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How Will AI Change the Role of the Data Scientist?
1. How Will AI Change the Role of
the Data Scientist?
Hugo Gävert
@hgavert
Helsinki Data Science meet-up 2017-02-16
2. Who am I?
Currently:
Chief Data Scientist @ Sanoma
Past:
• HUT Infolab
• Xtract
• Nokia
Hugo Gävert, 2017-02-16
3. Artificial Intelligence
World Goals Use cases Examples
Special
purpose AI
Restricted, clear
inputs
Well defined,
finite
- Recommendation
engines,
- Credit scoring,
- Insurance claim
handling
- Image recognition
- Playing games;
chess, go, ping
pong, …
- Driving car
- GOFAI,
- ML,
- ANN / Deep
Learning
- Expert systems
- Supervised
- Unsupervised
- Reinforcement
General AI
Open, chaotic,
messy inputs
Poorly defined,
unconstrained
Requirements:
- Reasoning,
- communication,
- learning new
things
- ability to apply
skills to new
problems
- Design better AI
- Whole brain
simulation?
- Robotic form?
- Sensing?
- Manipulating the
world?
Super human intelligence?
Hugo Gävert, 2017-02-16
4. Artificial Super-Intelligence
Human
Intelligence
Artificial
Intelligence
Intelligence/Performance
Time
Games Expert tasks Mundane tasks
- Checkers, 1952 / 1994
- Backgammon, 1979
- Othello, Chess, 1997
- Jeopardy, 2010
- Go, 2016
- Poker, 2017
- Theorem proving, eq solving
- Credit scoring / probability
to default, insurance claim
fraud
- Medical diagnosis
- Speech to text, translation…
- Image recognition
- Natural language /
understanding text
- Walking
- Object manipulation
- Driving cars
Lieutenant Commander Data, year 2338?
Human Level
Machine Intelligence:
10%: 2020
50%: 2040-2050
90%: 2080-2100
Hugo Gävert, 2017-02-16
5. • Original ideas inspired by brains, but nowadays it’s more engineering for machine
learning tasks.
• Artificial Neural Network ≈ Layers of connected simple neurons
• Multiple different architectures for different uses
Neural Networks?
A cartoon drawing of a biological neuron (left) and its mathematical model (right).
Stanford CS231n: Convolutional Neural Networks for Visual Recognition
Hugo Gävert, 2017-02-16
7. Why Deep Learning?
• Rebranded artificial neural networks, so what is different now?
Big Data
- Text, images, video
- Large annotated data
sources, like images
155k words, 117k senses
14M images, 1M BBoxes, 22k synsets
Computational power
Some new algorithms;
ReLU, dropouts,
initializations, ConvNets
-4 -3 -2 -1 0 1 2 3 4
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1
-4 -3 -2 -1 0 1 2 3 4
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1
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1
Hugo Gävert, 2017-02-16
8. Deep Belief Networks
• 2006, Geoff Hinton: A Fast Learning Algorithm for Deep Belief Networks
• First major results in 2009 in Acoustic Model using Deep Belief Networks
—> Speech recognition
• What is it?
• Multilayer feedforward network with
• Input layer
• Many hidden layers
• Output layer
• Training…
Train as RBM
Train as RBM
Train with
backpropagation
Hugo Gävert, 2017-02-16
9. From feature engineering to feature learning
Input Output
Hand
designed
program
Rule-based AI
Trained
classifier
Input Output
Hand
designed
features
Classic ML
Features
Trained
classifier
Input Output
Representation
Learning
Simple
features
Mid level
abstract
features
Trained
classifier
Input Output
High level
abstract
features
Deep
Learning
Hugo Gävert, 2017-02-16
10. • Deep Belief Networks have largely been replaced by convolutional networks for image recognition
• Architecture, layers:
• Input (width, height, depth = RGB)
• Convolutional layer
• Neuron calculates convolution of the weights over the local image area
• N filters with size (width, height, N)
• Relu activation layer
• Pooling layer
• Downsampling along the spatial width and height dimension
• Fully connected layer (output: 1 x 1 x num of classes)
• The conv + relu + pooling layers are repeated.
• Of course, other architectures also…
Convolutional networks?
