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Semelhante a How Will AI Change the Role of the Data Scientist?
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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 -1 1 -4 -3 -2 -1 0 1 2 3 4 -1 1 -4 -3 -2 -1 0 1 2 3 4 -1 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
- 12. More example layers… 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