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Can we characterize intelligent behavior?
Are there theoretical foundations on which Artificial Intelligence can be grounded?
This course on Algorithmic Information will offer you such a theoretical framework.
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.
Keywords:
Algorithmic information, Kolmogorov complexity, theoretical computer science, universal Turing machine, coding, compression, semantic distance, Zipf’s law, probability theory, algorithmic probability, computability, incomputability, random sequences, incompleteness theorem, machine learning, Occam's razor, minimum description length, induction, cognitive science, relevance.
Examples of what you should know before embarking on this course
How to measure information through compression
How to compare algorithmic information with Shannon’s information
How to detect languages through joint compression
How to use the Web to compute meaning similarity
How probability and randomness can be defined in purely algorithmic terms
How algorithmic information sets limits to the power of AI (Gödel’s theorem)
A criterion to make optimal hypotheses in learning tasks
A method to solve analogies and detect anomalies
A new understanding of machine learning as a way to achieve compression
Why unexpected means abnormally simple
Why coincidences are unexpected
Why subjective information and interest are due to complexity drop and why relevance, aesthetics, emotional intensity and humour rely on coding.
Caveat: This course DOES NOT address the notion of "computational complexity" which measures the speed of algorithms.
Chapter 1. Describing data
Chapter 2. Measuring Information
Chapter 3. Algorithmic information & mathematics
Chapter 4. Machine Learning and Algorithmic Information
Chapter 5. Subjective information
"The cognitive part was really exciting..." (Antoine, 2021, betatester)
"Yes, the overall course was very interesting!" (Joachim, 2021, betatester)
"A very interesting course that definitely changed how I see the world. Thank you so muchhh." (Hanady, 2021, betatester)
Do I need to be highly proficient in Python?
No. Basic knowledge of Python is sufficient. From time to time, you’ll have to use short Python programs, to understand them locally and to perform easy transformations.
I am feeling a bit rusty in maths. Is it a problem?
You need to feel comfortable with math concepts such as the ones listed above. If you are not sure about some of them ("oh, I knew that...!"), but are ready to refresh your memory using external Web resources such as Wikipedia, then make an attempt and visit the course. You will probably get most of it. Anyway, this is not a math course!
I already know a lot about Algorithmic Information. Will I learn something new?
Yes, definitely. This course includes original content. You may know mathematical aspects of AIT, but not its applications. Or the converse, you know for instance how to apply "MDL", but may not be fully aware of the underlying theoretical motivations. And you will be amazed to see how AIT applies to human intelligence!
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
