Artificial Intelligence - AI For Everyone

AI For Everyone.
connect: sridhar@seshadri.vc

A quick intro…
• Domain Expert and Architect of SMAC3 (Social,
Mobile, Analytics, Cloud & Web 3.0) Technologies,
Building Startups Ground-up, GoTo Market Strategies
& Business Models Creation.
• Advising growth-stage start-ups & conglomerates in
USA, UK, MENA in the space of DeepTech – AI &
Blockchain
• CoFounder of a DeepTech start-up(valued at around
USD 60M)
• Mentor at Stanford Seed, AICs THub
• Board Advisory Member For Mid Size SIs
• Advisor at Ankurit Capital.
Learner, Author, Technologist, and Entrepreneur.

• Quiz
• History & Basics of AI
• Automation vs AI
• Trends in AI
• Agent & Use cases
• AI & Ethics
• Socio-Economic Implication

When (Room temp = 25 Degree C),
Action = turn on the Air Conditioner
Based on body heat pattern regulate the
temperature of the Air Conditioner
QUIZ

Alarm Clock Gun
How will you make these smart?

Signals (Metrics)
1) Sleep Cycle (when do you get up
and when do you sleep)
2) Snoring Noise Pattern
3) Outdoor activity
4) Health activity monitoring
5) Calendar Schedule Based Prompts
Etc..
Alarm Clock

Signals (Metrics)
1) Biometric Pattern Matching
2) Pressure Matching
3) Modes based on Person’s profile ex.
Taser, Pellet, Bullet etc.
Etc..
GUN

Automation
• A task that has a fixed process
and can be accomplished by
following the same steps every
time
• It is based on conditions (ex. IF
THEN ELSE)
• Occurs at a fix frequency
Intelligence
• No fixed process to
accomplish, is critical thinking
problem statement
• Is beyond a IF THEN ELSE, and
is mostly probabilistic
• Based on a complex pattern
and needs analytical way to
think through
Automation vs Intelligence
As soon as it works, no one calls it AI anymore.” — John McCarthy

- 5 Senses
- Taste, Sight, Smell, Hearing and Touch
- Logic
- Rational & critical thinking
- Ability to think on the feet
- Action
- Execute on the best option of all
- Measure impact
- Get better with execution next time
What is Intelligence to a Machine?
“ Machine shall mimic alike a human baby”

Machine Learning
• ML is a subset of AI.
• Deep Learning (DL) is ML but applied to large data sets.
• Supervised Learning
Supervised learning is a learning in which we teach or train the machine using data
which is well labeled that means some data is already tagged with the correct answer
Supervised learning classified into two categories of algorithms:
Classification: A classification problem is when the output variable is a category, such as
“South Asian” or “Asian” or “American” and “British”.
Regression: A regression problem is when the output variable is a real value, such as
“How many Asians” or “How many predicted attacks.”
• Unsupervised Learning
• Unsupervised learning is the training of machine using information that is neither
classified nor labeled and allowing the algorithm to act on that information without
guidance. Here the task of machine is to group unsorted information according to
similarities, patterns and differences without any prior training of data.
• Clustering: Grouping terrorist by attack pattern.

NLG - NLP
• Natural Language Generation
(NLG), a subfield of artificial
intelligence (AI) which produces
language as output on the basis
of data input, is not a new
concept
• There are a plethora of ways
the technology is being
employed, primarily to improve
human productivity, customer
engagement and operational
efficiency

Natural Language Processing & Generation
Natural language processing (NLP) is the ability of a computer program to
understand human language as it is spoken.
Syntactic Analysis
Lexical Analysis

Computer Vision
Computer vision is a field of computer science that works on
enabling computers to see, identify and process images in the
same way that human vision does.
Thermal Imaging and Convolutional Neural Networks
At Edwards Air Force Base, for example, a system of
ground-based radars sweeps over its 308,000 acres.
Autonomous vehicles, robots, deep fakes, CCTV
surveillance, interrogation etc.

History
1.Early Dreams (1940s-50s): From Alan Turing's groundbreaking questions about machine intelligence to the birth of the term "artificial
intelligence," explore the early theoretical work and optimistic visions that laid the foundation.
2.AI Winter (1960s-70s): Discuss the initial setbacks due to limited computing power and overly ambitious goals, highlighting the lessons
learned and the impact on the field's trajectory.
3.Expert Systems & Knowledge Revolution (1980s): Focus on developing rule-based systems for specific tasks, like medical diagnosis, and the
rise of knowledge representation techniques, showcasing the first wave of practical applications.
4.Machine Learning Explosion (1990s-2000s): Dive into the paradigm shift towards statistical learning algorithms like neural networks,
leading to breakthroughs in speech recognition, image recognition, and other areas.
5.Deep Learning & the AI Renaissance (2010s-Present): Highlight the rise of powerful deep learning architectures, fueled by big data and
increased computing power, driving major advancements in natural language processing, computer vision, and other fields.

