Learn how to see if something must be true and how to make connections based on truth.
- You might be wondering why machine learning…took so long to catch on.…After all, it was in 1959 when Arthur Samuel…created his revolutionary checkers program.…At the time it seemed like machine learning…had the wind at it's back.…It was ready to become the dominant form…of artificial intelligence.…Yet what actually happened is that…machine learning took a backseat to other innovations.…Such as symbolic systems.…It wasn't until the late 1980's and early 1990's…when researchers started thinking again…about machine learning.…
The rise and fall, and rise again of machine learning…is both sad and interesting.…It shows how just a few researchers were instrumental…in building out early AI.…Back in 1958 a Cornell professor named Frank Rosenblatt…created an early version of an artificial neural network.…Except instead of using nodes and neurons…he used the term Perceptrons for his network.…He argued that if you tied together…enough of these Perceptrons,…you could create a complex form of machine intelligence.…
Rosenblatt thought that these Perceptrons…
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