Definition

Artificial Intelligence (AI) is a branch of computer science concerned with creating systems capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, natural language understanding, and decision-making. AI spans a range of capabilities, from simple automation to complex autonomous reasoning and adaptation.

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Historical Timeline of AI

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Core Subfields of AI

1. Machine Learning (ML)

A method where computers learn patterns from data to make decisions without explicit programming.

• Supervised learning: Learns from labeled data (e.g., classification, regression).

• Unsupervised learning: Discovers structure in unlabeled data (e.g., clustering).

• Reinforcement learning: Learns via trial-and-error in dynamic environments (e.g., AlphaGo, robotics).

2. Deep Learning

A subfield of ML using deep neural networks with many layers:

• CNNs: Used for images.

• RNNs/LSTMs/Transformers: Used for sequences and language.

• Transformers (e.g., GPT, BERT): Now dominant across modalities.

3. Natural Language Processing (NLP)

AI’s ability to understand and generate human language.

• Examples: translation, summarization, sentiment analysis, chatbots, code generation.

4. Computer Vision

Enables machines to interpret visual data (images, video).

• Tasks: object detection, facial recognition, OCR, scene understanding.

5. Robotics

Integration of AI with mechanical systems to enable real-world action.

• Applications: industrial robots, autonomous vehicles, drones.

6. Planning & Reasoning

AI that simulates logic-based problem solving and decision-making.

• Techniques: symbolic AI, constraint solvers, knowledge graphs.

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AI Paradigms

Narrow AI (Weak AI)

• Specialized for a specific task.

• Examples: Alexa, Netflix recommender, self-driving software.

Artificial General Intelligence (AGI)

• Hypothetical AI with broad cognitive capabilities like a human.

• Still under research; subject of ethical and existential debate.

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Applications of AI by Industry

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Leading AI Companies and Labs

Technology Giants

• OpenAI – GPT series, DALL·E, Codex

• Google DeepMind – AlphaGo, AlphaFold, Gemini

• Microsoft AI – Copilot, Azure OpenAI integration

• Meta AI – LLaMA models, FAIR research

• Amazon – AWS AI tools, Alexa

• IBM – Watson, Project Debater

• NVIDIA – AI hardware and platforms, CUDA, generative AI SDKs

Startups

• Anthropic – Claude family of LLMs

• Cohere – Enterprise LLMs and retrieval-augmented generation (RAG)

• Mistral AI – Open-weight generative models

• xAI – Elon Musk’s AI company focused on “truthful” reasoning

• Runway ML – Creative tools for video, art, design using generative AI

Academic Institutions

• Stanford AI Lab (SAIL)

• MIT CSAIL

• UC Berkeley BAIR

• CMU Machine Learning Department

• Oxford and Cambridge AI research centers

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Key Technologies and Architectures

• Transformers: Dominant neural network architecture (Vaswani et al., 2017)

• Large Language Models (LLMs): GPT-4, Claude, PaLM, LLaMA

• Multimodal Models: Combine text, image, audio, and video (e.g., Gemini, GPT-4o)

• Reinforcement Learning from Human Feedback (RLHF): Aligns AI with human preferences

• Retrieval-Augmented Generation (RAG): Combines generative AI with external knowledge bases

• Neurosymbolic AI: Merges deep learning with symbolic reasoning

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AI Ethics and Policy

Concerns

• Bias and Discrimination: Trained on biased data, AI can reproduce and amplify societal inequalities.

• Privacy: Data-driven models may expose personal or sensitive information.

• Misinformation: Deepfakes, fake news generation, and AI-generated spam are rising threats.

• Autonomy & Control: Risks from misaligned agents or self-directed systems.

• Economic Impact: Potential for job displacement, especially in routine and clerical sectors.

Key Principles

• Fairness

• Transparency

• Accountability

• Explainability

• Safety and Robustness

• Human-centeredness

Regulatory Initiatives

• EU AI Act – Risk-based framework regulating AI use in the European Union.

• U.S. Executive Order on AI – Introduces safety, civil rights, and innovation directives.

• China AI Governance Guidelines – Emphasis on alignment with socialist values.

• OECD AI Principles – International standards for trustworthy AI.

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Future of AI

Trends

• Scaling Laws: Bigger models continue to outperform smaller ones, but costs rise.

• Model Compression: Techniques like quantization and pruning to deploy AI on edge devices.

• Synthetic Data: AI-generated data used to train or augment datasets.

• Autonomous Agents: AI systems capable of long-term planning, execution, and collaboration.

• Open-Source AI: Tools like Hugging Face, Mistral, and OpenLLM democratize access.

• AI + Robotics: Embodied intelligence merges digital and physical reasoning.

• Brain-Computer Interfaces (BCIs): Long-term integration of AI with neural activity.

AGI Outlook

• Experts remain divided on timeline and feasibility.

• Consensus: even narrow AI will profoundly impact every industry.

• AGI safety and alignment research becoming central.

AI on SemiWiki

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