Historic Trends

Trends!
Focus:
Big Data Analytics
Focus:
Data Science
Focus:
Enterprise AI
Focus:
Industry Transformation
Focus:
Structured Data
• Focus on traditional business
intelligence (BI) analytics driven off
structured data
• E.g. Tableau, SAS, SPSS
• Focus on data lakes to gather
data from internal systems and
external sources
• E.g. Cloudera, Hortonworks, IBM
Big Insights, Palantir
• Focus on developer and data
science tools and technical services
to construct AI applications
• E.g. IBM Watson, AWS ML, Azure
ML, H2O.ai, Microsoft Cognitive
Services
• Augmented Intelligence to drive
hyper-personalized user
engagement and process
Intelligence
• Industry optimized Cognitive
Clouds focused on solving high ROI
use cases in vertical domains
TRADITIONAL
ANALYTICS
BIG DATA
PLATFORMS
MACHINE
LEARNING
PLATFORMS
AUGMENTED
INTELLIGENCE
PLATFORMS
INDUSTRY
COGNITIVE
CLOUDS
Structured Data
Unstructured Data
AI Technical
Services e.g. Watson
AI/ML Tools
e.g. Azure ML
User
Engagement
Process
Intelligence
Cloud scale
New Business
Models
DEEPTECH
GENAI
XAI
MULTIMODAL

Framework to design an AI Agent.

AI AGENTS & ENVIRONMENT
Artificial intelligence is defined as the
study of rational agents.
A rational agent could decide as a person, firm, machine,
or software. After considering past and current
percepts(agent’s perceptual inputs at a given instance), it
acts with the best outcome.
An AI system is composed of an agent
and its environment.
An agent is anything that can be viewed as :
• perceiving its environment through sensors and
• acting upon that environment through actuators

Four Phases of AI System
Act on the advice thru
software/hardware
Learn from feedback, models,
and advice that is accepted /
refuted
Connect to the domain data
Discover right algorithm
Define the relevant use case
and domain context
Model the logic to solve for the
use case
Advise based on the outcome of
the algorithm
Assure with the confidence score
8
Define & Model Connect & Discover Advise & Assure Act & Learn

AI – Hot Air Balloon
Problem Statement:
“Will we be able to fly a hot air
balloon tomorrow?
Looking at the outdoor conditions
(Outlook = Rainy, Temp = Mild,
Humidity = Normal, Windy =
True)?”
The posterior probability of occurrence of an event
What do we need to know? Factors on which the probability of occurrence and non-
occurrence depends.

Phase 1 : Define & Model
• Define the metrics that shall impact the final outcome
• Collect the data
• Enrich the data
• Choose the right model
• We need a math model for calculating posterior probability
• Transform the freq. data to likelihood data and finally use Naïve Bayesian
Equation to calculate the Posterior Probability for each classification (occur and
non-occur)

Phase 2 : Connect & Discover
• We need a math model for calculating posterior probability
• Transform the freq. data to likelihood data
• Finally use Right Math Equation {Naïve Bayesian} to calculate
the Probability {Posterior} for each classification (occur and
non-occur)

AI – Hot Air Balloon – Connect & Discover
Outlook Temp Humidity Windy Fly
Rainy Hot High FALSE No
Rainy Hot High TRUE No
Overcast Hot High FALSE Yes
Sunny Mild High FALSE Yes
Sunny Cool Normal FALSE Yes
Sunny Cool Normal TRUE No
Overcast Cool Normal TRUE Yes
Rainy Mild High FALSE No
Rainy Cool Normal FALSE Yes
Sunny Mild Normal FALSE Yes
Rainy Mild Normal TRUE Yes
Overcast Mild High TRUE Yes
Overcast Hot Normal FALSE Yes
Sunny Mild High TRUE No
Define: Signals/Metrics
Outlook, Temp, Humidity, Windy, Historic Outcome Flying
Apply the chosen model on the data
Probability of Yes = 9/14 No = 5/14
Frequency Table Fly Ballon Frequency Table Fly Ballon
Yes No Yes No
Outlook
Sunny 3 (3/9) 2 (2/5)
Temp.
Hot 2 (2/9) 2 (2/5)
Overcast 4 (4/9) 0 Mild 4 (4/9) 2 (2/5)
Rainy 2 (2/9) 3 (3/5) Cool 3 (3/9) 1 (1/5)
9 5 9 5
Frequency Table Fly Ballon Frequency Table Fly Ballon
Yes No Yes No
Humidity
High 3 (3/9) 4 (4/5)
Windy
FALSE 6 (6/9) 2 (2/5)
Normal 6 (6/9) 1 (1/5) TRUE 3 (3/9) 3 (3/5)
9 5 9 5
Historic Behavior

