ARTIFICIAL INTELLIGENCE (AI) – ADVANCED NOTES
(BY BRAJESH KATARA)
1. DEFINITION & SCOPE
Artificial Intelligence (AI) refers to the development of computational systems capable of
simulating human cognitive processes such as analytical reasoning, decision-making,
perception, pattern recognition, and language interpretation.
Types of AI:
- Narrow AI (Weak AI): Focused on executing specific tasks with high accuracy (e.g., virtual
assistants, diagnostic AI).
- General AI (Strong AI): Theoretical systems with the capability to perform diverse tasks at
human intelligence levels.
- Superintelligent AI: A future concept where machines could exceed human intelligence
and independently evolve.
2. CORE SUBFIELDS OF AI
(a). Machine Learning (ML): Statistical models that adapt and improve from data
(supervised, unsupervised, semi-supervised, reinforcement learning).
(b). Deep Learning (DL): Multilayered neural networks capable of autonomously extracting
features from massive datasets (CNN, RNN, Transformer architectures).
(c). Natural Language Processing (NLP): Computational methods for understanding and
generating human language using semantic and syntactic models.
(d). Computer Vision: AI models that interpret and analyze visual data such as images, 3D
scans, and videos.
(e). Robotics & Autonomous Systems: Intelligent control frameworks enabling real-world
interaction and autonomous decision-making in machines.
(f). Expert & Knowledge-Based Systems: Systems built on domain-specific knowledge
bases for decision automation.
3. KEY ADVANCED CONCEPTS
(a). Generative AI: Advanced algorithms capable of synthesizing original outputs such as
high-fidelity images, videos, text, and code (e.g., Diffusion models, GPT-series).
(b). Transformer-Based Architectures: Large-scale self-attention models for handling
sequential and multimodal data (BERT, LLaMA, GPT-4-class models).
(c). Multi-Agent Intelligence: Networks of autonomous agents collaborating or competing
to achieve complex objectives.
(d). Explainable AI (XAI): AI design methodologies focused on interpretability and
transparent decision-making processes.
(e). Edge AI: Running low-latency AI models directly on end devices or edge servers
without dependence on centralized cloud infrastructure.
(f). Neuromorphic Engineering: Hardware emulation of neural and synaptic architectures
to enable energy-efficient AI computations.
4. MODERN APPLICATIONS
- Healthcare Informatics: AI-assisted medical imaging, personalized treatment strategies,
drug discovery pipelines, genomics analytics.
- Financial Technology (FinTech): Real-time fraud prevention, algorithmic trading, credit
risk modeling, robo-advisors.
- Autonomous Mobility: Self-navigating vehicles, UAVs, and robotics in logistics and
defense.
(BY BRAJESH KATARA)
1. DEFINITION & SCOPE
Artificial Intelligence (AI) refers to the development of computational systems capable of
simulating human cognitive processes such as analytical reasoning, decision-making,
perception, pattern recognition, and language interpretation.
Types of AI:
- Narrow AI (Weak AI): Focused on executing specific tasks with high accuracy (e.g., virtual
assistants, diagnostic AI).
- General AI (Strong AI): Theoretical systems with the capability to perform diverse tasks at
human intelligence levels.
- Superintelligent AI: A future concept where machines could exceed human intelligence
and independently evolve.
2. CORE SUBFIELDS OF AI
(a). Machine Learning (ML): Statistical models that adapt and improve from data
(supervised, unsupervised, semi-supervised, reinforcement learning).
(b). Deep Learning (DL): Multilayered neural networks capable of autonomously extracting
features from massive datasets (CNN, RNN, Transformer architectures).
(c). Natural Language Processing (NLP): Computational methods for understanding and
generating human language using semantic and syntactic models.
(d). Computer Vision: AI models that interpret and analyze visual data such as images, 3D
scans, and videos.
(e). Robotics & Autonomous Systems: Intelligent control frameworks enabling real-world
interaction and autonomous decision-making in machines.
(f). Expert & Knowledge-Based Systems: Systems built on domain-specific knowledge
bases for decision automation.
3. KEY ADVANCED CONCEPTS
(a). Generative AI: Advanced algorithms capable of synthesizing original outputs such as
high-fidelity images, videos, text, and code (e.g., Diffusion models, GPT-series).
(b). Transformer-Based Architectures: Large-scale self-attention models for handling
sequential and multimodal data (BERT, LLaMA, GPT-4-class models).
(c). Multi-Agent Intelligence: Networks of autonomous agents collaborating or competing
to achieve complex objectives.
(d). Explainable AI (XAI): AI design methodologies focused on interpretability and
transparent decision-making processes.
(e). Edge AI: Running low-latency AI models directly on end devices or edge servers
without dependence on centralized cloud infrastructure.
(f). Neuromorphic Engineering: Hardware emulation of neural and synaptic architectures
to enable energy-efficient AI computations.
4. MODERN APPLICATIONS
- Healthcare Informatics: AI-assisted medical imaging, personalized treatment strategies,
drug discovery pipelines, genomics analytics.
- Financial Technology (FinTech): Real-time fraud prevention, algorithmic trading, credit
risk modeling, robo-advisors.
- Autonomous Mobility: Self-navigating vehicles, UAVs, and robotics in logistics and
defense.
