AI and Data Science - Revolutionising Industries and Shaping the Future

1. AI and Data Science
Revolutionizing Industries and Shaping the Future
Mohammad Ansari
https://www.linkedin.com/in/mcubea/

2. Historical advancements in AI
Alan Turing publishes
"Computing Machinery and
Intelligence," which
proposes the Turing Test.
1950
Allen Newell and Herbert A.
Simon develop Logic
Theorist, the first AI
program to prove
mathematical theorems.
1955
Joseph Weizenbaum
develops ELIZA, the first AI
program to simulate a
Rogerian therapist.
1966
Shakey, the first mobile
robot that could reason
about its environment and
make plans, is developed.
1969
The first expert system,
MYCIN, is developed.
1985
IBM's Deep Blue defeats
chess grandmaster Garry
Kasparov.
1997
Google's DeepMind
program defeats Go
champion Lee Sedol.
2011
OpenAI's Dactyl robot learns
to manipulate objects with
2022

3. Generative AI is a type of artificial
intelligence (AI) that can create
new content, such as text, images,
or music.
Content creation
New text, images,
or music to
generate new
product
descriptions,
create marketing
materials, or even
write music.
Data
augmentation
Augment existing
datasets. Increase
the size of a
dataset, or to add
new data points
that are similar to
the existing data.
Data analysis
Analyse data and
identify patterns
that would be
difficult to see
with traditional
methods. This
could be used to
identify trends in
customer
behaviour, or to
detect fraud.
Artificial creativity
New forms of art,
such as paintings,
sculptures, or
music. This could
lead to new forms
of expression and
new ways of
interacting with
the world around
us.
Generative AI - The Game Changer

4. Success Now
Increased Computational
Power
•GPUs (Graphics Processing Units)
and TPUs (Tensor Processing Units
Big Data and Data
Availability
•The abundance of data in various
domains
Advancements in Deep
Learning
•Generative Adversarial Networks
(GANs) and Variational
Autoencoders (VAEs),
Transfer Learning and
Pre-trained Models
•The development and dissemination
of pre-trained models
Research Community and
Open Source Initiatives
•Fostered collaboration and
knowledge sharing..
Industry Applications and
Commercial Success
•Demonstrated success and real-
world applications of generative AI
in various industries.
Generative AI
Competitions and
Challenges
•Public competitions and challenges,
like the ImageNet Large Scale Visual
Recognition Challenge (ILSVRC
Investments in AI
Research
•Governments, tech companies, and
research institutions have made
substantial investments.
Availability of AI Tools
and Frameworks
•The availability of user-friendly AI
tools and frameworks, such as
TensorFlow, PyTorch, and Keras,
Demand for
Personalization and
Creativity
•The increasing demand for
personalized experiences and
creative content in various industries

5. Research Papers
Generative
Adversarial Nets"
(GANs):
Unique approach for
training generative
models using two neural
networks: a generator
that produces data
samples and a
discriminator that
distinguishes real data
from fake data
Improving Language
Understanding by
Generative Pre-
Training" (GPT):
Transformer-based
language model pre-
trained on a vast amount
of text using unsupervised
learning, resulting in
enhanced language
understanding and better
performance on various
tasks
Attention is All You
Need" (Transformer)
Transformer model, a
neural network
architecture that relies
solely on self-attention
mechanisms, achieving
state-of-the-art
performance in natural
language processing tasks
A Style-Based
Generator
Architecture for
Generative
Adversarial
Networks" (StyleGAN)
Introduces a novel
generator architecture for
GANs called StyleGAN,
allowing better control
and manipulation of
image generation by
separating the model's
latent space into different
style and content
representations
ClipGPT: Connecting
Text and Images using
Contrastive Learning
Leverages contrastive
learning to establish a
connection between text
and images, enabling
improved performance on
various vision-language
tasks
DALL·E: Creating
Images from Text
A powerful generative
model capable of creating
images from textual
descriptions, generating
diverse and creative visual
content

6. Foundation
Models
•This generative AI model can create images from text
descriptions. For example, you could ask it to create
an image of a cat wearing a hat, or a painting of a
beach scene at sunset.
DALL-E 2
•This generative AI model can generate text that is
indistinguishable from human-written text. For
example, you could ask it to write a poem, or a news
article.
GPT-3
•This generative AI model can create music that is
similar to the work of specific artists. For example, you
could ask it to create a song that sounds like a Beatles
song, or a symphony that sounds like Beethoven.
MuSig

7. Adoption
Cost-Effective Product Development
Simplifying Life in the Community
Solving Previously Unsolvable Problems
Enhancing Creativity and Innovation
Personalization and Customer Engagement
Faster Time-to-Market
Optimal Resource Allocation
Addressing Skill Gaps
Environmental Impact