AI – Hot Air Balloon – Outcome
Prediction for a day, where
Outlook = Rainy
Temp = Mild
Humidity = Normal
Windy = True
Likelihood of Yes =
P(Outlook=Rainy|Yes)*P(Temp=Mild|Yes)*P(Humidity=Normal
|Yes)*P(Windy=True|Yes)*P(Yes)
= 2/9 * 4/9 * 6/9 * 9/14
= 0.014109347
Likelihood of No =
P(Outlook=Rainy|No) * P
(Temp=Mild|No)*P(Humidity=Normal|No)*P(Windy=True|No
)*P(No)
= 3/5 * 2/5 * 1/5 * 3/5 * 5/14
= 0.010285714
Normalize
P(Yes) = 0.014109347/(0.014109347+0.010285714) =
0.578368999
P(No) = 0.010285714/ (0.014109347+0.010285714) =
0.421631001

AI – Hot Air Balloon– Outcome
Phase 3: Advice & Assure
“It’s is likely that you can fly the hot air balloon if the weather conditions
are, Outlook = Rainy, Temp = Mild, Humidity = Normal, Windy = True.
Assurance : Confidence Level for The Prediction = 57%
Phase 4: Act & Learn
Add the Weather condition and the outcome back to the original
database

CHURNER
PROFILE
SESSIONS/DAY
SESSION TIME
AGE IN GAME
LEVEL
DEVICE
4
85 SECS
2 DAYS
8
IPHONE 6
HISTORICAL PLAYERS CURRENT PLAYERS
Mobile Game Company - Use Case
Identify historical churners
Determine play patterns
Identify key churn indicators
Create multiple profiles
Search players w/ similar profiles
Make predictions

POTENTIAL CHURNERS
AI SDK automatically messages predicted churned users
Automatically flight incentives
Track ‘rewardees’ for 14 days
AWESOMENESS!
You’re doing great,
greatness deserves
rewarding!
TOOL AGNOSTIC
Direct to game client
Through game server
Through marketing tools
AI SDK has its own tool as well
ACTUAL AI SDK MESSAGES
Player Experience

AI & ETHICS

The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems
“A set of moral principles, especially ones relating to or
affirming a specified group, field, or form of conduct.”
5 Principles of Ethics!
• Human Rights
• Prioritizing Well-being
• Accountability
• Transparency
• Technology Misuse and Awareness of It

How IEEE define it?
• How can we ensure that A/IS do not infringe upon human rights?
• Traditional metrics of prosperity do not take into account the full effect
of A/IS technologies on human well-being.
• How can we assure that designers, manufacturers, owners, and operators
of A/IS are responsible and accountable?
• How can we ensure that A/IS are transparent?
• How can we extend the benefits and minimize the risks of A/IS
technology being misused?

Challenges
• Communicating complexity: How do we best communicate, through words and processes, the
nuances of a complex field like AI?
• Ethical design: How do we build and design technologies that consider ethical frameworks and
moral values as central features of technological innovation?
• Advancing accountable and fair AI: What kinds of controls do we need to minimize AI’s potential
harm to society and maximize its benefits?
• Innovation in the public interest: How do we maintain the ability of engineers and entrepreneurs
to innovate, create and profit, while ensuring that society is informed and that the work integrates
public interest perspectives?
• Expanding the table: How do we grow the field to ensure that a range of constituencies are
involved with building the tools and analysing social impact?

SOCIO-ECONOMIC IMPLICATIONS

IMPLICATIONS
Potential benefits:
• Economic growth: AI can automate tasks, improve
efficiency, and create new industries, potentially
boosting economic growth and productivity.
• Improved healthcare: AI can analyze medical
data, diagnose diseases, and personalize treatment
plans, leading to better healthcare outcomes.
• Enhanced education: AI-powered tutors and
personalized learning platforms can adapt to
individual needs and improve educational outcomes.
• Increased accessibility: AI tools can assist people with
disabilities, making society more inclusive.
• Innovation and creativity: AI can generate new ideas
and designs, accelerating innovation across various
fields.
Potential challenges:
• Job displacement: Automation through AI could lead
to job losses in certain sectors, requiring workforce
retraining and social safety nets.
• Widening inequality: AI-powered technologies might
benefit those with access to them, exacerbating
existing inequalities.
• Privacy concerns: AI systems collecting and analyzing
data raise concerns about privacy and potential
misuse of personal information.
• Ethical considerations: Biases in AI algorithms could
lead to discrimination, and the development of
autonomous weapons raises ethical questions.
• Transparency and explainability: Complex AI models
can be difficult to understand, making it challenging
to hold them accountable for their decisions.

IMPLICATIONS
• Policy and regulation: Governments and institutions can
develop policies to mitigate job losses, ensure data privacy,
and address ethical concerns.
• Upskilling and reskilling initiatives: Providing training and
education opportunities can help workers adapt to the
changing job market.
• Open dialogue and collaboration: Engaging stakeholders from
diverse backgrounds in discussions about AI can help develop
responsible and inclusive solutions.

I WOULD LOVE TO TAKE SOME QUESTIONS

Connect With Me
Sridhar Seshadri
Email: sridhar@seshadri.vc
LinkedIn: linkedin.com/in/seshadrisridhar/
Uncommon Man’s Artificial Intelligence

Artificial Intelligence - AI For Everyone

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