54: Lazy Sunday, HarvardX: introduction to AI.

2023-01-08

  • After yesterday’s fun, and not getting home until gone 01.00, we had a very quiet day today.
  • Our sole focus for the day was a new 1000-piece jigsaw puzzle, which, sadly, we didn’t finish by the time we stopped at 22.30.
  • Apart from that, I finished the first lecture of HarvardX’s CS50 introduction to artificial intelligence with Python.
  • The course is one of Harvard’s free lecture series released under the HarvardX brand and is, so far, a great introduction to the fundamentals of AI.
  • The first lecture is on search, where we learn about search problems.
  • We were introduced to several different search algorithms
  • Uninformed search is a general-purpose search algorithm which operates in a brute force way as it does not have additional information about the state other than how to traverse the tree (so it is also called blind search).
  • Depth-first Search. The search algorithm will follow the nodes to the deepest possible depth before evaluating the next available path.
  • Breath-first Search: The search algorithm will look at all child nodes before progress to the next level of the tree.
  • Informed search is a search algorithm that knows its end state and uses that information to estimate how close it is to its goal.
  • We learn about greedy best-first search and A* search.
  • Then we were introduced to adversarial search problems (think Tic-tac-toe), where we were introduced to the following algorithm
  • Minmax – we calculate all possible moves and assign a value. Player 1 will try to minimise their score to win, and player 2 will try to maximise thier score to win.
  • Alpha-beta pruning is where we calculate enough of the subsequent moves to see what has the highest or lowest score (depending on if you are minimising or maximising) before making a move.
  • Depth-limited Minmax where (in more complex games) we only calculate our next moves up to a limited depth (10 moves) and assign a probability that a move will be winning.
  • It is a well-put-together lecture, as you’d expect, and the content was new to me, which was awesome.
  • Next, we’ll learn how to extract knowledge from a body of text.

  • Reading: Agency by William Gibson (100% Complete)
  • Wordle: 568 0/6

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