Artificial Intelligence (A.I.), Machine and Deep Learning...
Transcript of Artificial Intelligence (A.I.), Machine and Deep Learning...
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Artificial Intelligence (A.I.), Machine and Deep Learningpast, present and future aspectsBálint Gyires-Tóth
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About me
13/06/2018Bálint Gyires-Tóth, PhD ([email protected])
Bálint Gyires-Tóth, PhD
SmartLab @ BME TMIT
NVidia Deep Learning Institute Certified Instructor & University Ambassador
- Signal Processing
- Time series modeling
- Machine & deep learning since 2007
- Research & Development
- Education & Trainings
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13/06/2018Bálint Gyires-Tóth, PhD ([email protected]) 4/30
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AI: 4th generation of knowledge acquisition
1. Evolution
2. Tacit knowledge
3. Explicit knowledge
4. AI
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Challenge
Analitical and data driven thinking
Data driven thinking: Maslow’s hammer
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2014 – Deep Mind, ~500.000.000$ (https://deepmind.com)• Space Invaders
• Breakout
• AlphaGo
• AlphaGo Zero
2015 – OpenAI, ~1.000.000.000$ (https://openai.com)• Elon Musk (PayPal, SpaceX, Tesla Motors) + investors• Open source AI soulitions for mankind
2016 – Nervana – Intel fusion
2017 – Maluuba (Montreal, Canada) – Microsoft fusion
2018 – France – 1.850.000.000$ AI Research
To be continued…13/06/2018Bálint Gyires-Tóth, PhD ([email protected]) 10/30
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Deep learning based AI is the next big possibility for growth!
I works! Superhuman level…
~ Hearing ~ Vision
~ Speech ~ Knowledge?
13/06/2018Bálint Gyires-Tóth, PhD ([email protected]) 11/30
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The Holy Grail of data science is Deep Learning?
‘Deep learning is part of a broader family of machine learning methods based on learning data representations.’
In practice: deep neural networks
Whats new?
Algorithms + data + GPU
+ lower entry level
+ open-source research society (‘democratizing AI’)
Bálint Gyires-Tóth, PhD ([email protected]) 13/06/2018 12/30
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Feed forward neural networks
13/06/2018
f(∑): nonlinearn functions
e.g. f(∑)=max(0, ∑)
backpropagation
LEARNING: „fine-tuneing” weights
Bálint Gyires-Tóth, PhD ([email protected]) 13/30
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Basic layer types
• Fully Connected layers, FC• Classification and regression
• Recurrent layers (e.g. Long Short-Term Memory, LSTM)• Sequential and temporal data
• Convolutional layers (Convolutional Neural Net, CNN)• Feature extraction vs feature (representation) learning
• 1D, 2D and 3D convolution
• Initially for images and speech, nowadays for general purposes
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Hardver: GPU
NVidia GTX, Titan, Titan X, Xp, V• Titan X Pascal: 12 teraFlop (single precision)
• ~500..2800 EUR
NVidia DevBox: 40-50 TFlop, 15k EUR
NVidia DGX Station: 480 TFlop, 65k EUR
NVidia DGX-1: 960 TFlop, 220k EUR
Nvidia DGX-2: 2 PFlop (2000 TFlop), 560k EUR
3rd party vendors
Cloud services
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SoftwareTensorFlow (Google)
Keras (Fchollet @Google)
PyTorch, Torch7 (Facebook, Twitter, NVidia, stb.)
