Lecture 5 - AI and ML-AK

Overview of the Lecture

  • Instructor: Gojo Lamperouge

  • Total Questions: 57

  • Worksheet Time: 29 minutes

  • Class Date: 2/9/24

  • Focus: Artificial Intelligence (AI) and Machine Learning (ML)

Key Topics Covered

Popular AI Systems

  • Search Engines (e.g., Google, Bing)

  • Voice Assistants (e.g., Google Assistant, Siri, Alexa)

  • Conversational AI (ChatBots) (e.g., ChatGPT, Bard, Bing Chat)

  • Self-Driving Cars

Understanding AI

  • Definition of AI: Machines/software performing tasks requiring human-like intelligence.

  • Types of AI:

    • Weak AI (Narrow AI): Performs specific tasks well but lacks general intelligence.

    • Strong AI (AGI): Hypothetical intelligence that can perform any intellectual task a human can.

AI Systems Explained

Search Engines

  • Understand natural language queries and provide relevant results.

  • Learn from user behavior to enhance search experience.

Voice Assistants

  • Capabilities include answering questions, setting reminders, and controlling smart devices.

Conversational AI (ChatBots)

  • Examples: ChatGPT, Bard, Bing Chat

  • Based on large language models (LLMs) that generate text and perform tasks such as:

    • Language translation

    • Creative content generation

    • Text-based information answering

  • Notable Fact: ChatGPT was trained on 45TB of text data.

Self-Driving Cars

  • Operate independently using AI technology.

  • Equipped with sensors like cameras, radar, and lidar.

  • Have potential to enhance road safety and improve traffic flow.

AI Methodologies and Concepts

Turing Test

  • Definition: A test to evaluate a machine's ability to exhibit intelligent behavior equivalent to that of a human.

  • Proposed by Alan Turing in 1950.

Rationality in AI

  • AI systems maximize expected utility, guided by predefined criteria for success.

  • Actions are taken based on inputs from the environment through sensors and actuators.

Limitations and Challenges of AI

  • Current Limitations include:

    • Repetition and incoherence in outputs.

    • Factual inaccuracies.

    • Lack of contextual understanding in generated outputs.

    • Inherited biases from training data.

Ethical Considerations

  • Issues like algorithmic bias, privacy concerns, job displacement, and the need for transparency in AI development.

Future of AI

  • Growth in areas such as deep learning, natural language processing, and computer vision.

  • Application prospects across multiple industries including healthcare, finance, and transportation.

Discussion Points

  • Societal implications of strong AI.

  • Ensuring ethical AI development.

  • The future relationship between humans and AI.

  • Potential for achieving AGI.