts. The results were published in the Journal of the American Medical Association — the first study in the journal ever published involving deep learning, according to editor-in-chief Howard Bauchner.

oct-image.width-1500.jpg
An OCT eye scan. Credits: Google’s DeepMind Health


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#46
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#47
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Google DeepMind is putting the entire source code for its training environment -- which it previously called Labyrinth and has now renamed as DeepMind Lab -- on the open-source depository GitHub, the company said Monday

Alphabet Inc.’s artificial intelligence division Google DeepMind is making the maze-like game platform it uses for many of its experiments available to other researchers and the general public.
DeepMind is putting the entire source code for its training environment -- which it previously called Labyrinth and has now renamed as DeepMind Lab -- on the open-source depository GitHub, the company said Monday. Anyone will be able to download the code and customize it to help train their own artificial intelligence systems. They will also be able to create new game levels for DeepMind Lab and upload these to GitHub.
The decision to make this AI test bed available to the public is further evidence of DeepMind’s decision to embrace more openness around its research. Last month, the company announced a partnership with Activision Blizzard Inc. to turn the popular video game Starcraft II into a testbed for any artificial intelligence researcher who wanted to try to create an AI system that could master the complex game.
Putting its Lab code on GitHub will allow other researchers to see if its developer’s own breakthroughs can be replicated and will allow these scientists to measure the performance of their own AI agents on the exact same tests DeepMind uses, one of the company’s co-founders, Shane Legg, said in an interview. "They can try to beat our results if they want," he said.


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#48
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Alphabet’s DeepMind Hiring U.S. Team to Work on Google Products

DeepMind, the London-based division of Alphabet Inc. that’s responsible for numerous recent breakthroughs in artificial intelligence, is hiring its first researcher in the U.S. to boost collaboration across the Atlantic.
The “applied research scientist position” described in a job posting on DeepMind’s website Thursday will be located at Google’s headquarters in Mountain View, California. It would be the first of “a couple dozen” the company hires, a spokesman said, as DeepMind builds "a small team" in the U.S. "to bridge the gap between Google and our team in London, helping us collaborate even more closely to bring our research breakthroughs to Google users around the world."
 
DeepMind has not previously had any researchers based outside of London.
“The Applied team in Mountain View will be made up of a mixture of software engineers and research scientists who work together to solve real-world problems at Google-scale,” according to the job listing.
The new hires will have their own offices at Google’s headquarters. Google’s other artificial intelligence research division, Google Brain, which uses machine learning to improve several Google products, also has offices on that campus. Both vie for the highly competitive field of artificial intelligence experts.


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#49
Raklian

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At first, I thought DeepMind itself did the hiring.

 

At that moment, I thought it was reasonably plausible given all the "miraculous" achievements DeepMind scored in the past few years.


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#50
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#51
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Someone Is Destroying Online Go, And Nobody Knows Who It Is

Right now, there's a player lurking in the depths of the online Go scene that is laying waste to some of the best players in the world. It's called Master, and nobody knows who it is.
The account is simply called "Master", and since the start of the new year it has made a habit out of trashing some of the world's best Go professionals. It's already beaten Ke Jie twice, who is currently the highest ranked Go player in the world. AlphaGo, incidentally, is #2.
Not that the ranking stopped him from being battered, mind you. A European professional Go player, Ali Jabarin, wrote on Facebook that Ke Jie was "a bit shocked ... just repeating 'it's too strong'". Jabarin wasn't sure whether the player was AlphaGo or not, but he was certain that an AI was behind the mystery account.
By January 3, the number of probably-but-we-can't-officially-say AI sanctioned beatings had risen to 41-zip. There's a few signs that it might not be an all-AI account, though. Jabarin received a polite message on New Year's declining a match, and a post appeared offering around $US14,000 to any professional player who could beat it.

So why I am posting this here? Why couldn't this just be some freak human prodigy? And even if it were AI, why would it necessarily be DeepMind?

Well...
Straight from Demis Hassabis himself: Mysterious new player that crushed pro players in online Go community is revealed to have been a new version of AlphaGo all along
C1VcLkyWEAEN3k4.jpg


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#52
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Update!