Theano (University of Montreal)
Nervana (Intel), Lasagne, Microsoft CNTK, Matlab Deep Learning Toolbox, stb
Caffe (University of California, Berkeley)
Kaldi
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ImageNet challenge
• ~1.200.000 images, ~1000 categories
• Goal: Top-5 accuracy
• AlexNet [A. Krizhevsky et al., 2012]
13/06/2018Bálint Gyires-Tóth, PhD ([email protected]) 18/30
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Top-5 accuracy 2010-2015
13/06/2018
https://devblogs.nvidia.com/parallelforall/mocha-jl-deep-learning-julia/
Bálint Gyires-Tóth, PhD ([email protected]) 20/30
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from urllib.request import urlretrieve
from keras.applications.inception_v3 import InceptionV3,preprocess_input,decode_predictions
from keras.preprocessing import image
import numpy as np
url_dog="https://pixabay.com/static/uploads/photo/2016/02/19/15/46/dog-1210559_960_720.jpg"
urlretrieve(url_dog, "dog.jpg")
img_path = 'dog.jpg'
img = image.load_img(img_path, target_size=(299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
model = InceptionV3(weights='imagenet', include_top=True)
preds = model.predict(x)
print('Predicted class:', decode_predictions(preds))
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from urllib.request import urlretrieve
from keras.applications.inception_v3 import InceptionV3,preprocess_input,decode_predictions
from keras.preprocessing import image
import numpy as np
url_dog="https://pixabay.com/static/uploads/photo/2016/02/19/15/46/dog-1210559_960_720.jpg"
urlretrieve(url_dog, "dog.jpg")
img_path = 'dog.jpg'
img = image.load_img(img_path, target_size=(299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
model = InceptionV3(weights='imagenet', include_top=True)
preds = model.predict(x)
print(‘Predicted class:', decode_predictions(preds))
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from urllib.request import urlretrieve
from keras.applications.inception_v3 import InceptionV3,preprocess_input,decode_predictions
from keras.preprocessing import image
import numpy as np
url_dog="https://pixabay.com/static/uploads/photo/2016/02/19/15/46/dog-1210559_960_720.jpg"
urlretrieve(url_dog, "dog.jpg")
img_path = 'dog.jpg'
img = image.load_img(img_path, target_size=(299, 299))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
model = InceptionV3(weights='imagenet', include_top=True)
preds = model.predict(x)
print('Predicted class:', decode_predictions(preds))
13/06/2018Bálint Gyires-Tóth, PhD ([email protected])
Predictions:[[('n02099712', 'Labrador_retriever', 0.5143939), ('n02099601', 'golden_retriever', 0.28467834), ('n02108551', 'tennis_ball ', 0.006718181), ('n02104029', Rhodesian_ridgeback', 0.0056073326), ('n04409515', kuvasz', 0.0041805333)]]
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Deep learning applications
• Images• Classification, object detection, segmentation
• Neural art, style transfer
• Sequential and temporal data• Speech recognition and synthesis
• Natural Language Processing (eg. text, intent classification)
• Telecommunication data (logs, traffic, anomalies, etc.)
• Medical data (EEG, ECG signals)
• Financial data
• Corporate Data• Customer analysis – e.g. fraud, churn prediction
• Risk modeling
• Marketing
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Some interestingDeep learning applications
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WaveNet: SOTA Text-To-Speech Engine
Inputs:
4k-8k previous samples (0.2-0.5 s) +
contextual features
Layers:
40-80 dilated convolutional layers
256 neurons in an FC layer
Output:
256 level μ-law quantized
raw waveform
(based on DeepMind’s WaveNet)
13/06/2018Bálint Gyires-Tóth, PhD ([email protected])
inputs
output
1d causal
CONV
1d dilated
CONV
1d 1x1
CONV
1d 1x1
CONV
1d 1x1
CONV
#trainable parameters: 1.2 million
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Modeling sensor data
Sensors• Gyroscope
• Orientation
• GPS, WiFi, etc.
Deep learning based classification• Activity
• Behaviour
• Body gestures
Applications• Emergency
• Activity analysis
• User and user group identification
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Sensor data: best model
13/06/2018Bálint Gyires-Tóth, PhD ([email protected])
INPUT:
200 x 7 sample points (1-2 seconds)
LAYERS (HYPEROPT):
16 residual blocks
filter (16+) and kernel size (3,5,7,9,11,…)
OUTPUT:
Binary classification
Over 99% accuracy.
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(Near) future…
High level frameworks.
Standardized workflows and model formats.
Easier and faster deployment.
More corporate / industrial applications.
Better tools for Deep Reinforcement Learning.
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