Lecture 28: The Future of AI

Lecture 28: The Future of AI¶

AIMA Chapter 28 — 1 hour¶

Learning Objectives¶

• Synthesize AI components: perception, reasoning, learning

• Consider paths to artificial general intelligence

• Discuss AI engineering and deployment

• Reflect on societal impact

AI Components¶

• Perception: Sensors, vision, language

• Representation: State, knowledge

• Reasoning: Logic, probability

• Action: Planning, control

• Learning: From data, from experience

Integration¶

• Modular: Separate components

• End-to-end: Learn jointly

• Hybrid: Symbolic + neural

General AI¶

• Narrow AI: Specific tasks

• AGI: Human-level across domains

• Timeline: Uncertain

• Approaches: Scale, architecture, new paradigms

AI Engineering¶

• Deployment: Production systems

• Monitoring: Drift, performance

• Maintenance: Updates, retraining

Societal Impact¶

• Benefits: Healthcare, education, science

• Risks: Job loss, misuse, existential

• Governance: Regulation, standards

Course Summary¶

• Part I: Agents, search

• Part II: Logic, knowledge

• Part III: Uncertainty, probability

• Part IV: Learning

• Part V: NLP, vision, robotics

• Part VI: Ethics, future

Thank You!¶

• Textbook: Russell & Norvig, AIMA 4e

• Resources: aima.cs.berkeley.edu

• Exercises: aimacode.github.io/aima-exercises/

• Code: github.com/aimacode

Questions?¶

End of AIMA Lecture Series