How Will AI Change the Role of the Data Scientist?

How Will AI Change the Role of the Data Scientist?

Hugo Gävert @hgavert

Helsinki Data Science meet-up 2017-02-16

Who am I?Currently:Chief Data Scientist @ Sanoma

Past: • HUT Infolab • Xtract • Nokia

Hugo Gävert, 2017-02-16

Artificial IntelligenceWorld 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?


Artificial Super-Intelligence

Human Intelligence

Artificial Intelligence

Inte

lligen

ce /

Perfo

rman

ce

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 LevelMachine Intelligence: 10%: 2020 50%: 2040-2050 90%: 2080-2100


• 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


http://playground.tensorflow.org/


Why Deep Learning?• Rebranded artificial neural networks, so what is different now?

Big Data - Text, images, video - Large annotated data

sources, like images155k 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 4-1

1

-4 -3 -2 -1 0 1 2 3 4-1

1-4 -3 -2 -1 0 1 2 3 4

-1

1


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 RBMTrain with backpropagation


From feature engineering to feature learning

Input OutputHand

designed program

Rule-based AI

Trained classifierInput Output

Hand designed features

Classic ML

Features Trained classifierInput OutputRepresentation

Learning

Simple features

Mid level abstract features

Trained classifierInput Output

High level abstract features

DeepLearning


• 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?


Example, 17 layers, 7000 params.

http://cs231n.stanford.edu/Hugo Gävert, 2017-02-16

More example layers…


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


Speech Recognition at Google

Brandon Ballinger: Deep Learning and the Dream of AI, Strata 2013Jaitly et al (2012), Application of pretrained deep neural networks to LVSRHugo Gävert, 2017-02-16

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…


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


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…


What does typical data science project look like?

Business understanding

Data understanding and quality

Data pre-processing

Feature engineering

Modeling

Evaluation

Production deployment


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


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…


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.


Thanks!Hugo Gävert

[email protected] @hgavert

Data & Analytics

How Will AI Change the Role of the Data Scientist?