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The official trailer for the movie about Alan Turing called "The Imitation Game", starring Benedict Cumberbatch as Alan Turing and Keira Knightley as close friend and fellow code-breaker Joan Clarke. is now available on YouTube. This movie seems to be sticking much closer to the facts, unlike the previous movie Enigma, which totally wrote Turing out of the WWII story, replacing him with a traditional heterosexual male lead alongside Kate Winslet. The imitation Game is scheduled for release November 14.
from The Universal Machine http://universal-machine.blogspot.com/
Posted by David Silver and Demis Hassabis, Google DeepMind
Games are a great testing ground for developing smarter, more flexible algorithms that have the ability to tackle problems in ways similar to humans. Creating programs that are able to play games better than the best humans has a long history - the first classic game mastered by a computer was noughts and crosses (also known as tic-tac-toe) in 1952 as a PhD candidate’s project. Then fell checkers in 1994. Chess was tackled by Deep Blue in 1997. The success isn’t limited to board games, either - IBMs Watson won first place on Jeopardy in 2011, and in 2014 our own algorithms learned to play dozens of Atari games just from the raw pixel inputs.
But one game has thwarted A.I. research thus far: the ancient game of Go. Invented in China over 2500 years ago, Go is played by more than 40 million people worldwide. The rules are simple: players take turns to place black or white stones on a board, trying to capture the opponents stones or surround empty space to make points of territory. Confucius wrote about the game, and its aesthetic beauty elevated it to one of the four essential arts required of any true Chinese scholar. The game is played primarily through intuition and feel, and because of its subtlety and intellectual depth it has captured the human imagination for centuries.
But as simple as the rules are, Go is a game of profound complexity. The search space in Go is vast -- more than a googol times larger than chess (a number greater than there are atoms in the universe!). As a result, traditional “brute force” AI methods -- which construct a search tree over all possible sequences of moves -- don’t have a chance in Go. To date, computers have played Go only as well as amateurs. Experts predicted it would be at least another 10 years until a computer could beat one of the world’s elite group of Go professionals.
We saw this as an irresistible challenge! We started building a system, AlphaGo, described in a paper in Nature this week, that would overcome these barriers. The key to AlphaGo is reducing the enormous search space to something more manageable. To do this, it combines a state-of-the-art tree search with two deep neural networks, each of which contains many layers with millions of neuron-like connections. One neural network, the “policy network”, predicts the next move, and is used to narrow the search to consider only the moves most likely to lead to a win. The other neural network, the “value network”, is then used to reduce the depth of the search tree -- estimating the winner in each position in place of searching all the way to the end of the game.
AlphaGo’s search algorithm is much more human-like than previous approaches. For example, when Deep Blue played chess, it searched by brute force over thousands of times more positions than AlphaGo. Instead, AlphaGo looks ahead by playing out the remainder of the game in its imagination, many times over - a technique known as Monte-Carlo tree search. But unlike previous Monte-Carlo programs, AlphaGo uses deep neural networks to guide its search. During each simulated game, the policy network suggests intelligent moves to play, while the value network astutely evaluates the position that is reached. Finally, AlphaGo chooses the move that is most successful in simulation.
We first trained the policy network on 30 million moves from games played by human experts, until it could predict the human move 57% of the time (the previous record before AlphaGo was 44%). But our goal is to beat the best human players, not just mimic them. To do this, AlphaGo learned to discover new strategies for itself, by playing thousands of games between its neural networks, and gradually improving them using a trial-and-error process known as reinforcement learning. This approach led to much better policy networks, so strong in fact that the raw neural network (immediately, without any tree search at all) can defeat state-of-the-art Go programs that build enormous search trees.
These policy networks were in turn used to train the value networks, again by reinforcement learning from games of self-play. These value networks can evaluate any Go position and estimate the eventual winner - a problem so hard it was believed to be impossible.
Of course, all of this requires a huge amount of compute power, so we made extensive use of Google Cloud Platform, which enables researchers working on AI and Machine Learning to access elastic compute, storage and networking capacity on demand. In addition, new open source libraries for numerical computation using data flow graphs, such as TensorFlow, allow researchers to efficiently deploy the computation needed for deep learning algorithms across multiple CPUs or GPUs.
So how strong is AlphaGo? To answer this question, we played a tournament between AlphaGo and the best of the rest - the top Go programs at the forefront of A.I. research. Using a single machine, AlphaGo won all but one of its 500 games against these programs. In fact, AlphaGo even beat those programs after giving them 4 free moves headstart at the beginning of each game. A high-performance version of AlphaGo, distributed across many machines, was even stronger.
This figure from the Nature article shows the Elo rating and approximate rank of AlphaGo (both single machine and distributed versions), the European champion Fan Hui (a professional 2-dan), and the strongest other Go programs, evaluated over thousands of games. Pale pink bars show the performance of other programs when given a four move headstart.
It seemed that AlphaGo was ready for a greater challenge. So we invited the reigning 3-time European Go champion Fan Hui — an elite professional player who has devoted his life to Go since the age of 12 — to our London office for a challenge match. The match was played behind closed doors between October 5-9 last year. AlphaGo won by 5 games to 0 -- the first time a computer program has ever beaten a professional Go player. AlphaGo’s next challenge will be to play the top Go player in the world over the last decade, Lee Sedol. The match will take place this March in Seoul, South Korea. Lee Sedol is excited to take on the challenge saying, "I am privileged to be the one to play, but I am confident that I can win." It should prove to be a fascinating contest!
