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Exploring Intelligence and AI Development
Sep 4, 2024
Lecture Notes on Intelligence and AI
Introduction to Intelligence and AI
Intelligence has been a key problem for humanity over 2000 years.
The term "artificial intelligence" was coined at a conference in 1962 by John McCarthy and Marvin Minsky.
AI has seen significant progress, especially in the last 20 years, driven primarily by computer science and common sense.
Importance of Interdisciplinary Collaboration
Understanding intelligence requires expertise beyond just computer science.
Collaboration involves disciplines like:
Neuroscience
Cognitive Science
Various institutions (e.g., MIT and Harvard)
Milestones in AI Development
Deep Blue
: IBM's chess-playing computer that defeated Garry Kasparov.
Watson
: Beat champions in the quiz show Jeopardy.
Drones
: Demonstrated ability to land on aircraft carriers.
DeepMind's AI
: Learned to play 49 classical Atari games better than humans.
Mobile AI Systems
: Capable of visual recognition and responding to surroundings.
Current State of AI
The field is experiencing a golden age for intelligent applications.
Great opportunities for innovation and profit in AI.
However, there's a gap in understanding how humans interact with images and process visual information.
Research Focus Areas
Investigating how the brain answers questions about images:
What is present in the image?
Actions or thoughts of individuals depicted?
Storytelling based on visual input.
Aim: Develop systems that mimic human cognitive processes while answering these questions.
Turing Test and Beyond
The Turing test is not sufficient; researchers aim for a deeper understanding of intelligence.
Turing Plus Plus Questions
: An exploration of intelligence that goes beyond simply passing the Turing test.
Defining Human Intelligence
There is no universally accepted definition of intelligence due to its diverse forms.
Focus: Understanding human intelligence through:
Neuroscience
Computational models
Psychophysical responses
Research Directions
Current focus at the center is on vision-related tasks.
Goals include:
Understanding computational, psychophysical, and neural levels of visual processing.
Developing models for face recognition:
Utilizing findings from monkey studies and human fMRI to inform AI models.
Challenges remain in studying more complex cognitive tasks like storytelling.
Conclusion
Research in AI and intelligence is ambitious and multifaceted.
Continued interdisciplinary collaboration is essential to advance understanding and technology.
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