Disruptive Technologies Data Science & AI-Machine Learning

Transcription

1 Disruptive Technologies Data Science & AI-Machine Learning Alvin C. Francis Program Director, Predictive Analytics & Manager IBM Machine Learning Hub Canada 2017 IBM Corporation

2 About me 1. Program Director, Analytics Business Unit - Statistical Analysis -Predictive Analytics -Algorithms & Machine Learning components Consumed by Statistical & Predictive solutions and in Watson Machine Learning 2. Manager IBM Machine Learning Hub Canada -Place to collaborate with clients on Machine Learning 2 2

3 Digital businesses are disrupting industries and professions 72% are vulnerable to disruption within three years 3 3

4 Disruption is driven and enabled by IT 4

5 Every Industry is Affected Banking Financial Services Retail Health Care Manufacturing Telecommunications $$$$$ $10s of Billions 5 5

6 Telecom Industry 61% of CSPs point to Google as posing the biggest competitive threat to their business Heavy Reading: Webscale Internet Companies: New Drivers of the Network Equipment Market The global telecoms industry landscape is changing faster than ever. Erosion of legacy revenue streams driven by over-the-top (OTT) competitors continues, forcing operators to consider new ways of remaining relevant to consumer and enterprise customers. EY:Digital transformation for 2020 and beyond - A global telecommunications study 6

7 AI & Data Science are key agents of disruption in Telecoms IDC has predicted that within Telco organizations: 31% are leveraging existing investments & infrastructure 31% 69% 69% are making new technology investments for AI systems 7

8 Data Science is the practice of various scientific fields, their algorithms, approaches and processes, through the use of programming languages and software frameworks, that aims to extract knowledge, insights and recommendations from data, and deliver them to business users and consumers in consumable applications. 8

9 Artificial Intelligence vs. Machine Learning Artificial Intelligence: Machines being able to carry out tasks in a way that we would consider smart - Copying Intelligent Human Behavior Machine Learning is an application of AI where we give machines access to historical data and let them learn for themselves. 9

10 Hyper or Radical Personalization 10

11 Price and Product Optimization 11

12 Predictions and Classifications 12

13 Discover Patterns, Anomalies, and Trends 13

14 and to beat humans Jeopardy in

15 IBM Analytics Telecom Predictive Maintenance Fix problems with telecom hardware (such as cell towers, power lines, Power Generators etc.) before they happen, by detecting signals that usually lead to failure wear fatigue usage thermal stress physical damage abuse material buildup corrosion master data 15 time

16 Steps to put Data Science to work.. Clearly Articulate Use Case Gather all the Data Prepare Data Digital Application Evaluate Apply Machine Learning 16

17 Machine Learning What is it? Computers that learn without being explicitly programmed Using algorithms to understand patterns in data Data Predictions & Insight Algorithms 17

18 Machine Learning 101 Historical Data Train Algorithm Recognizes Patterns Data 1 Identify Patterns not recognizable by humans Contains Patterns New Data Find Patterns Build Model 2 from Build Models those patterns Data Use Model 3 With Make Predictions the deployed models 18

19 Supervised Learning All data is labeled and the algorithms learn to predict the output from the input data. Unsupervised Learning All data is unlabeled and the algorithms learn to inherent structure from the input data X 2 x x x x x x x x x boundary X 2 clusters X 1 Fraud detection (fraud, not fraud) Text sentiment analysis (happy, not happy ) Network Security - ( attack, not attack) X 1 Image Identification - What type of animal is this? Customer segmentation for targeted marketing 19

20 IBM Embraces Open Source for Data Science IBM Data Science Experience 20

21 CANADA Markham USA San Jose GERMANY Boeblingen CHINA Beijing INDIA Bangalore 5 Locations - One mission! Collaborate with clients, Share best practices. 21

22 3 collaboration Tracks New to Machine Learning? Learn about ML hottest Industry trends Take an ML 101 course with hands on exercises Practice on uses cases that are applicable to your industry Bring your own data or use publicly available Have an ML challenge that you would like to collaborate on? Work with IBM Data scientists for 2 days Bring your own sample data to the ML Hub Analyze &prepare data Feature Engineering Create, Evaluate & optimize models Want to Learn about IBM latest innovations in ML? Data Science Experience Driving efficiency & accuracy via Automation Continuous Feedback & Retraining 22

23 Machine Learning is to the 21st Century, what the Industrial Revolution was to the 18th Century Rob Thomas, GM IBM Analytics What role will/should Data Science & AI play in new networking technologies such as 5G, IOT, NFV, SDN? How will Network manufacturers, CSPs and Application developers use Data Science and AI to differentiate their offerings? ETSI: Experiential Networked Intelligence Specification Group Goal: Improve operators' experience regarding network deployment and operation, by using AI techniques.

24 THANK YOU 2017 IBM Corporation

25 Legal Disclaimer IBM Corporation All Rights Reserved. The information contained in this publication is provided for informational purposes only. While efforts were made to verify the completeness and accuracy of the information contained in this publication, it is provided AS IS without warranty of any kind, express or implied. In addition, this information is based on IBM s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this publication or any other materials. Nothing contained in this publication is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in this presentation may change at any time at IBM s sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific sales, revenue growth or other results. 25