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Optimizing Life: Lessons from AI Search Approaches

· 3 min read

Life can often feel like a journey towards a specific goal, be it happiness, wealth, expertise, or fame. And just like in artificial intelligence, achieving our goals can be seen as an optimization problem. But what if we thought of life itself as an AI search problem? What lessons could we learn from the various search approaches used by computer scientists and mathematicians?

Step 1: Define your goal

The first step in any AI search problem is defining the goal. The same applies to life. We need to know when to stop and have a clear understanding of what success looks like for us. This could be a specific career, a happy family, or simply a sense of inner peace.

Step 2: Explore solutions

There are several search approaches that we could take to reach our goals. The first and most basic approach is to make random choices. While this may find an optimal solution, it's unlikely and not a practical approach to life.

Another approach is to use a depth-first search. This is where we pursue something until its natural end and then change course, no matter how dramatic. This approach is straightforward, but it may not lead to the best solution.

A breadth-first search involves exploring many avenues, dipping your toe into many things, and exploring all options. This approach may lead to more opportunities and a wider range of experiences, but it can also be time-consuming and may not lead to the best solution.

A branch and bound approach involves exploring avenues that have a reasonable chance of leading to good results. This approach is more practical, as it saves time by avoiding avenues that are unlikely to lead to the desired outcome.

However, there is a time restriction in life that doesn't apply in AI search problems - death. Our life span is a constraint on the time we have to search for the best solution. This means that we need to consider the worst-case scenario when choosing an approach.

Step 3: Use heuristics

Heuristics are problem-solving techniques that are practical and time-saving, but not guaranteed to lead to the best solution. In life, we can use heuristics such as hill climbing, which involves making small changes to our routine and seeing if it improves our situation. However, we need to be careful not to get stuck in a local minimum.

Another heuristic is saving progress and returning to partial solutions. This is similar to taking breaks and revisiting a problem with fresh eyes. We can also embrace chance and random opportunities, like mutations in genetic algorithms, which can lead to unexpected breakthroughs.

However, convergence is a concern with these heuristics. It's important to remember that the optimal solution may not be reachable, and we need to be open to alternative solutions.

Final thoughts

In life, we are all versions of ourselves trying to solve slightly different problems, and our channels of communication are often flawed. We need to consider how our lives fit into the context of other lives and be aware of external factors that can force changes in our plans.

Life is an optimization problem, and we can learn valuable lessons from AI search approaches. Defining our goals, exploring solutions, and using heuristics can help us find the best path towards our desired outcome. Ultimately, life is a journey, and the search for the best solution is part of the adventure.