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Artificial Intelligence, Machine Learning, Deep Learning, Cisco? Artificial Intelligence, Machine Learning, Deep Learning, Cisco? Tomáš Ondovčík Systems Architect 29.10.2019 Part 1: Introduction to AI / Machine Learning, … © 2019 Cisco and/or its affiliates. All rights reserved. An Introduction to ML/AI Lip Reading Deep Blue Watson Deep Mind Playing Music WaveNet (TTS) Counting people Recognition NLP Translation Self-driving Networks And Many more: CRM, Healthcare, Personal Assistants, …. © 2019 Cisco and/or its affiliates. All rights reserved. Von Neumann used this logic McCullogh & Pitts - 1943 when designing the “universal computer” Binary threshold neuron If sum > threshold, output 1 Output 0 otherwise Weighted inputs © 2019 Cisco and/or its affiliates. All rights reserved. In the 1950s and 1960s, Principles of Neurodynamics were examined and Symbolic ML expanded When I show these shapes to the camera This IBM 704 computer can say “it’s a triangle” © 2019 Cisco and/or its affiliates. All rights reserved. Why Now? Computing power Data “tsunami” Solve above equation for coefficients: Availability of tools Insatiable desire for efficiency / productivity © 2019 Cisco and/or its affiliates. All rights reserved. The Emergence of Large Data Sets and ML Data is the rocket fuel of Machine Learning – Andrew Ng • Open data sets have been a crucial factor in the success of ML • http://yann.lecun.com/exdb/mnist/ • http://image-net.org/ • https://bis.lexisnexis.com • https://catalog.data.gov/dataset • Allows for direct comparison of learning and inference algorithms • The result has been improvement of error rates • Video analytics (facial recognition) Graph courtesy Andrew Ng • NLP/Voice recognition © 2019 Cisco and/or its affiliates. All rights reserved. MNIST – Handwritten Digit Data Set • Training set of 60,000 examples (6000 per digit) • Test set of 10,000 examples (1000 per digit) • Each character is a 28x28 pixel box © 2019 Cisco and/or its affiliates. All rights reserved. Understanding the Roles Analytics / BI Software Data Acquisition Packages Data Manipulation Selection of the Right Data Data Data Movement analytics Tools Scientist Analyst Machine Learning Methods SQL Developers Connecting Data Insights to Visual Analytics Tools the Business Users of Data Mining Tools Network Engineers build the platform supporting the business © 2019 Cisco and/or its affiliates. All rights reserved. Artificial Intelligence, Machine Learning, and Deep Learning (AI/ML/DL) Artificial Intelligence Artificial Technique where computer can mimic human behavior Intelligence Machine Learning Machine Subset of AI techniques which use Learning algorithms to enable machines to learn from data F(x) Deep Learning Deep Subset of ML techniques which uses multi- Learning layer neural network to learn © 2019 Cisco and/or its affiliates. All rights reserved. It is a Complex Landscape Supervised Naïve Nearest Classification SVM Learning Bayes Neighbor Develop predictive models Linear Neural Decision Regression Machine Regression Networks Trees Learning Deep ANNs CNNs Learning Unsupervised Gaussian Hidden Clustering K-Means Learning Matrix Markov Discover patterns and anomalies Neural based on input data Networks © 2019 Cisco and/or its affiliates. All rights reserved. Machine Learning: The Main Methods Supervised Learning Learning with a labeled training set image by Jeff Dean Unsupervised Learning Discovering patterns in unlabeled data Inference (Statistical Learning) How confident are you in the result? © 2019 Cisco and/or its affiliates. All rights reserved. Reinforcement Learning • A semi-supervised learning model • No training data or correct/incorrect guidance needs to be given. • Involves behavioural psychology • In summary, a lot of trial and error Agent State S t Action At Reward Rt Environment © 2019 Cisco and/or its affiliates. All rights reserved. Supervised Learning Part 1 Linear Regression © 2019 Cisco and/or its affiliates. All rights reserved. Supervised Learning: Linear Regression (Single Variable) Data sample from across company “How much is our headcount going to cost us in 5 years?” © 2019 Cisco and/or its affiliates. All rights reserved. Supervised Learning: Linear Regression (Single Variable) Bad fit Good fit “What makes one fit bad and another good?” © 2019 Cisco and/or its affiliates. All rights reserved. How Close Is Our Prediction to the Dataset? The Cost Function 1 퐽 (휃 , 휃 ) = 푚 푚 ( 휃 + 휃 푥 − 푦 )2 0 1 2 푖=1 0 1 푖 푖 How far am I from the real y, if I use my random 휃0 and 휃1 and do 휃0 + 휃1x © 2019 Cisco and/or its affiliates. All rights reserved. Gradient Descent Getting Closer to the Answer © 2019 Cisco and/or its affiliates. All rights reserved. Gradient Descent Getting Closer to the Answer © 2019 Cisco and/or its affiliates. All rights reserved. Gradient Descent Getting Closer to the Answer © 2019 Cisco and/or its affiliates. All rights reserved. Gradient Descent Getting Closer to the Answer © 2019 Cisco and/or its affiliates. All rights reserved. Now we can Predict a Future Event Now I can