it is a presentation about how AI is overtaking the world. About how It involves the development of algorithms and models that enable machines to perform tasks that typically require human intelligence, such as problem-solving, learning from experience, recognizing patterns, and making decisions. AI technologies encompass various subfields, including machine learning, which involves training models on data to improve performance over time, and neural networks, which mimic the structure of the human brain to process information. AI applications are vast and range from voice assistants and self-driving cars to medical diagnosis and financial analysis.
3. AI has evolved from simple rule-
based systems to advanced
machine learning enabling it to
analyze vast amounts of data,
recognize patterns, and make
complex decisions.
With advancements in hardware, data
availability, and algorithmic techniques, AI
has become increasingly capable of
solving complex problems, enhancing
automation, and delivering personalized
experiences in various domains.
4. Narrow AI :
General AI :
Superintelligent AI :
Focused on specific tasks.
Human-level intelligence across various
domains.
Capable of surpassing human
intelligence.
5. • Virtual assistants (e.g., Siri, Alexa, Google Assistant)
• Recommendation systems (e.g., Netflix, Spotify)
• Natural language processing (e.g., chatbots)
• Autonomous vehicles
6. Disease
diagnosis and
prediction
AI enables disease diagnosis and
prediction by analyzing medical
data, including imaging, patient
information, and genomic data,
to identify patterns, assess risks,
and support clinical decision-
making.
Drug discovery
and
development
AI has revolutionized drug
discovery and development by
accelerating the process of
identifying potential drug
candidates, predicting their
effectiveness, optimizing
molecular structures, and
streamlining clinical trials.
Precision
medicine
AI plays a crucial role in
advancing precision medicine
by utilizing advanced algorithms
and computational power to
analyze vast amounts of patient
data, including genetic
information, medical records,
and environmental factors, in
order to personalize diagnoses,
treatments, and preventive
measures for individuals.
Robotic
surgery
Robotic surgery with AI
involves the use of
robotic systems
integrated with
artificial intelligence
algorithms to assist
surgeons in performing
minimally invasive
procedures with
enhanced precision,
control, and real-time
feedback.
7. • Fraud detection and prevention.
• Algorithmic trading.
• Risk assessment and
management.
• Customer service and chatbots.
8. Process
automation
AI in process automation involves
using artificial intelligence
technologies to automate and
optimize business processes,
improving efficiency and
productivity by analyzing data,
making intelligent decisions, and
performing tasks traditionally
done by humans.
Quality control
and inspection
AI improves quality control and
inspection processes through
automation, computer vision, and
machine learning techniques,
enhancing efficiency and accuracy
across various industries.
Predictive
maintenance
AI in predictive maintenance uses
machine learning and data
analysis to predict equipment
failures, optimize maintenance
schedules, and improve
operational efficiency by analyzing
sensor data and historical records.
Supply chain
optimization
AI is utilized in supply chain
optimization to enhance efficiency
and accuracy by analyzing data,
predicting demand, optimizing
inventory, improving logistics, and
facilitating decision-making
processes.
9. • Data quality and availability
• Algorithmic bias and interpretability
• Workforce implications and job
displacement
• Limited Contextual Understanding
• Lack of Common Sense Reasoning
• Scalability and Resource Intensiveness
• Ethical and Social Implications
• Human-AI Collaboration
• Limited Creativity and Intuition
• Reliance on Historical Data
10. • Advancements in natural language
processing and understanding
• Explainable AI and ethical
frameworks
• Integration of AI with Internet of
Things (IoT) and other technologies
11. • Data quality and availability
• Algorithmic bias and interpretability
• Job displacement and unemployment
• Bias and discrimination in decision-making
• Lack of transparency and interpretability
• Privacy and security risks
• Overreliance and dependency
• Ethical dilemmas and accountability
• High costs and infrastructure requirements
• Potential for job polarization and skill gaps.
13. Recap the transformative
potential of AI across industries
Emphasize the importance of
responsible and ethical AI
development and deployment
14. Google AI Blog
(ai.googleblog.com)
MIT Technology Review: Artificial
Intelligence section
AI TrendsTowards Data Science
(towardsdatascience.com)
OpenAI Blog (openai.com/blog)
