Today i’d like to give a flash about an Artificial Intelligence concept wich is, in my opinion, very Zen.
Say you’re on the top of the hill (or near it), but if you can’t afford it, cope with it, it’s like watching tv on your sofa while a fire is burning down the house.
Well, maybe you are likely to be on the wrong hill!
Think about Steve Jobs’ Commencement address delivered on June 12, 2005.
‘You can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future. You have to trust in something — your gut, destiny, life, karma, whatever. This approach has never let me down, and it has made all the difference in my life‘
In computer science, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is relatively simple to implement, making it a popular first choice. Although more advanced algorithms may give better results, in some situations hill climbing works just as well.
Hill climbing can be used to solve problems that have many solutions, some of which are better than others. It starts with a random (potentially poor) solution, and iteratively makes small changes to the solution, each time improving it a little. When the algorithm cannot see any improvement anymore, it terminates. Ideally, at that point the current solution is close to optimal, but it is not guaranteed that hill climbing will ever come close to the optimal solution.
For example, hill climbing can be applied to the traveling salesman problem. It is easy to find a solution that visits all the cities but will be very poor compared to the optimal solution. The algorithm starts with such a solution and makes small improvements to it, such as switching the order in which two cities are visited. Eventually, a much better route is obtained. Hill climbing is used widely in artificial intelligence, for reaching a goal state from a starting node. Choice of next node and starting node can be varied to give a list of related algorithms. A problem with hill climbing is that it will find only local maxima. Unless the heuristic is convex, it may not reach a global maximum.
At any given moment, you take a step in the direction that takes you higher. The risk with this method is if you happen to start near the lower hill, you’ll end up at the top of that lower hill, not the top of the tallest hill.
So Stay Hungry. Stay Foolish.
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