In this video, put AI into context and learn about the original challenges and goals with AI.
- Early artificial intelligence was a mix…of ambition and self-discipline.…Some scientists were quick to over promise…what could be done with early machines.…At the same time, you could see the potential…in these machines to solve complex problems.…In 1956, you had one of the first attempts…to create a machine with general intelligence.…Allen Newell and Herbert Simon created a computer program…they called the General Problem Solver.…This program was designed to solve any problem…that could be presented as mathematical formulas.…
One of the key parts of the General Problem Solver…was what Newell and Simon called…the physical symbol system hypothesis.…In their paper, they argued that symbols…were the key to general intelligence.…If you could get a program to connect…enough of these symbols, then you would have…an intelligent machine.…Symbols are a very big part of how you interact…with the world.…When you see a stop sign, you know to look for traffic.…When you see the letter a, you know that the word…will make a certain sound.…
This course will introduce you to some of the key concepts behind artificial intelligence, including the differences between "strong" and "weak" AI. You'll see how AI has created questions around what it means to be intelligent and how much trust we should put in machines. Instructor Doug Rose explains the different approaches to AI, including machine learning and deep learning, and the practical uses for new AI-enhanced technologies. Plus, learn how to integrate AI with other technology, such as big data, and avoid some common pitfalls associated with programming AI.
- The history of AI
- Machine learning
- Technical approaches to AI
- AI in robotics
- Integrating AI with big data
- Avoiding pitfalls