The Role of Beam Search in Modern Machine Learning

Beam search is a machine learning search method designed to work effectively in large search spaces. In this article, we will explore machine learning, common search algorithms, how beam search works, and its advantages and limitations.

What is machine learning?

Machine learning is a branch of artificial intelligence designed to enable computers to learn from experience, similar to how humans do. Imagine teaching a child to recognize different vegetables. You show them carrots, broccoli, and corn, and each time, you name the vegetable. Gradually, the child learns to identify each vegetable on their own.

In machine learning, the computer is the child. Instead of vegetables, we give it data in the form of examples and information. This could be any format, such as pictures, text, or video. The computer looks at this data and tries to find patterns or rules, either through labeled examples (supervised learning) or by making sense of the data on its own (unsupervised learning). 

This learning process can get very sophisticated. The more data we give and the more the computer practices, the better it gets. This is why we often hear about big data in machine learning—more data usually means better learning. As you might imagine, the quality of data is also highly important. If you were trying to teach a child to recognize vegetables and only showed them pictures of broccoli and no other vegetables, they would likely have a hard time generalizing on their own later.

Read more: Supervised vs. Unsupervised Learning: Pros, Cons, and When to Choose

What is a search algorithm?

In machine learning, a search algorithm is a method used to find something, such as a search term. With the high volume of data available online, it’s important for machines to have efficient algorithms to track down the information they need reliably and accurately. When working with search algorithms, you’re likely to encounter these main types:

1. Brute force search 

Brute force searches are the most general and don’t require domain-specific knowledge. You can use this method for more complex searches, like navigating a maze. Within brute-force search, you can have depth-first and breadth-first search methods. Depth-first explores one path deeply before moving to others, while breadth-first looks at all the nearby paths before going deeper. You can also combine these into depth-first iterative deepening, where you define depth for each path going on each iteration. In some cases, this is a linear search. A linear search goes through each item in a list, one by one, in sequence, until it finds the search term or reaches the end.

2. Divide-and-conquer search

You can use divide-and-conquer search algorithms on sorted lists. One common type of this is the binary search algorithm. It repeatedly divides the list in half to reduce the search area, which makes it more effective than linear search algorithms in many cases.

3. Greedy search 

Greedy search algorithms choose the best option at each step, hoping to find the best overall solution. This type of approach uses domain-specific knowledge to inform a series of choices, each time picking the option that looks best in that instant.

For example, if a computer is using a greedy algorithm to find a path through a network, it will always choose the next step that seems shortest or fastest without considering the overall length of the path it’s creating. Greedy algorithms can be very efficient because they make decisions quickly and don’t slow processing times by considering every option. However, this type of algorithm may not be accurate, and it can be difficult to identify whether your solution is correct.

4. Heuristic search 

Heuristic searches use rules or a guide to make the search faster and more efficient. It uses a technique to estimate which path has the highest probability of being correct out of several possible options. This algorithm doesn’t check every possibility but instead uses prior knowledge or an intuitive “best guess” strategy to expedite the search. However, heuristic search requires higher levels of time and memory than other search algorithms, making it impractical in some cases.

What is beam search?

Beam search is a type of heuristic search algorithm designed to address the memory and time limitations of traditional heuristic algorithms. This search algorithm is mainly used in machine learning for problems where you have too many possibilities to explore each one individually. It functions similarly to a hybrid of other search models.

Example of beam search

To better understand this algorithm, imagine you’re playing a game of chess. Instead of considering every possible move (which would be brute force), you select a few promising moves. For each of these moves, you again pick a few good responses, creating a tree of possibilities. However, at each level, you only keep the most promising sequences of moves, disregarding earlier possibilities you decided against. Using this “search algorithm” type to find your next move is similar to how a beam search algorithm works.

The key idea is to explore multiple paths simultaneously but limit the number of paths to the most promising ones at each step. Where a greedy approach might predict the next word in a sentence based solely on the previous one, a beam search would widen the scope and search several possible words to find the best fit. 

In practical applications, like machine translation or speech recognition, beam search helps efficiently find the most probable sequence of words or actions. One of the differences between beam search and other methods like brute force or greedy search is how it balances breadth and depth. Brute force searches exhaustively through all possibilities, which may not be possible for extremely large data sets. Conversely, greedy search might miss the optimal solution because it only looks for the best choice at the current step without considering future consequences. Beam search strikes a balance by exploring a set number of paths deeply rather than just one or all.

Setting your beam width

When you set up a beam search algorithm, you will need to choose your beam width. The beam width is the number of options the algorithm considers at each step. For example, if you were predicting words in a sequence and you set your beam width to five, the algorithm would consider the top five options for each word.

Breadth-first vs. best-first beam search

In the best-first beam search method, the algorithm only expands on the single best node during each step. In the breadth-first algorithm, several nodes are explored to a certain depth before exploring any one node to its full capacity.

Advantages and limitations of beam search

When choosing beam search as your search algorithm, knowing the advantages and limitations can help you use it effectively while avoiding pitfalls. A few of this method's core advantages and disadvantages are as follows.

Advantages

• Practicality in real-world applications: Many real-world problems have huge search spaces where an exhaustive search is impractical. Beam search provides a feasible solution for this, making it particularly useful in natural language processing and speech recognition, where countless combinations of words or sounds exist.

• Balanced approach: Unlike greedy search, which can be short-sighted, beam search considers multiple potential paths, leading to more reliable and potentially more accurate outcomes. This is especially important in tasks where the best immediate choice might not lead to the best overall outcome.

• Integration with neural networks: You can use beam search with neural network-based models, like those used in language translation or image captioning. This integration allows for more effective handling of complex, sequential decision-making processes.

Limitations

• No guarantee of optimal solution: Because the algorithm doesn’t explore all possible paths, you can’t guarantee that beam search will always find the absolute best solution.

• Dependency on beam width: The choice of beam width will make a big impact on your algorithm. A narrow beam might miss good solutions, while a very wide beam might reduce the efficiency.

Learn more on Coursera

Coursera is a learning platform where you can find a wide variety of courses, including highly ranked lectures, courses, Specializations, and Professional Certificates from top universities and industry leaders. To build your machine learning skill set, consider taking Duke's Introduction to Machine Learning course.