To better understand this course, a solid definition of AI and its history is needed, and the following is what I gained from this weeks readings. From my understanding AI is any machine capable of interpreting data, potentially learning for the data, and use the knowledge to adapt and achieve specific goals. AI’s roots come from the history of computers which are machines that can reliably follow very simple instructions very, very quickly, and they can make decisions as long as those decisions are precisely specific. With that being said the question then posed is, can computers produce intelligent behavior simply by following lists of instructions like these? I think machine learning is a step in that direction but the issues that AI faces is twofold:

1) We have a recipe for the problem that works in principle, it doesn’t work in practice because it would require impossibly large amounts of computing time and memory. 

2) Or we have no real idea what a recipe for solving the problem might look like.

Because of these issues AI research is currently focused on first developing AGI the ability of computers to have the same intellectual capabilities as humans but isn’t concerned with issues such as consciousness or self-awareness (weak AI). AI research embodies various disciplines including physics, statistics, psychology, cognitive sciences, neuroscience, linguistics, computer science and electrical engineering, which is wild. From my understanding the furthest we’ve gotten to being close to weak AI is machine learning: constructing a program that fits the given data by creating a learning program that is a general template with modifiable parameters.

Combing Simon and Alpaydin’s work we see that machine learning is a requirement for AI and that it is based off the human brain. In fact all of AI takes inspiration from the brain hence the various disciplines involved in its advancement. Though Simon poses an interesting hypothesis that intelligence is the work of symbol systems, and by comparing the human brain to a computer system, both symbol systems in work, computers are therefore intelligent? The logic comes form the argument that logic is computation and since both the brain and computers work to compute data they are therefor intelligent. *please correct me if I’m wrong in this analysis.* I can see the reasoning behind this but I believe there are so many more gaps to fill, but with AI being so vast this could be a correct interpretation of weak AI or Narrow AI but what about the Grand Dream?

Having the understanding that computers work off of binary codes and require instructions and guidance to complete task, it seems improbable that the Grand Dream will come into fruition. Even machine learning requires some sort of template of instructions for the computer to go off of. I think with the amount of data available and continually growing computers can start inferring patterns and making predictions, but humans as much as human are predictable they are also unpredictable. Additionally, there are certain unwritten rules that govern relationships which is why maybe we can get to a point that computers can become indistinguishable from humans but can they pass the Winograd schemas? I think this ability to relate to humans on an emotional level is what will always prevent this sort of self-awareness in machines. 

Questions that I still have:

1. What is the difference between an algorithm and a program?
2. What is the difference between an artifact and a symbol?
3. How does electromagnetic energy convert to symbols ie binary codes?
   1. Specifically how did the Turing Machine work and why was that the foundation of computers?
4. Is cybernetics just another word for neural networking? 
5. Why is the divide between cybernetics and symbolic computing in AI so hotly debated? What really is the difference?

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