# Understanding Artificial Intelligence through Algorithmic Information Theory

Artificial Intelligence is more than just a collection of brilliant, innovative methods to solve problems.

If you are interested in machine learning or are planning to explore it, the course will make you see artificial learning in an entirely new way. You will know how to formulate optimal hypotheses for a learning task. And you will be able to analyze learning techniques such as clustering or neural networks as just ways of compressing information.

If you are interested in reasoning , you will understand that reasoning by analogy, reasoning by induction, explaining, proving, etc. are all alike; they all amount to providing more compact descriptions of situations.

If you are interested in mathematics , you will be amazed at the fact that crucial notions such as probability and randomness can be redefined in terms of algorithmic information. You will also understand that there are theoretical limits to what artificial intelligence can do.

If you are interested in human intelligence , you will find some intriguing results in this course. Thanks to algorithmic information, notions such as unexpectedness, interest and, to a certain extent, aesthetics, can be formally defined and computed, and this may change your views on what artificial intelligence can achieve in the future.

Half a century ago, three mathematicians made the same discovery independently. They understood that the concept of information belonged to computer science; that computer science could say what information means. Algorithmic Information Theory was born.

Algorithmic Information is what is left when all redundancy has been removed. This makes sense, as redundant content cannot add any useful information. Removing redundancy to extract meaningful information is something computer scientists are good at doing.

Algorithmic information is a great conceptual tool. It describes what artificial intelligence actually does , and what it should do to make optimal choices. It also says what artificial intelligence can’t do. Algorithmic information is an essential component in the theoretical foundations of AI.

What you'll learn

- You will be able to see machine learning, reasoning, mathematics, and even human intelligence as abstract computations aiming at compressing information.
- This new power of yours will not only help you understand what AI does (or can’t do!) but also serve as a guide to design AI systems.
- what a convex curve looks like,
- that log(7^n) is
- times log(7)
- that rational numbers have finite or periodic expansion,
- that rational numbers are countable, but that real numbers are not,
- that the probability of "A and B" is the probability of "A knowing B" times the probability of B,
- that 65 is 1000001 is in base 2 and 41 in base 16,
- how to compute the sum of a finite geometric series,
- that {'a':1, 'i':0} is a Python dictionary and why list('ab'*4)[::2] yields ['a','a','a','a'],
- that k-means is a clustering method,
- what Bayes’ theorem tells us,
- how Shannon’s information is related to probability,
- that what is called a
- is NOT the machine that Alan Turing (Benedict Cumberbatch)
- is using in the movie
- .
- is more than just a collection of brilliant, innovative methods to solve problems.
- If you are interested in
- or are planning to explore it, the course will make you see artificial learning in an entirely new way. You will know how to formulate optimal hypotheses for a learning task. And you will be able to analyze learning techniques such as clustering or neural networks as just ways of compressing information.
- If you are interested in
- , you will understand that reasoning by analogy, reasoning by induction, explaining, proving, etc. are all alike; they all amount to providing more compact descriptions of situations.
- If you are interested in
- , you will be amazed at the fact that crucial notions such as probability and randomness can be redefined in terms of algorithmic information. You will also understand that there are theoretical limits to what artificial intelligence can do.
- If you are interested in
- , you will find some intriguing results in this course. Thanks to algorithmic information, notions such as unexpectedness, interest and, to a certain extent, aesthetics, can be formally defined and computed, and this may change your views on what artificial intelligence can achieve in the future.
- Complexity as code length
- Conditional Complexity
- Complexity and frequency
- Meaning distance
- Algorithmic probability, Randomness
- Gödel’s theorem
- Universal induction - MDL
- Analogy & Machine Learning as complexity minimization
- Simplicity & coincidences
- Subjective probability
- Relevance

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Rating | Not enough ratings |
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Length | 5 weeks |

Effort | 4 - 8 hours per week |

Starts | On Demand (Start anytime) |

Cost | $49 |

From | edX |

Instructor | Jean-Louis Dessalles |

Download Videos | On all desktop and mobile devices |

Language | English |

Subjects | Programming |

Tags | Computer Science |

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## Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Algorithmic Execution Desk Support $67k

Information Resources $69k

Information Security 1 $72k

Algorithmic Trading Support $73k

Information Service $73k

Information Artist $77k

Equity Algorithmic Quant Analyst $83k

Information Engineer 3 $92k

Algorithmic Trading Developer - C++ $104k

Information Coordinator 3 $105k

Algorithmic Software Engineer $111k

Information Architect 2 $118k

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Rating | Not enough ratings |
---|---|

Length | 5 weeks |

Effort | 4 - 8 hours per week |

Starts | On Demand (Start anytime) |

Cost | $49 |

From | edX |

Instructor | Jean-Louis Dessalles |

Download Videos | On all desktop and mobile devices |

Language | English |

Subjects | Programming |

Tags | Computer Science |

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