Demystifying Machine Learning and Artificial Intelligence
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By Rob Baxter, EPCC. This talk was presented at the EPCC-CeeD event 'Advanced technologies for Industry 4.0' in September 2018.
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Demystifying Machine Learning and Artificial Intelligence
- 1. DEMYSTIFYING MACHINE LEARNING & AI Advanced Technologies for Industry 4.0 Dr Rob Baxter r.baxter@epcc.ed.ac.uk
- 2. Machine learning & AI: why now? • Because: Big Data • We have (a lot) more (digital) data • from the Web • from sensors (including cameras, mobile devices) • from transactional business systems • We have faster computers • parallel ‘cluster’ computing is mainstream • CPUs, GPUs, FPGAs, ASICs • We have lots of useful open source software • for data management, data pipelining & analytics • driven by social media giants Advanced Technologies for Industry 4.0
- 3. Big Data 2017: per year… 1PB NASDAQ 3PB US Census 4PB US Library of Congress 5PB NOAA archive 6PB YouTube 15PB Advanced Technologies for Industry 4.0
- 4. Big Data 2017: per year… Advanced Technologies for Industry 4.0
- 5. Big Data 2017: per year… CERN archive 73PB searches on Google 98PB uploads to Facebook 180PB Advanced Technologies for Industry 4.0
- 6. Big Data 2017: per year… CERN archive 73PB …2025 per year Square Kilometre Array Telescope, Phase 1 300PB searches on Google 98PB uploads to Facebook 180PB High Luminosity Large Hadron Collider 1,000PB Square Kilometre Array Telescope, Phase 2 1,000PB Advanced Technologies for Industry 4.0
- 7. Different kinds of “big” • Big Data are typically measured three ways • volume – from gigabytes to terabytes to petabytes • velocity – data streams at you or changes rapidly • variety – no longer are data in nice, neat tables • some folk add others • veracity, verifiability, validity, value… • Big Data come in many flavours • very large transaction databases • very large social graphs • very large image collections • very large numbers of sensor feeds • etc. Advanced Technologies for Industry 4.0
- 8. Data [ science | engineering | management ] ~20% Data science • analytics • statistics • machine learning ~40% Data engineering • data movement • data pipelines • data tech deployment (“data dev ops”) • database design • data preparation & cleaning ~40% Data management • data storage • data formats • metadata management • data preservation & backup • data preparation & cleaning Advanced Technologies for Industry 4.0
- 9. Machine learning “Machine learning is the science of getting computers to act without being explicitly programmed.” – Andrew Ng, Stanford University • Two main kinds of machine learning • unsupervised learning finds patterns in data without being told exactly what to look for • e.g. for clustering, fitting • supervised learning uses labelled training data to build a model, which is then used to make predictions • e.g. for classification Advanced Technologies for Industry 4.0
- 10. Unsupervised learning in action: k-means clustering Advanced Technologies for Industry 4.0 Iteration
- 11. Unsupervised learning: limitations of k- means • Clusters assumed to the the same size • Clusters on density not so good Advanced Technologies for Industry 4.0
- 12. minPts = 5, ε = 0.7 minPts = 5, ε = 0.8 minPts = 5, ε = 0.9 Unsupervised learning as art • Plenty of other unsupervised learning algorithms • distribution-based clustering • density-based clustering… etc • More complex ones have more free parameters • tweaking is as much art as science Advanced Technologies for Industry 4.0
- 13. Supervised learning: classifying irises Advanced Technologies for Industry 4.0 ? o o ? ? o Versicolor iris image courtesy of David Berger under a CC-BY licence setosa versicolor virginica • Crunch data on flower size, shape to identify its type (class label) • label = F (petal, sepal)
- 14. Supervised learning: step 1 – training • Need labelled (i.e. already classified) data • want to train a model to recognise the classes from the data (i.e. find F() ) • class label is dependent variable • rest of data are independent variables or predictors • Split your big data set into training & test sets • 70/30 or 60/40 or so • Feed training data into model-learning software • e.g. neural net, decision tree… • Result: a classifier model F : • label = F (petal, sepal) Advanced Technologies for Industry 4.0 petal sepal label 1.5 5.2 setosa 1.2 4.6 setosa 4.1 6.0 versicolor 5.2 6.0 virginica 6.0 7.2 virginica … … … Modelling software Classifier
- 15. Supervised learning: step 2 – evaluation • Feed test data into classifier model F • Count hits, misses vs your known labels • true positives, false positives… • Good enough? • good to go! • Not good enough? • go back • tweak your modelling software • try again Advanced Technologies for Industry 4.0 petal sepal label 1.4 5.1 setosa 5.3 6.5 virginica 4.5 6.2 virginica … … … Classifier petal sepal label model says… 1.4 5.1 setosa setosa 5.3 6.5 virginica virginica 4.5 6.2 virginica versicolor … … … …
- 16. Advanced supervised learning: deep learning • Deep learning: “learn multiple levels of representations that correspond to different levels of abstraction” • Wikipedia • An old-fashioned neural net is 1 layer deep • Deep learning neural nets are… deeper! • multi-layer NNs, deep NNs, recurrent NNs, convolution NNs • e.g. deep learning for image recognition • look at flat pixel data… (1 layer) • …and edge-detection in the image data… (another layer) • …and different scales of the image data… (another layer) • all in the same modelling framework Advanced Technologies for Industry 4.0
- 17. Advanced supervised learning: deep learning Advanced Technologies for Industry 4.0
- 18. Deep learning: spotting solar panels • Accuracy: • 99.60% ! • Careful! • a classifier that always says “background” is 98.75% accurate • precision is a better measure! • Precision: • 84.54% Advanced Technologies for Industry 4.0
