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Artificial Intelligence and Machine Learning

University of Michigan, Ann Arbor 1002
(University of Michigan, Ann Arbor)

Artificial Intelligence (AI)  -  Human Intelligence Exhibited by Machines

AI is a flourishing and exciting field: everyone can contribute.

 

 

AI is a broad field with a long history. It went through ups and downs, successes and failures, optimism and disappointment, big enthusiasm with large funding, and then cutting funding and so on and so forth. AI is now maturing. Today, as data will drive future discoveries and alleviate the complexity of AI.

Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) - images, text, transactions, mapping data, you name it.

The most important thing to understand about AI is that it is not a static formula to solve. It’s a constantly evolving system designed to identify, sort, and present the data that is most likely to meet the needs of users at that specific time, based on a multitude of variables that go far beyond just a simple keyword phrase. 

AI is trained by using known data, such as: content, links, user behavior, trust, citations, patterns, and then analyzing that data using user experience, big data, and machine learning to develop new ranking factors capable of producing the results most likely to meet user needs.

 

The Foundation of AI

 

AI is an interdisciplinary field and several other disciplines have contributed to its progress and this includes mathematics, economics, linguistics, neuroscience, control theory and cybernetics, psychology, computer engineering, and finally philosophy. Given how broad the field is, it won't be possible to go every single contribution. However, let's go through the main concept introduced by each of these disciplines.

  • With no surprise philosophers were the first AI contributors. They formulated ideas for AI, starting with Aristotle in 400 BC. They considered the mind as a machine, our physical system operating as a set of logical rules. There have been different philosophy movements including rationalism, dualism, materialism, empiricism, induction, etc. 
  • Mathematicians provided the tools to formalize and manipulate logic. They also worked out the details of propositional logic and first order logic. Mathematics also lead the ground for algorithms for logical deduction to draw valid conclusions. Finally mathematicians also contributed with the theory of probability invaluable to help deal with uncertainty in the real world.
  • Economists provided the formal theory of rational decisions to maximize what they call payoff or utility. They combine decision theory and probability theory for decision making and uncertainly. They also address game theory in which an agent is planning to maximize its utility in the presence of an opponent who is aiming or planning against him. Economists also formalize mark of decision processes as a class of sequential decision problems with the mark of property.
  • Neuroscience contributed to AI progress by addressing how brain functions and how brains and computers are similar or dissimilar. A good progress has been made so far in understanding how the brain functions. And we could expect more involvement in AI in the next decades or so.
  • Psychologists care about how we think and act. Cognitive psychology specifically perceives the brain as an information processing machine. It lead to the development of the field of cognitive science.
  • Computer engineering cares about how to build powerful machines to make AI possible. For example although the idea of self driving cars or autonomous driving has been there for decades, it's became only possible today thanks to advances in computer engineering.
  • Control theory and cybernetics aim to design simple optimal agents receiving feedback from the environment. Today modern control theory design systems that maximize
    an objective functions over time, which gets AI and control theory today closer disciplines than ever.
  • Linguistics cares about how our languages and thinking related. And today modern linguistics and AI format we call computational linguistics or natural language processing which is a very important piece in natural language understanding in AI.

 

[More to come ...]



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