Hugo Gävert, 2017-02-16
11. Example, 17 layers, 7000 params.
http://cs231n.stanford.edu/Hugo Gävert, 2017-02-16
13. Convolutional networks - What is deep?
• AlexNet, 2012
• ImageNet challenge, top 5 error rate 16% (previous 26%)
• 5 conv, max-pooling, drop-out layers, 3 fully connected
• ZF Net, 2013
• Top 5 error rate 11.2%
• Similar architecture, only 10% of training data
• DeConvNet - visualisation of the layers
• VGG Net, 2014
• Top 5 error rate 7.3%
• 19 layers, but simple 3x3 convolution and 2x2 max pooling
• CNNs need to be deep, but otherwise simple
• GoogLeNet, 2015
• Top 5 error rate 6.7%
• 22 layers, but has inception-modules that do work in parallel
• Microsoft ResNet, 2015
• Top 5 error rate 3.6% (better than human)
• 152 layers, ultra deep
Hugo Gävert, 2017-02-16
14. Speech Recognition at Google
Brandon Ballinger: Deep Learning and the Dream of AI, Strata 2013
Jaitly et al (2012), Application of pretrained deep neural networks to LVSRHugo Gävert, 2017-02-16
15. Chatbots and AI
• Speech recognition ok
• Natural language
understanding needs work
• Logic
• If … then…
• No memory in session
• Behavior / approach
• Reactive, just answers
questions
• Proactive would be helpful…
Hugo Gävert, 2017-02-16
16. Products you should test / use
• Google APIs
• Machine learning platform (Deep
Learning: TensorFlow)
• Natural Language API
• Speech API
• Translation API
• Vision API
• IBM Watson analytics…
• Also, some of the famous image
ConvNets are downloadable in pre-
trained format
• MS Azure ML (Cortana analytics,
cognitive services)
• Deep Learning: CNTK
• Vision: Face API, Emotion API,
Computer Vision API, Content
Moderation API
• Recommendations API, Academic
knowledge API, Entity linking API,
Anomaly Detection
• Language: Text Analysis, Web
Language Model, spell checking,
translation
• Speech: Speech to text, speaker
identification, translation
Hugo Gävert, 2017-02-16
17. So is AI going to take the job of Data Scientists?
• Yes, absolutely
• Why?
• We, the data scientists, are building the
AI - we’re lazy, we build AI to do our
job…
• Harder to build the robots (or cars,
trucks, flying machines) than to just run
the AI inside computer. The early use
cases will be confined in the computers.
• When?
• Not very soon…
Hugo Gävert, 2017-02-16
18. What does typical data science project look like?
Business
understanding
Data understanding
and quality
Data pre-processing
Feature engineering
Modeling
Evaluation
Production
deployment
Hugo Gävert, 2017-02-16
19. What does typical data science project look like?
Business
understanding
Data understanding
and quality
Data pre-processing
Feature engineering
Modeling
Evaluation
Production
deployment
Data collection
design
Monitoring, control
Feature learning
Deep Learning
architecture
Communications,
internal consulting
How do we get
representative data for
the network?
Ok, images easy - how
about others?
Does it work?
Still expected results?
Fraudulent use?
What is this Black Box?
APIs
Hugo Gävert, 2017-02-16
20. Recommendations for
Data Scientists
• Keep on doing what you do
• Evolve with the world
• You still need
• Math; stats, probabilities, linear algebra…
• Algorithms and data structures
• You also need now
• Deep Learning (hype!)
• More communications skills
• Software writing & engineering skills (APIs)
• Google and Stack Overflow helps…
Hugo Gävert, 2017-02-16
21. Recommendations for companies
• Data
• Create data strategy; collect, store and make data available
• Data is key business asset in building AI capability. Deep
Learning needs data in training. Software can be replicated,
but data cannot - if a business has data, then it’s already in
better position than competitors.
• Hire talent
• AI models need to be customized for the business need,
application and context.
• Downloading open source software is not enough.
Applying it is far from trivial. The APIs solve only specific
problems and are too much black boxes.
• You need to be able to explain the models to customers -
specially in the legal, finance, insurance, health etc.
business.
“The best ideas
come from the guys
closest to the data.”
Todd Holloway
Head of Data Science at Trulia.
Hugo Gävert, 2017-02-16
