Understanding AI search algorithms

An AI search algorithm is the method for understanding natural language queries and finding relevant results by evaluating indexed data and documents. It does this by exploring a set of potential solutions in order to find the best answer or solution to the query it's given.

Imagine you’re building a chess application using artificial intelligence that predicts the best move to make next. In order to determine the optimal move, your AI search algorithm must evaluate the different options to decide which one is best. This means systematically assessing the position of each piece, evaluating every possible combination of moves, and calculating the tactic that gives you the best chance of winning.

AI search algorithms play a vital role across a huge variety of fields. This ranges from computer science problem-solving to sophisticated decision-making in logistics. Their versatility makes them indispensable for tackling diverse challenges and solving important problems.

For example, NASA is able to analyze rover data from the Mars mission using the AI search algorithms in Elastic®. This enables them to unlock crucial insights and navigate complex challenges much quicker than they would if they had to analyze this data manually. And in the healthcare sector, AI search algorithms are being used to assist in medical diagnosis, treatment planning, and drug discovery. This leads to better diagnostic accuracy, more efficient treatment plans, and the development of new therapies.

These examples highlight the importance and potential, but applications of these algorithms extend far beyond just these use cases. Various fields such as finance, manufacturing, legal services, and more are already benefiting from this new ability to process huge amounts of data and make informed decisions. As it continues to evolve, AI algorithms will play an even more prominent role across every industry and have a huge impact on the world around us.

Each AI search algorithm can be broken down into four elements: states, actions, goals, and path costs. This framework of elements is how the algorithm can navigate complex problem spaces to find optimal solutions.

States are a snapshot of the problem at a particular point in time. They encapsulate all the relevant information about the problem at that moment, so the algorithm can assess the current situation. Think of it like a maze — every turn represents a different “state” in the maze. So by looking at the state, you know where the AI is in the algorithm. 

Actions are the possible transitions between the states. Staying with the maze metaphor, these actions are the available directions you can choose to go. By combining these actions, you can determine the different potential paths to travel through the maze. 

The goal is the ultimate objective of the search process. In search, this goal will be the best and most relevant answer to the initial query. This provides a clear direction for the algorithm, so its efforts are focused on finding the best results. In the maze example, the query “find the best route to escape the maze” would be the goal. 

Path costs are the trade-off between precision and recall for each step or action in the path toward answering the query. This cost represents the effort or resources required to make each particular move. The cost can then be used by the algorithm to prioritize efficient and resource-economic routes.

NLP algorithms are an essential part of search because they bridge the gap between human communication and machine understanding. This enables search AI to understand what is being asked of it and to deliver results that are relevant and contextual to the query.

Using NLP, the search results will be more aligned with the user's intent, and the algorithm will be able to handle complex queries by understanding more nuanced requests. This is because it can identify sentiment and understand the context, as well as personalize the search experience based on previous conversations with the user.

Word embeddings
One of the ways an algorithm can work with words to find similarity is with word embeddings, where words and assets are represented as vectors. This is where it analyzes unstructured data like text and images and transforms them into a numeric value.

A popular example of this is Word2vec, an algorithm that learns word embeddings from a huge collection of written texts. It then analyzes the surrounding text to determine meaning and understand context. Another example is GloVe (Global Vectors for Word Representation), which is also trained to build connections between different words by mapping them depending on their semantic similarity.

Language models
There are also language models that analyze large amounts of data in order to accurately predict the likelihood of what order words will appear. Or in simpler terms, they’re algorithms that allow the search AI to not just understand what we’re saying, but also be able to respond in a way that matches how humans communicate.

For example, BERT (Bidirectional Encoder Representations from Transformers) is a popular language model that’s able to understand complex and nuanced language, which can then be used for powerful semantic search and question answering.

kNN is a simple but versatile AI search algorithm that’s used for identifying the closest data points (“k”) to a new data point. It then uses those “neighbors” to predict the class or value of the new data point. Or in simpler terms, it analyzes the new data and finds the existing data that matches it the closest.

This makes it great for ranking results by relevance, suggesting similar images or videos, or recommending products based on previous purchases. 

Although it is relatively simple and easy to understand, it can be an expensive algorithm to run. This is especially the case for large data sets, because it needs to calculate the distance between the new data points and all the existing data.

An alternative to finding the closest match with kNN is to find a match that’s good enough for your specific needs. This is where approximate nearest neighbors algorithms excel. That’s because ANN algorithms look for data that is a very close match to the query, but not necessarily the closest one. So instead of painstakingly analyzing every bit of data, which can be time and resource-intensive, ANN will settle for something that’s not as close, but still “close enough” in relative terms.

The benefit of this is that you can create a much faster and more efficient similarity search. ANN achieves these “close enough” results by inferring semantic relationships between content and data.

For this approach to be worthwhile, though, you need to accept the trade-off with accuracy, as it doesn’t guarantee the closest result. ANN will be a great solution most of the time, but if you need to guarantee absolute accuracy, this might not be the best option for you.

Uninformed search algorithms (also known as blind search algorithms) do not know information about the search space. They solve the query systematically, with no guidance or domain-specific knowledge. They rely entirely on the existing structure of the search space to find the solution.

There are several different types of uninformed search algorithms, but the three most common are breadth-first search (BFS), depth-first search (DFS), and uniform cost search (UCS).

Informed search algorithms (also known as heuristic search algorithms) are a type of search that uses additional information and domain-specific knowledge to guide their searches. Unlike uninformed searches, they use heuristics, which are rules of thumb and estimates that help them prioritize paths and avoid unnecessary exploration.

There are a few different types of informed search algorithm, but the most common are greedy best-first search, A* search, and beam search.

As we have already mentioned, AI search algorithms are being utilized across a wide range of industries to complete a variety of tasks. Here are just a few real-world examples where they’ve made a huge impact.

• Informational retrieval: NLP search algorithms can enhance search results by understanding the context and tone of a query to retrieve more useful information.

• Recommendations: kNN algorithms are often used to recommend products, movies, or music based on their preferences and past behavior.

• Speech recognition: ANN algorithms are commonly used to recognize patterns in speech. This is useful in things like speech-to-text and language identification.

• Medical diagnosis: AI search algorithms can help with speeding up medical diagnosis. For example, they can be trained on massive data sets of medical images and use image recognition to detect anomalies from photos, X-rays, CT scans, etc.

• Pathfinding: Uninformed search algorithms can help find the shortest path between two points on a map or network. For example, determining the shortest delivery route for a driver.

AI search algorithms might have revolutionized various industries with efficient problem-solving and decision-making, but they also create challenges and limitations. For a start, the computation complexity involved can make them extremely expensive to run. This is because they require huge amounts of processing, computational, and memory resources to carry out the search. This limits their effectiveness where there are constraints in place.

Another problem is that an informed search algorithm can only be as good as the heuristics it uses. If the heuristic function isn’t accurate, it can lead the algorithm down the wrong path and result in suboptimal or even incorrect solutions.

Also, AI search algorithms have generally been designed to solve specific types of problems like pathfinding and constraint satisfaction. This has been useful for certain tasks, but there are still limitations to the problem-solving scope, especially when solving more diverse problems. 

AI search algorithms are important tools in solving complex modern problems across a wide range of fields. Their diversity and versatility make them indispensable for tasks like pathfinding, planning, and machine learning.

But while they are revolutionizing industries such as robotics, healthcare, and finance, there is still so much potential. The current limitations and challenges are also opportunities for future advancement. As research continues to enhance performance, AI search algorithms will continue to play an increasingly prominent role in solving real-world problems and changing the face of technology.

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