Data and AI: The largest technology disruption in 240 years - Sam Lightstone CTO for Data & IBM Fellow - Sam Lightstone(2)
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Data and AI: The largest technology disruption in 240 years Sam Lightstone CTO for Data & IBM Fellow #ThinkLisboa
Disclaimer IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. Information presented and discussed during this meeting may be both IBM and client confidential. The agreements signed by members of the Technical Advisory Board govern usage of any and all information discussed and shared.
IBM Cloud / © 2018 IBM Corporation
IBM Cloud / © 2018 IBM Corporation
IBM Cloud / © 2018 IBM Corporation
Technology advancing. Business models transforming.
Amazing times
B-COOL
Amazing times….
The world’s largest personal transport company owns NO vehicles.
The world’s largest accommodations provider
12
owns no real estate.Photo from coursera.com
Coursera over 30 million users no classrooms
13The biggest disruption in 240 years IBM Analytics
The most disruptive innovations of the past 600 years IBM Analytics
AI is creating the largest
mass automation since the
advent of the steam engine
in 1776.
IBM AnalyticsWhy now? IBM Analytics
IBM Analytics
How does machine learning relate
to data science and AI?
Artificial Intelligence
Linear regression
Logistic regression
Machine
Linear Discriminant Analysis
Learning
Classification and Regression Trees
Naive Bayes Deep
K-Nearest Neighbors Learning
Learning Vector Quantization
Support Vector Machines
Data
Bagging and Random Forest Science
Gradient Boosting Data access, &
Artificial Neural Networks (many kinds) preparation
19The era of Machine Learning IBM Analytics
“Our relationship to computers has changed. Instead of programming them we now show them and they figure it out.” Geoffrey Hinton “The godfather of deep learning” See https://youtu.be/-eyhCTvrEtE. 34m.50s IBM Analytics
AI is getting real
You want to debate that?
IBM Analytics24
Philyra system Daub hired IBM to create AI for perfume. Produces new inventive perfumes Consumes large amounts of information about the formulas of existing fragrances, consumer data, regulatory information, etc. 25
The AI Ladder Multi-Cloud Data
Architecture
INFUSE – Automate and scale across your processes
TRUST– Archive trust and transparency in outcomes
ANALYZE – Scale insights with ML everywhere
ORGANIZE– Create a trusted analytics foundation
COLLECT – Make data simple and accessible
Data of every type: your critical data
assets no matter where they
physically are
Think 2019 / DOC ID / February 12, 2019 / © 2019 IBM CorporationIBM Data & AI Portfolio
Everything you need for Enterprise AI, on any cloud
Pre-built Use Cases
Watson Applications
The Ladder to AI
Build Run Manage
Watson
Watson Watson
Machine
Studio OpenScale
Learning
Hybrid Data Management Data Governance & Integration
Open source meets a
multicloud, working as ONE Db2 Family InfoSphere Family
Multicloud Data & AI Platform
IBM Cloud Pak for Data
27Watson Studio
Pre-Integrated Tools, Algorithms, Libraries for Data Science, ML/DL
Tools
• Best of breed open source & IBM tools
• Code (R, Python or Scala) and no-code/visual Decision
modeling tools Optimization
• Most popular open source frameworks Machine Learning Runtimes Deep Learning Runtimes
• IBM best-in-class frameworks
• Container-based resource management
Scalable Infrastructure
• On IBM Cloud: Elastic pay as you go
CPU/GPU use
Model Lifecycle Management
Think 2019 / 6974 / February 14, 2019 / © 2019 IBM Corporation 28
28Use Cases for Machine/Deep Learning
Cyber Defense Fraud Detection
Drug Discovery
Loan Face Chatbots
IoT Approval Medical Recognition
Decision-Making
Recommender Weather
systems Disease
Forecasting
Diagnostics
Targetted
marketing Robotics
Advanced Physics
Research Supply Chain Sales Media Analytics
Management Forecast
29Fun fact:
~75% of practical ML projects for business
operate on text and structured data (i.e.
relational or JSON).
Small amount on audio. Very small on
image & video.
Estimate, as of March 2019
IBM AnalyticsWhat if you could tap into
all of your critical data
assets no matter where they
physically are?
Think 2019 / DOC ID / February 12, 2019 / © 2019 IBM CorporationIBM Data Virtualization
Unified data asset catalog, lineage Unified access control and
and provenance security policies
Data Virtualization
[+ caching layer]
Data Warehouses & Big Data
Marts (Hadoop)
Spreadsheets &
Relational
Text files
Databases
No SQL
Locations: Private and public clouds, standalone systems, worldwide.
Think 2019 / DOC ID / February 12, 2019 / © 2019 IBM CorporationIBM Data Virtualization
Rich application capabilities
• Connect to Data Virtualization with your
favorite SQL apps and tools
• RStudio, Jupyter Notebook, Cognos,
Tableau, Microstrategy
• Db2 SQL and driver compatible
IBM AnalyticsDemo IBM Analytics
Meet Max
—Name: Max von Datacrunch
—Position: CIO for Capital Mercantile Bank
—Data: 12 Petabytes under management
—Pet peeve: Spends 600M USD on IT, but can’t
see a global view of his data.
—Pastimes: none
—Medical:
• Age 42 (looks 64).
• Blood pressure 180/120
• LDL cholesterol: 210 mg/dl
Demo
Think 2019 / DOC ID / February 12, 2019 / © 2019 IBM CorporationThe Capital Mercantile Bank *
What you’ll see
— Data Virtualization over widely distributed data
— SQL editor inside ICP for Data
— Data Science Experience with R Studio query over distributed
databases
— Plot.ly data visualization
The setup (actual!)
— Scale: 8 distributed data sources
— Format: MySQL, Informix, Db2, Oracle and others.
— Global locations: Hursley UK, Toronto CAN, San Jose USA
— Hardware: Mix of Intel and ARM CPUs.
The transaction data
— 2 ½ Years worth of data
— 1,500 data point per database per day
The Capital Mercantile Bank
Talented. Trusted. Global.
Demo
Think 2019 / DOC ID / February 12, 2019 / © 2019 IBM Corporation * fictional bankWe are the
technology
company
for the
enterprise
IBM AnalyticsThank You
Sam Lightstone
39About the Speaker
Sam Lightstone is IBM Chief
Technology Office (CTO) for Data, an
IBM Fellow and Master Inventor. He
loves to help customers solve real
problems and help IBM invent the
future. He has over 60 patents and is
widely published. In his spare time he
is a an avid guitar player and fencer.
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