Someone Is Destroying Online Go, And Nobody Knows Who It Is

Right now, there's a player lurking in the depths of the online Go scene that is laying waste to some of the best players in the world. It's called Master, and nobody knows who it is.
The account is simply called "Master", and since the start of the new year it has made a habit out of trashing some of the world's best Go professionals. It's already beaten Ke Jie twice, who is currently the highest ranked Go player in the world. AlphaGo, incidentally, is #2.
Not that the ranking stopped him from being battered, mind you. A European professional Go player, Ali Jabarin, wrote on Facebook that Ke Jie was "a bit shocked ... just repeating 'it's too strong'". Jabarin wasn't sure whether the player was AlphaGo or not, but he was certain that an AI was behind the mystery account.
By January 3, the number of probably-but-we-can't-officially-say AI sanctioned beatings had risen to 41-zip. There's a few signs that it might not be an all-AI account, though. Jabarin received a polite message on New Year's declining a match, and a post appeared offering around $US14,000 to any professional player who could beat it.

Straight from Demis Hassabis himself: Mysterious new player that crushed pro players in online Go community is revealed to have been a new version of AlphaGo all along
C1VcLkyWEAEN3k4.jpg

Now, AlphaGo's not even playing in the same league as Ke Jie or Lee Sedol. It's literally playing on another level entirely. It's so impossibly strong, even Ke Jie can't take its onslaught.


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#53
Jakob

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Now, AlphaGo's not even playing in the same league as Ke Jie or Lee Sedol. It's literally playing on another level entirely. It's so impossibly strong, even Ke Jie can't take its onslaught.

Once neural augmentation starts taking off, we'll begin to catch up. Go has a finite level of complexity, one can only get so good at it.


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#54
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I was thinking the same thing. At this point, the only way to meet AlphaGo will be through transhumanist neural augments that allow us to think infinitely more quickly, like an AI even. As I said several times before, our fastest neurotransmitter signals only move at around 200 kilometers per second and that's only for a limited range of neural activity at that, whereas computers' upper limit is literally the speed of light for all processes.


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#55
Casey

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Can I Like this twice? That story is so awesome.



#56
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DeepMind secretly unleashed its Go-playing AI online, and it beat some of the world's best players 50-0

The creator of a mysterious artificial intelligence (AI) that has beaten some of the world's best human players at the Chinese board game Go over the last few days has been revealed as Google DeepMind.
Demis Hassabis, the cofounder and CEO of Google DeepMind, tweeted a screenshot of a DeepMind update that stated: "We've been hard at work improving AlphaGo, and over the past few days we've played some unofficial online games at fast time controls with our new prototype version, to check that it's working as well as we hoped.
"We thank everyone who played our accounts Magister (P) and Master (P) on the Tygem and FoxGo servers."
Hassabis's announcement came within hours of a Business Insider story reporting that people were keen to know who was behind the Master AI. 
"This whole thing is just really cool," wrote Reddit user Open_Thinker. "I hope we learn who the author(s) are soon."


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#57
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Remember Ke Jie's arrogant boasting from six months ago:  

AlphaGo, DeepMind's Go-playing software, played against Go grandmaster Lee Se-dol in March. Lee lost to the computer 4-1. Although Lee was the world's top-rated Go player between 2007 and 2011 and is widely considered to be one of the greatest Go players the world has ever seen, he was only ranked number four in the world when he played against AlphaGo.
 
For the last couple of years the world's best Go player (by Elo score) has been Ke Jie, an 18-year-old Chinese child prodigy. Back in March, after AlphaGo had won the first game of the match with Lee, Ke said that, “I don’t want to compete with AlphaGo because judging from its matches with Lee, AlphaGo is weaker than me. I don’t want AlphaGo to copy my style." He did some bragging on his Weibo account, too: “Even if AlphaGo can defeat Lee Se-dol, it can’t beat me."
Later, after AlphaGo had picked up three games in a row from Lee, Ke revised his stance a little: “AlphaGo was perfect and made no mistake. If the conditions are the same, it is highly likely that I can lose.”

SOURCE

And now he's screaming "It's too strong! It's too strong! It's too strong!"

 

God, makes me wonder what the fuck we're gonna do when AGI arrives.

gRXensb.jpg


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#58
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#59
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Oh, so AlphaGo won 3:0 against Ke Jie. Holy shit. 


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#60
Raklian

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The frightening aspect of all of this is that AlphaGo is only an infant (or perhaps merely a budding a few-week old fetus) in terms of reaching its full potential.


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Also tagged with one or more of these keywords: DeepMind, deep learning, deep reinforcement learning, progressive neural network, artificial intelligence, AGI, differentiable neural, Google, RankBrain, artificial neural networks

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