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TRANSCRIPT
hello, world
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Everything you need to know to survive the
upcoming robot apocalypse!
What is Artificial Intelligence?
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With artificial intelligence we are summoning the demon
Elon Musk CEO & CTO, SpaceX CEO, Tesla Motors Chairman, Solar CityDRAFT
The development of full artificial intelligence could spell the end of the human race
Stephen Hawking CH CBE FRS FRSA Director of Research, Center for Theoretical Cosmology, CambridgeDRAFT
1+1 = ?A. 0 B. 1 C. 2 D. None of the AboveDRAFT
1+1 = ?A. 0 B. 1 C. 2 D. None of the AboveDRAFT
2*2 = ?A. 2 B. 22 C. 4 D. A and BDRAFT
2*2 = ?A. 2 B. 22 C. 4 D. A and BDRAFT
Where is the maximum of this function?
A.
B.
C.
D.
x
y
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Where is the maximum of this function?
A.
B.
C.
D.
x
y
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x00
x01
x02
y00Σ φ
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x00
x02
x01
Σ φ y00
y01
Σ φ
Σ φ
Σ φ
Σ φ
Σ φ
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x00
x02
x01
Σ φ y00
y01
Σ φ
Σ φ
Σ φ
Σ φ
Σ φ
Feed-forward PassDRAFT
x00
x02
x01
Σ φ y00
y01
Σ φ
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Backpropagation PassDRAFT
CNNConvolutional
Neural Network
RNNRecurrent Neural
Network
L-STMLong Short-Term
Memory
Q
DQNDeep Q-Network
C
NTM
MR
W
Neural Turing Machine
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Going deeper with ConvolutionsGoogle used a new variant of convolutional neural network called “Inception” for classification, and for detection the R-CNN [5] was used. The results and the approach that Google’s team took are summarized here [2, 3]. Google’s team was able to train a much smaller neural network and obtained much better results compared to results obtained with convolutional neural networks in the previous year’s challenges.
1: Computers can recognize objects
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Show and Tell: A Neural Image Caption GeneratorAutomatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image.
2: Computers can write image captions
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Human-level control through deep reinforcement learningWe tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters.
3: Computers can play video games
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4: Computers can evolve better models
Mar I/O: Evolving Neural Networks through Augmenting TopologiesNeural networks can be combined with other machine learning techniques to solve complex problems like model selection. Mar I/O is a program made of neural networks and genetic algorithms that kicks butt at Super Mario World.
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• 2880 CUDA Cores
• 7.1 Billion Transistors
• 15 SMX units
• > 1 TFLOP FP64
• 1.5M L2 Cache
• 384-bit DDR5
GK110 / GTX Titan X, 980, …
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