- 19. Advanced supervised learning: reinforcement learning • Reinforcement learning allows software “agents” to “explore” • don’t need labelled data • just set up an environment & go • An agent: • takes actions in an environment • which is interpreted into a reward… • and a representation of the state… • which are fed back into the agent Advanced Technologies for Industry 4.0 • Good example is DeepMind’s AlphaGo Zero • two versions of the agent play Go against each other • learn winning strategies by beating the other guy
- 20. Machine learning and artificial intelligence • Today’s ML is principally pattern recognition • IF data.looksLike(pedestrian) THEN report(‘Pedestrian’); • This can be a powerful tool for decision support • Think of AI as taking next step to decision making: • IF data.looksLike(pedestrian) THEN brakes.On(now); • Generally, we want to use empirical data to take next-best- action • whether a human is in, on or out of the loop Advanced Technologies for Industry 4.0
- 21. The future of AI • State-of-the-art in AI driven robotics: • a team at Nanyang Technological University, Singapore got two industrial robots to assemble (most of) an IKEA STEFAN chair in c. 20 mins • The Economist, April 2018 • Current research topics are transfer learning… • can a machine learn the rules of Go (yes) then figure out how to apply them to the game of Chess (not yet) • …and curiosity-based learning • continuing the reinforcement-learning trend • Hardware is becoming specialised • GPUs (graphical processing units) and more • Excellent source: https://www.stateof.ai/ • Nathan Benaich, Ian Hogarth (UK AI VCs), June 2018 Advanced Technologies for Industry 4.0
- 22. Be problem-driven, not data-driven • Big Data / AI / ML is not a silver bullet • Don’t start with the tech – start with the problem • Don’t look at “your” data and ask what can I do with them? • Look at your business and ask, what can I do better? • improve operational efficiency (data management) • understand my customers better (data science/ML) • measure or monitor things with sensors (data engineering) • simulate things digitally (data engineering/management) • automate processes/decisions (ML/AI) Advanced Technologies for Industry 4.0
Editor's Notes
- We have a lot of data but we need techniques/tools/machines to understand/interpret the data and make use of it. Here is where machine learning and AI come into play. Data, powerful machines, and open-source software are available.
- Although we call this ‘unsupervised’ actually we have told the computer to divide the dataset into three groups. We could have said 2 or 4. This is the ‘k’ value. The ‘means’ part signifies that a data point is assigned to the cluster that has the closest mean (average) value. The algorithm tries to get the points so that the sum of the distances of each point to the mean of its cluster is the minimum for the whole set. Run the animation and watch the crosses (the mean of each cluster) move as the algorithm progresses towards a better solution.
- This method does not work for all datasets. These are standard datasets that are used to show that the method can break down.
- Setting the ringed parameter to differing values produces different results. These diagrams show unsupervised learning where the number of clusters is not given. Instead there are parameter to define the minimum number of points and how far they are allowed to be from the centre of a cluster but still be counted as part of it. Varying the distance parameter changes the results significantly. Which value is correct? There is no correct answer to that!
- Iris is a type of flower with three categories. The picture shows versicolor. Sepal is the part of the flower that supports the petals (usually green) not shown that well in this diagram. How would you classify the question marks? First one is quite easy Second one is quite easy Third one is trickier --- Plot comes from: my.plot <- xyplot(Sepal.Length ~ Petal.Length, data = iris, groups=Species, panel = panel.superpose, col.line = trellis.par.get("strip.background")$col, col.symbol = trellis.par.get("strip.shingle")$col, key = list(title = "Iris Data", x = .15, y=.85, corner = c(0,1), border = TRUE, points = list(col=trellis.par.get("strip.shingle")$col[1:3], pch = trellis.par.get("superpose.symbol")$pch[1:3], cex = trellis.par.get("superpose.symbol")$cex[1:3] ), text = list(levels(iris$Species))))
- The algorithm (set of steps) used to train a model.
- It’s quite an important point here that it’s not a good idea to use your training data as test data. This is why you hold some back (as on the previous slide). It’s possible to end up with a very misleading accuracy by ‘overtraining’ the algorithm so that it performs with very high accuracy on the training set, but is no good for data it has not ‘seen’ before.
- Neural networks usually have several layers. Deep learning comes from “deep neural networks”, ie a deep learning model contains a lot of layers. For further information, see: https://www.mathworks.com/discovery/deep-learning.html https://devblogs.nvidia.com/deep-learning-nutshell-core-concepts/
- CNNS are commonly used for image processing. A CNN consists of an input and an output layer, as well as multiple hidden layers. The hidden layers of a CNN typically consist of convolutional layers, pooling layers, fully connected layers and normalization layers. Convolutional layers apply a convolution operation to the input, passing the result to the next layer. See: https://en.wikipedia.org/wiki/Convolutional_neural_network#Convolutional
- i.e. PPV: 84.54% of the predicted solar panel pixels are solar panel pixels i.e. TPR: 83.78% of the pixels that belong to a solar panel are correctly predicted as solar panel pixels
- To learn more, see the Deepmind page: https://deepmind.com/blog/alphago-zero-learning-scratch/