8. Age of Co-Pilots
GitHub Copilot
Code completion
Boilerplate code
Entire functions
Natural language prompts
Code review
Office Copilot
Real-time Assistance
Task Automation
Collaboration and Knowledge
sharing
Personalized Assistance
Sales Copilot
Identify relevant information from
CRM
Summarizes cases and
conversations
Draft chat responses

9. Manufacturi
ng and
Supply Chain
•Unplanned downtime and equipment failures
•AI solution: Predictive maintenance models using
sensor data for proactive maintenance, reducing
downtime and costs
Predictive
Maintenance
•Complex supply chain networks and inventory
management challenges
•AI solution: AI algorithms for demand forecasting,
optimizing inventory levels, and improving logistics
efficiency
Supply Chain
Optimization
•Manual inspection and high error rates
•AI solution: Computer vision systems for automated
quality control, ensuring consistent product quality
and reducing defects
Quality
Control

10. Finance and
Banking
•Rising complexity and sophistication of financial
fraud
•AI solution: AI models for real-time fraud detection,
pattern recognition, and anomaly detection
Fraud
Detection and
Prevention
•Managing complex financial risks
•AI solution: AI algorithms for risk assessment and
mitigation, optimizing portfolio management
Risk
Management
•Long wait times and inefficient customer service
processes
•AI solution: AI-powered chatbots for instant
customer support and personalized assistance
Customer
Service
Efficiency

11. Retail and E-
commerce
•Difficulty in understanding customer preferences
and delivering personalized experiences
•AI solution: AI algorithms for customer
segmentation and recommendation systems,
enhancing customer satisfaction and engagement
Segmentation and
Personalized
Recommendations
•Inventory stockouts or excess inventory,
suboptimal pricing strategies
•AI solution: AI-powered algorithms for inventory
management and dynamic pricing, optimizing
stock levels and pricing strategies
Inventory
Management and
Pricing
Optimization
•Limitations of traditional shopping experiences
•AI solution: Virtual try-on and augmented reality
technologies for enhanced shopping experiences,
increasing customer engagement and sales
Virtual Try-On and
Augmented Reality
in Shopping

12. Health Care
•Limited availability of expert radiologists
•AI solution: AI algorithms for automated image
analysis, improving accuracy and efficiency
Medical
Imaging
Analysis
•Human error and misdiagnosis rates
•AI solution: Machine Learning models for accurate
disease diagnosis and prediction based on patient
data
Disease
Diagnosis and
Prediction
•One-size-fits-all treatment approaches
•AI solution: AI-powered algorithms for precision
medicine, considering individual patient
characteristics for tailored treatment plans
Personalized
Medicine

13. AI is here to stay and make a big impact
Percentage budget spent on AI in each segment, according to
IDC:
• Manufacturing: 18.8%
• Healthcare: 17.1%
• Financial services: 15.1%
• Retail: 12.9%
• Telecom: 11.2%
• Logistics: 10.3%
• Others: 14.6%
These percentages are based on the global spending on AI in
2023, which is estimated to be $154 billion

14. Challenges
Job Displacement
Bias and Fairness Issues
Privacy and Security Concerns.
Lack of Accountability
Ethical Dilemmas
Social Isolation
Economic Inequality
Reliance on AI Accuracy
Disinformation and Manipulation
Unemployment and Skills Gap

15. What’s next ?
• Quantum
Computation and AI
•Integer factorization, which is the process of finding
the prime factors of a large composite number for RSA
Factorization
•Salesman problem and portfolio optimization.
Optimization
•Quantum computers can naturally model quantum
systems
Simulation of
quantum systems
•Quantum support vector machines and quantum
neural networks.
Machine learning
•Quantum cryptography, such as quantum key
distribution, can offer enhanced security solutions.
Cryptography
•Quantum algorithms like Grover's algorithm can
significantly speed up searching in unsorted databases
Database search

16. Summary
AI Revolution Rapid advancements in Artificial Intelligence (AI) are disrupting
industries across the globe
Automation
and Job
Transformation
AI-driven automation is streamlining tasks, leading to increased
efficiency, cost savings, and enhanced productivity.
Personalization
and Customer
Experience
AI enables personalized experiences, tailored
recommendations, and improved customer service
Healthcare
Revolution
AI is poised to revolutionize healthcare with improved
diagnostics, drug discovery, and personalized treatment plans
Ethical and
Regulatory
Challenges
As AI becomes more pervasive, concerns about data privacy,
bias, and ethical implications emerge.
Limitless
Possibilities
The future holds immense potential for AI to transform
transportation, education, energy, and environmental sectors,
with autonomous vehicles

17. Questions