The Evolution of Artificial Intelligence (AI)

Introduction

• AI is a transformative technology that can be helpful or intimidating.
• Relevant for students, professionals, and curious individuals.

Perception of AI

• Science fiction has shaped AI perceptions:
  • Positive examples: Bicentennial Man, Baymax
  • Negative examples: 2001: A Space Odyssey, I Robot, The Matrix, Avengers: Age of Ultron
• Common fear: AI becoming smarter than humans and taking control.

Historical Background

• Term Origin: Introduced by John McCarthy in the 1950s.
  • Defined as tasks requiring intelligence if performed by humans.
• Key figures:
  • Alan Turing: Developed the Turing Test, foundational for AI.
  • Helped decipher the Enigma code in WWII.
  • Inspired the film The Imitation Game.

Dartmouth Workshop

• 1956 Dartmouth Workshop: Considered the birthplace of AI.
  • Focused on problem-solving programs (e.g., checkers, English language).

Ancient Roots of AI

• AI concepts can be traced back to ancient myths (e.g., automatons and golems).

Symbolic AI Phase (1960s-1970s)

• Emphasis on neural networks and cognitive processes.
• Development of expert systems.
• Eliza: An early chatbot simulating psychotherapist conversations (1966).

AI Winter (1970s-80s)

• Period of decreased funding and interest due to unrealized goals.
• Shift towards connectionist models and neural networks.

Landmark Events

• 1997: IBM's Deep Blue defeated chess champion Garry Kasparov.

Resurgence in AI Research

• 2000s brought increased computing power and data availability.
• Success with convolutional and recurrent neural networks.

Deep Learning Era

• Machines began learning from data with minimal human intervention.
• Deep learning allows machines to think creatively within set parameters.
• Current applications include Narrow AI (specific task execution).

Transformers in AI

• Introduced in 2017 by Google Brain.
• Improved accuracy in language processing and translations.
• Examples: BERT, GPT, and Chat GPT.

Applications of AI

• Used for document summarization, fraud detection, healthcare, and more.
• Mid Journey: AI generating art and involved in legal discussions on ethics and IP rights.

Societal Impact and Concerns

• Rapid AI advancement raising ethical questions and job displacement concerns (20 million jobs by 2030).
• Potential for superintelligence within 60 years.
• Concerns from figures like Elon Musk regarding AI regulation and risks.

Misalignment of AI Goals

• Fear of AI being used for harmful purposes by individuals with bad intentions.
• Historical example: Microsoft's Tay chatbot learning offensive content in 2016.

Conclusion

• Caution is needed as AI provides crucial services and may benefit humanity if regulated properly.