Search engines have become an integral part of our daily lives and are one of the most important tools for the world's billions of internet users. According to Netmarketshare, Statista, and StatCounter statistics, Google, Bing, Yahoo, Baidu, and Yandex are the top five search engines in the world in terms of market share. Modern search engines heavily use AI, and understanding how AI works in search can help you rank your own website better. Everything in search is controlled by AI, from the search results you see to the related topics you're encouraged to look into further. Today, a modern search engine would be impossible to run without sophisticated AI.

Many AI problems can theoretically be solved by searching through a large number of possible solutions: For example, logical proof can be thought of as tracing a path from premises to conclusions, with each step involving the application of an inference rule. In a process known as means-ends analysis, planning algorithms search through trees of goals and subgoals in an attempt to find a path to a target goal.

How to solve the search problem?

In AI, finding a solution to a problem is frequently viewed as a quest through the universe of possible outcomes.

A search problem consists of the following:

• A State Space is a collection of all possible states that you could be in.
• A Start State: The state in which the search is initiated.
• A Test of Objectives: True or false- A function that looks at the current state and sees if it is the goal state returns true or false based on whether it is.

A search problem's solution is a series of actions, referred to as the plan, that transform the start state into the goal state.

• This objective is accomplished through the use of search algorithms.

How is AI used in Search?

Almost everything about how and what search results you see is controlled by AI. And today, a modern search engine can't function without advanced AI. The 1990s saw the emergence of a new type of search based on mathematical optimization. For many problems, it is possible to begin with an educated guess and gradually refine it until no further refinement is possible. These algorithms are similar to blind hill climbing: we begin at a random point on the landscape and work our way uphill in leaps or steps until we reach the summit. 

Search techniques: 

• Simulated annealing, 
• Beam search, and 
• Random optimization

Optimization search is a technique used in evolutionary computing. Then they allow the population to mutate and recombine, with each generation selecting only the fittest individuals to survive (refining the guesses). Likewise, classic evolutionary algorithms include genetic algorithms and genetic programming. Algorithms based on swarm intelligence can also be used to coordinate distributed search processes. 

In search, two swarm algorithms are frequently used: 

• Particle swarm optimization
• Ant colony optimization (inspired by ant trails).

Some of the SEO tasks that AI can help with

Many things can benefit from machine learning and AI, but a good place to start is with routine or repetitive tasks. This includes, but is not limited to the following:

• Recognize the underlying need in a customer journey—ascertain where the user is in the purchasing cycle, what information they are seeking, and how you can assist them in moving closer to the purchase cycle.
• Identify content opportunities: Analyse competitor data, search impression queries, and related searches, as well as third-party tools such as AnswerThePublic, to generate topic and content ideas for your website.
• Define opportunity space within a competitive context—collect and receive suggestions for optimizations for queries and topics with a high degree of relevance, search volume, and low competition.
• Convert intent to content: ascertain intent by analysing search queries and keyword research and determining the best page to direct users to. Additionally, this procedure can be used to create new landing pages.
• Utilize structured data and markup—collect structured data from competitor websites to evaluate the best navigation, taxonomy, and structure for the purpose of creating or improving your website's crawlability and search engine understanding of your products and services.
• Invest more in long-tail content: combine data from multiple tools to identify queries with high search volume and low competition for high search engine visibility.
• Ascertain exceptional crawlability—ascertain that your content is easily crawled and viewed by all search user-agents.

Conclusion

Search has always been a critical component of AI. The other significant application of "search" in AI is via algorithms, which are also known as "optimisation" techniques. Hill Climbing, Gradient Descent, Simulated Annealing, and possibly Genetic Algorithms are all examples of these techniques. Additionally, when it comes to problem solving, AI is heavily reliant on search algorithms of various types to accomplish a task. Without these algorithms, AI would be incapable of being self-aware, humanoid, or adaptable. Thus, search algorithms and intelligent agents are the fundamental building blocks of AI, which is also evolving as a result of this futuristic technology.

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