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ramaling145 karma

Modern day AI learns extensively from data. If the data used to train AI systems is not of good quality, the decisions made by the AI systems may not be accurate. In the good old days, AI relied on domain knowledge. We need to integrate domain knowledge and data so that the decisions are more accurate. The other issue I worry about is the vulnerability of AI systems to adversarial attacks. Researchers are actively working on these issues. Like any technology, AI will slowly and surely get better.

ramaling119 karma

The technology has been there since the late sixties. As you may now, the term AI was coined at the 1956 summer workshop at Dartmouth. I took my first class on AI in spring 1978. As I said before, current AI systems are trained; older systems relied on domain knowledge.

ramaling117 karma

The book by Russell and Norvig is a good one to read. There are many short courses on deep learning, AI and ML that you can benefit from. Youtube has lot of lectures at various levels.

ramaling114 karma

AI has evolved over the decades. In the early years, AI was driven by domain knowledge. Now AI systems are designed using data.

The deep learning approach that is driving AI these days, basically fits a non-linear hierarchical regression model to data. This approach and new variations are giving very good results on many applications of AI (computer vision, NLP, robotics, etc).

When domain knowledge is integrated into data-driven methods, we can comfortable be in the AI world again!

ramaling111 karma

A great question!

when I studied AI in the late seventies, we were quite happy if AI played games (checkers, chess, etc) well. Although speech recognition was at its infancy in the seventies, we could imagine an AI system that could hold a decent conversation. Data did not play much of a role then. what surprises me is how the data-driven AI contributed to the reemergence of AI. This has to with having powerful computers and sensors that collect data.