make some predictions: “If a new person joined my team and they have been with the company 9 years, their salary will likely be:” Salary = 휃0 + 휃1 ∗ Years $54.6k + $2.35k x 9 = $75.75k © 2019 Cisco and/or its affiliates. All rights reserved. Expanding to Many More Dimensions And instead of 2 dimensions (x,y), You might have 1 million dimensions! (But the idea is the same) © 2019 Cisco and/or its affiliates. All rights reserved. Supervised Learning Part 2 Logistic Regression (Classification) © 2019 Cisco and/or its affiliates. All rights reserved. Supervised Learning: Classification • With linear regression you try to find a number (y) that predicts an outcome. • There is also classification, where your line separates two groups • E.g. Is a credit card transaction fraudulent or is it safe? • Also known as Logistic Regression © 2019 Cisco and/or its affiliates. All rights reserved. Fitting the Data • The main challenge in Supervised Learning is to find the right equation… and figure out if the samples represent the full population © 2019 Cisco and/or its affiliates. All rights reserved. Regression Summary: Learn, then Predict Algorithm Model Model Prediction Training Data New Data Learning (controlled experiments) Predict (AI in the wild) © 2019 Cisco and/or its affiliates. All rights reserved. How Accurate is Your Model? Measure Your Accuracy! Your Data Model Accuracy Training Data 99.3% Test Data 98.2% © 2019 Cisco and/or its affiliates. All rights reserved. A Typical Supervised Learning ML Work Flow Test Deploy Data Data Define Train Test Evaluate Acquisition Cleaning Model Model Model Model Adjust Model Parameters © 2019 Cisco and/or its affiliates. All rights reserved. Unsupervised Learning © 2019 Cisco and/or its affiliates. All rights reserved. Unsupervised Learning • You do not know the right answer, and there is too much data for you to guess • Example: you manufacture small engines • Some of them will fail • You want to spot the failures before they get installed on mowers, chainsaws, etc. • How do you do that? © 2019 Cisco and/or its affiliates. All rights reserved. Unsupervised Learning 4 groups = 4 engine categories • Your engine will group engines that have similar characteristics. • In math, this is simply grouping points that are close to one another • And will spot the outliers • Those are the engines likely to fail (different from the others) Outlier © 2019 Cisco and/or its affiliates. All rights reserved. Unsupervised Learning The math can take may forms, but a common form is K-means Want 3 groups? Take 3 random points (3 ‘centroids’, 휇푐 푖 ) Then take a known point, calculate which centroid is closest © 2019 Cisco and/or its affiliates. All rights reserved. Unsupervised Learning K-means Want 3 groups? Repeat for all points. That’s our usual distance equation: 퐾 푖 2 min 푖=1 | 푥 − 휇 푖 | 푐(푖) 푐 © 2019 Cisco and/or its affiliates. All rights reserved. Unsupervised Learning Then you move your ‘centroids’, 휇푐 푖 to the center (mean x,y) of each group you formed © 2019 Cisco and/or its affiliates. All rights reserved. Unsupervised Learning And you repeat. You are done when no point jumps to another group anymore © 2019 Cisco and/or its affiliates. All rights reserved. Unsupervised Learning And you repeat. You are done when no point jumps to another group anymore © 2019 Cisco and/or its affiliates. All rights reserved. Unsupervised Learning And you repeat. You are done when no point jumps to another group anymore © 2019 Cisco and/or its affiliates. All rights reserved. Unsupervised Learning And you repeat. You are done when no point jumps to another group anymore © 2019 Cisco and/or its affiliates. All rights reserved. Neural Networks © 2019 Cisco and/or its affiliates. All rights reserved. Neural Networks: From Neurons to Perceptrons Output 1. Inputs arrive from other neurons / signals nodes. Activation (threshold) 2. Receiving neuron / node processes Signal (impulse) propagation the inputs weighted sum. Input signals (form other axon terminas) 3. Receiving neuron / node applies an activation function (sigmoid). x1 w1 s(z) w2 X 4. Output of activation function passed x2 z = wi xi n to subsequent neurons / nodes. Output Weighted sum Activation . function w xn n © 2019 Cisco and/or its affiliates. All rights reserved. Neural Networks • Then you put tons of units, possibly in multiple layers (in this case, it is called deep learning) • Also called Artificial Neural Networks (ANNs) • Convolutional Neural Networks (CNNs) are a common sub- class of ANNs © 2019 Cisco and/or its affiliates. All rights reserved. Neural Networks: Image Recognition Backpropagation (just 10 years ago by Geoffry Hinton) Training Images Adjust weights Eventually, you train model to recognize “car” © 2019 Cisco and/or its affiliates. All rights reserved. Types of Neural Networks • The way you connect the units can vary immensely • And this is what makes this family very rich • Tons of possible applications depending on what data you are looking at, and what you try to find © 2019 Cisco and/or its affiliates.
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