deepak.rout@microsoft.com

Demystifying

Artificial Intelligence not a new term …

But with many new applications!

Buzzwords - often used interchangeably

Not quite the same thing

Both terms connected with Big Data & Analytics

• AI is the broader concept of machines being able to carry out tasks

in a way that we would consider “smart”, and

• ML is an application of AI based around the idea that machines

with access to data can “learn” for themselves

Artificial Intelligence - 1950’s

In 1952, Alexander Douglas at University of Cambridge developed one of the first known

video games - playing tic-tac-toe with a computer (guess who won!)

Artificial Intelligence - 1990’s

In 1996, after four decades of on-going battle, Deep Blue became the first machine to win a

chess game against reigning world champion Garry Kasparov

Artificial Intelligence - 2018

“Artificial Intelligence is like summoning the demon” - Elon Musk

ML is a branch of AI, concerned with the design and development of algorithms

that allow computers to evolve behaviors based on empirical data, i.e. improve (or

optimize) performance with experience (or more data)

https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/

Training examples of a person

Test images

http://www.uk.research.att.com/facedatabase.html

Which of these things is NOT like the others?

Which of these things is like the others? And how?

Why “Learn”?

• There is no need to “learn” to calculate payroll

• Learning is useful when:

• Human expertise does not exist (navigating on Mars, choosing an action)

• Humans are unable to explain their expertise (speech/image recognition)

• Solution/action changes with time (routing on a network, conditional access)

• Solution needs to be adapted to particular cases (using biometrics)

• ML is the increasingly preferred approach for:

• Speech recognition & Natural Language Processing

• Image analysis

• Medical outcomes analysis

• Robotics controls

• Computational biology

• Security analytics

• Accelerating trend on:

• Improved machine learning algorithms

• Improved data capture, networking, faster computers

• New sensors / IO devices

• Demand for self-customization to user, environment

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

• Concept Learning

• Clustering Algorithms

• Connectionist Algorithms

• Genetic Algorithms

• Explanation-based Learning

• Transformation-based Learning

• Case-based Learning

• Macro Learning

• Evaluation Functions

• Cognitive Learning Architectures

• Constructive Induction

• Discovery Systems

• Knowledge capture

• Data Mining

• Probability & Statistics

• Information theory

• Numerical optimization

• Computational complexity theory

• Control theory (adaptive)

• Psychology (developmental, cognitive)

• Neurobiology

• Linguistics

• Philosophy

AI & ML Applications

Autonomous Vehicles Home Automation

and many more…

• Recognize faces

• Transcribe speech

• Translate between hundreds of languages

• Spot subtle financial fraud

• Find relevant web pages for ambiguous queries

• Map the best driving route

• Automated cancer diagnoses

• Housecleaning robots

• Automated scientific discovery

Over the next five to 10 years, Facebook A.I. will

“get better than human level at all of the

primary human senses: vision, hearing,

language, general cognition.” - Mark Zuckerberg

Personal Assistants

Cyberspace landscape is rapidly

changing

Virtually anything can be attacked

Security skills are in short supply

99 days Median # of days between infiltration and detection

88% Of companies concerned

about cyberattacks in 2017

$4M Average cost of a data

breach in 2017

$8 trillion Cost (USD) of cybercrime

to global economy by 2022

750+% Growth in # of ransomware

families in 2016

Cybersecurity: in the News, in the Boardroom

Criminals are and

Cybercrime & Cybersecurity challenges

Too much to handle

False positives & Prioritization

Lack of in-house expertise

Opportunities from the Challenge

Towards Insight and Intelligence

6.5T threat signals

analyzed daily

Microsoft Trust Center

Bucketing Functions • Extract key dimensions

• Dependent on richness of the data

Promotion Signals • Signal of insight of “new issue”

• Selector for higher analysis

• Key to scalable system

Supervised Automatic Learning • Seek signals for self-correction

• Resiliency and Robustness

• HITL Human Over The Loop

Signals Labels

Higher dimensional data

Lower dimensional data

Labeling • Analyze and decide verdict or labels

• E.g. mapping, analysis, classifier, …

Identity at the center of security leveraging AI & ML

Corporate Network

Geo-location

MacOS

Android

iOS

Windows

Windows Defender ATP

Client apps

Browser apps

Google ID

MSA

Azure AD

ADFS

Employee & Partner Users and Roles

Trusted & Compliant Devices

Location

Client apps & Auth Method

Conditions

Microsoft Cloud App Security

Force password reset

Require MFA

Allow/block access

Terms of Use

******

Limited access

Controls

Machine learning

Policies

Real time Evaluation Engine

Session Risk

3

40TB

Effective policy

Security Operations leveraging AI & ML

Digital Cybercrimes Unit Leading the fight against cybercrime

Protecting people, organizations and our cloud through

global disruptions and enforcement actions against

cybercriminals leveraging AI & ML

Investigations, forensics and analytics

Machine learning, AI and data visualization

Public and private partnerships

Creative legal strategies

PhotoDNA 99% of Cyber Tip reports to the National Center for Missing and Exploited Children, are generated by PhotoDNA (8.2M+ annually) PhotoDNA has enabled increasing law enforcement engagement, and introduction of private sector participation (130+ private organizations) PhotoDNA is built into international law enforcement forensic tools Leveraging membership organizations like Internet Watch Foundation and Protect.org, to drive increased licensing/usage of PhotoDNA Innovation key to progress: PhotoDNA Cloud Service and PhotoDNA for Video

Online Child Exploitation

Every day, billions of unique images are uploaded and

shared via the Internet. Finding known child sex abuse

images is like trying to find a needle in a haystack.

AI & ML is critical to making sense of the information.

Perpetuation of child exploitation / revictimization

Law enforcement challenges from the scale of problem

At Microsoft, we do not take your trust for granted

• We live by standards and practices designed to earn your confidence and trust

• We collaborate with industry and regulators to build trust in the cloud ecosystem

• We leverage the scale and scope of the cloud, and cutting-edge AI & ML

• We are leading the deliberations on ethics in AI

“Businesses and users are going to embrace technology only if they can trust it.”

—Satya Nadella

• Increase in the number of

products and services

implementing AI effectively in

multitude of domains has led to

increased interest in

standardization of AI

• Countries and regulators have

begun taking interest in many

aspects of AI

• Standards are needed to enable

wider acceptance of approaches

and concepts, and to address the

interests and concerns of

governments and society

• Standards provide requirements, specifications, and guidelines that can be used

consistently to ensure that AI technologies meet objectives for functionality and

interoperability, and that they perform reliably, safely & ethically

• Techniques and algorithms in AI & ML are evolving rapidly

• More and more applications of them are being adopted

• They will continue to play an important role in all spheres of life

• Their usage in everything including Security will grow exponentially