MACHINE LEARNING and AI

AI Learns from data and make predictions about data by using statistics to develop self learning algorithm

By Tiranee AchalakulGovernment Big Data Institute

Pre-Crime Intelligence System

Beware by

DAS by

internet crawling, searching, data aggregation, data analysis, data visualization, data extraction and image and VDO analysis.

In 17 seconds, Watson analyzes 3500 text book and 400,000 piece of data in order to find the probable causes

of cancer and give options of treatment

• Evidence-based treatment recommendations

• Matching eligible patients with relevant clinical trials

City Surveillance

A Chinese school has equipped several classrooms with cameras that can recognize the emotions of students

Transfer of Occupationsfor unemployed newly grads

• CVs and committed occupations of jobseekers

• University Graduates

Data from สกอ

• Number of job vacancies (JobDB, JobTOPGUN, JobThai, etc.)

Input OutputProcess

• Up-skills/ Re-skills • Transfer

occupation list for surplus occupations

NLP

Unemployed Newly Grads

Job Coaching Service Platform

Skill Analytics Labor Market

CoachesGrads

Platform

AI Biases

When companies are looking to deploy AI, how much human biases can make their way into AI systems

AI systems learn to make decisions based on training data, which can include biased human decisions or reflect historical or social inequities.

AI / Machine Learning

Software

Bias can come from flawed data sampling, i.e. over-or underrepresented in the training data

EX, facial analysis technologies: Less accurate with minority women

Health data is mostly

white and male

• Tracked cancer mortality over 20 years pointed

to a lack of diverse research subjects as a key

reason why black Americans are significantly

more likely to die from cancer than white

Americans.

• Studies found that 81% of participants in

genome-mapping studies were of European

descent. Disease studies thus, used more data

of European descent than those of African,

Asian, Hispanic, or Middle Eastern descent.

Amazon stopped using a hiring algorithm after finding it favored applicants based on words like “executed” or “captured” that were

more commonly found on men’s resumes,

As the tech scene is mainly

dominated by men, and the

data fed to AI contained

resumés from the past 10

years, the program taught

itself that women were less

preferable candidates.

https://medicalfuturist.com/

What can we do ?

• Business leaders will need to stay

up to-date on AI.

• Deploy “human-in-the-loop” systems

• Establish processes that mitigate

bias. EX third-party audits.

• Provide more data, and take a multi-

disciplinary approach in bias

research

• Google AI has published

recommended practices

• IBM’s “Fairness 360” framework

pulls together common technical

tools.https://hbr.org/

“Human in the loop”

Three Levels of Intelligence

Artificial

Narrow

Intelligence

Specialized in

one specific

area.

Three Levels of Intelligence

Artificial

Narrow

Intelligence

Artificial

General

Intelligence

Specialized in

one specific

area.

Specialized in

all areas.

Artificial

Narrow

Intelligence

Artificial

General

Intelligence

Specialized in

one specific

area.

Specialized in

all areas.

Three Levels of Intelligence

16

Knowledge Graph

Adaptive ML

Edge AI

• Sensing and mobility

• augmented human• Postclassical

compute and comms

• Digital ecosystems• Advanced AI and

analytics

Towards General Artificial Intelligence

Three Levels of Intelligence

Artificial

Super

Intelligence

Smarter than

human in

every way.

We are currently climbing the tree

It’s too early to worry about the moon

Data Governance : ธรรมาภิบาลข้อมูล

Accuracy Completeness Timeliness Security Privacy Connectedness WorthinessData management

Policy

Organization &Stewardship Audit

Building Knowledge &Awareness

• Data Lifecycle• Data Security & Privacy

• Data Quality Assurance• Data Exchange

Function of• DG Committee• Steward team• Data

Controller/Processor/User

• Data Risk Management • Law & Regulation

Compliance• Data Quality Audit

• Program Coverage• Measurement• Ongoing

Data Management Policy : นโยบายการบริหารจดัการข้อมลู

Create Store Use Archive Destroy

DataLifecycle

Data Catalog Data Security & Privacy Data Quality

Fou

nd

atio

n

• What data ?• Who’s the

owner ?

• Search Tags• Data sources

• Confidentiality• Integrity

• Availability• Accuracy• Validity• Timeliness

• Completeness• Uniqueness• Consistency

Dat

a Ex

chan

ge

การบรูณาการข้อมลูภาครฐั

01 AUDIENCE

Thai people from different demographics and sectorsAudience segmentation:• Public: Gen, Geography, Occupation, Social Status• Government Employees: Type, Domain, Rank, etc.• Psychographic e.g. interests or attitudes

03 CONTENTS

Issues to consider:• Why do they want to know?• What are the topics or context of their interest?• How does this content affect their lives?

Content creators:• Government Agencies• Educational & research institutions• Online content creators: bloggers, YouTubers

02 CHANNELS

• Online: social media, blogs/online publications, official website

• Offline: TV, radio, out-of-home (OOH) ads – billboard or digital signage, events, PR

04 SPEAKERS

Celebrities who have a large group of followers and/or understand the content

Influencers/micro-influencers

who share stories and influence their followers on social media

Academic people who have good knowledge/expertise and well-respected

Inspirational youth who might be famous for both entertainment and understand the context

STRATEGIC COMMUNICATION