We are thrilled to have mastered Go and thus achieved one of the grand challenges of AI. However, the most significant aspect of all this for us is that AlphaGo isn’t just an ‘expert’ system built with hand-crafted rules, but instead uses general machine learning techniques to allow it to improve itself, just by watching and playing games. While games are the perfect platform for developing and testing AI algorithms quickly and efficiently, ultimately we want to apply these techniques to important real-world problems. Because the methods we have used are general purpose, our hope is that one day they could be extended to help us address some of society’s toughest and most pressing problems, from climate modelling to complex disease analysis.
How to use the Kinect (Microsoft Drivers v. 1.5) with Unity 3D
Here is my current result for fun (The beginning is my own C++ version that doesnt involve unity) in a game where you can throw fireballs:
After spending a while developing a little C++ library to use with the Microsoft Kinect Drivers (which Ive been meaning to release), I decided to try to integrate the Kinect functionality into the popular Unity 3D Game Development software.
I was pretty excited after finding the handy CMU package here. However, it only worked with the beta Microsoft SDK and didnt have some of the more advanced functionality that I needed. Im running version 1.5 hacked together a quick DLL and modified some code.
I use the Microsoft Kinect SDK Version 1.5 which I have available for download here.
The DLL source code can be found here in case you want to modify it for a different version.
The DLL itself (needed for the Kinect to work with Unity) can be found here and should be placed in your Microsoft SDK/Kinect folder like below: C:Program FilesMicrosoft SDKsKinectv1.5AssembliesUnity
The scripts to control a player and receive information from the Kinect can be found here. They are used the same way as the CMU ones but are slightly modified with a bit more error checking and some extra flags. All you have to do is follow the same instructions on the CMU page linked above which just involves enabling the Kinect and telling the KinectModelControllerV2 where your joints are. The movement flag is still in beta as Im trying to make it so the character can walk around.
I have a version that is entirely in C++ using OpenGL, FreeGlut, my own version of Blepo, and my own development libraries that is previewed in the beginning of the video that can be found here. It is what I do most of my testing in before I port it over to Unity.
This is an "off topic" post, but like many Ive become addicted to Game of Thrones and have spent some time trying to figure out how it may end and I think Ive cracked it. Im going to share my theory with you. Although it seems that Daenerys Targaryen wants to claim the 7 Kingdoms for herself she has shown that every time she comes across slaves she frees them and she abhors suffering and oppression. Her growing army fights for her as free men because they want to, not because they are her subjects. In the North the Wildlings are free, they bend the knee to no Lord. So Heres what I think will happen (note: I cant be precise on the detail but Im sure of the general theme). The surviving Starks, led by Bran, with magical Warg powers, will defeat the White Walkers. But to do this theyll need to team up with the Wildlings. I suspect Jon Snow will be instrumental in forming that allegiance. Meanwhile Daenerys and her army will attack the south. Eventually a combination of the two "free" armies will defeat the Lanister, Tyrel, Frey, Baratheon, et al Lords. The point you need to understand here is that this is a revolution. Free people are overturning the corrupt, decadent, self-serving, self-appointed nobility. Instrumental in this will be various characters of common birth who have no allegiance to a Lord, or who have come to believe their Lords are corrupt: Davos the Onion Knight (a smuggler made good), The Hound (Sandor Clegane), Gendry (the bastard of Robert Baratheon). I also think that Tyrion Lanister and Arya Stark have shown commitment to the oppressed. I dont know if any of these characters will survive but they will all fight for the revolution. The final ending will be at the Iron Throne, just as we think Daenerys Targaryen is about to claim the thrown of the 7 kingdoms she will command that one of her dragons (maybe all three if they still survive) melt it down with dragonfire. Shell then give an uplifting speech on how now all people are free. The End. The Game of Thrones will end with no throne. In support of my theory is the fact that the author George R.R. Martin was a conscientious-objector during the Vietnam War. He doesnt believe people should be forced to fight wars for the rich and powerful.
from The Universal Machine http://universal-machine.blogspot.com/
To save the image, first click to enlarge the image, then right-click on it and click on Save Image As.. or simply right-click on the thumbnail and click on Save Link AS.. Note: all are high resolution images greater than 1024 x 728 px.
From the visual side, this game appeared older with pixel art or broken images. Even so, the image is full color makes the game seem more exciting.
When I started playing, you will be given two options namely Qualifier and Nationals. However, to play a game at Nationals session, you must complete the first all race racing at the session Qualifier.
No different from how to play car racing games in general. After selecting a car, you can immediately sped from the beginning to the end line.
Theres nothing that will compete with your enemies. But, do not drive home because there are many obstacles along the way, ranging from ordinary cars to large trucks. Do not let your car hit by another car or truck because it will make you eliminated. But please note that there are some things that you do have to hit to get the bonus value such as money, gold, and nitrous oxide to supplement the cars speed. This car racing game has several levels. The higher the level, will be the more obstacles that must be avoided on the road.
