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Modern Algorithms That Will Revolutionize Your Business

Algorithms and AI are words that have become commonplace in the business world. What are some of the most popular and modern algorithms, and what do we use them for?

Nate Dow

By Nate Dow

Solutions Architect Nate Dow helps BairesDev teams provide the highest quality of software delivery and products with creative business solutions.

10 min read

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Algorithms are a key part of any business, whether it’s to drive sales, target marketing efforts, or even just streamline internal processes. By understanding how algorithms work, businesses can make the most of them and use them to their advantage. 

There are a few different types of algorithms that businesses can use, each with its own advantages: 

  1. Decision trees: Decision trees are a type of algorithm that helps businesses make decisions by considering all potential options and outcomes. They’re often used in marketing campaigns to determine the best way to target customers or in sales operations to choose the most efficient path for closing a deal. 
  2. Linear programming: Linear programming algorithms help businesses optimize resources by solving complex mathematical problems. They’re commonly used in manufacturing and logistics applications to minimize costs and maximize profits. 
  3. Genetic algorithms: Genetic algorithms mimic the process of natural selection by iteratively improving solutions over time. They’re often used in optimization problems where there is no clear best solution, such as finding the shortest route between multiple points or creating new products through design exploration.
  4. Neural networks: Neural networks are a type of algorithm that is modeled after the brain and can learn to recognize patterns. They’re commonly used in image recognition and voice recognition applications. 
  5. Data mining: Data mining algorithms help businesses make sense of large data sets by finding hidden patterns and trends. They’re often used in marketing applications to understand customer behavior or in financial applications to detect fraud.
  6. Machine learning: Machine learning algorithms are a type of algorithm that gets better at a task over time through experience. They’re commonly used in applications where there is a lot of data, such as website recommendations or spam detection. 
  7. Optimization algorithms: Optimization algorithms help businesses find the best solution to a problem from a set of potential solutions. They’re commonly used in route planning and scheduling applications to find the shortest or most efficient path.

And we could keep on going and going. Algorithms are everywhere, and each is specifically designed to solve a problem. Depending on the nature and scope of your business, you could use one algorithm or several. You could concatenate algorithms to create extremely complex systems that can solve any kind of problem imaginable. So, where should you start?

XGBoost

Let’s start by talking about one of the most popular machine learning algorithms on the market. XGBoost is a powerful tool for solving machine learning problems. It was originally developed by Tianqi Chen and his team at the University of Washington. The name XGBoost comes from the fact that it uses “eXtreme Gradient Boosting” to train its models. 

XGBoost has become one of the most popular machine learning libraries in recent years, due to its effectiveness in a variety of tasks such as classification, recommendation, regression, and ranking. The original implementation of XGBoost was in C++, but there are now also versions available in R, Python, Java, and Julia, so it should come as no surprise that every data scientist out there swears by its quality and ease of use. 

XGBoost is an implementation of gradient-boosted decision trees designed for speed and performance. How does it work? XGBoost builds an ensemble of weak learners, or decision trees. Each tree is only slightly better than random, but when they are combined, they can produce accurate predictions. It’s like a random decision forest on steroids.

The algorithm works by sequentially adding trees, each one correcting the errors of the previous ones. The main advantage of XGBoost is its speed and efficiency. It’s able to train large models quickly and accurately on large data sets. Additionally, it’s relatively easy to use and tune, which makes it a good choice for many machine-learning tasks.

If you’re looking to use XGBoost, there are a few things you’ll need: 

  • A computer with at least 4GB of RAM – XGBoost is a memory-intensive algorithm and will need plenty of RAM to run effectively. Keep in mind that this is the bare minimum. XGBoost will usually work a lot better on systems with at least 16GBs of RAM, and if your data set is big enough, it might be best to host it on the cloud. 
  • A good data set – XGBoost can be used with both classification and regression problems, but works best when there is a large, high-quality data set to learn from. By large, we mean anything from a hundred cases (if there is very little variability) to millions of data entries (for highly complex and variable data sets).
  • The right parameters – Tuning the parameters of an XGBoost model can be tricky, but it’s important to get them just right for the algorithm to perform its best. The good news is that tinkering with the parameters is really simple; it takes very little to change a few values and run the model again. This is in big part thanks to the amazing libraries available. 
  • Time – XGBoost is a computationally intensive algorithm and can take some time to train, especially on large data sets. And still, it’s one of the faster alternatives.

To be honest, these are things you will need for every other algorithm we’ve listed, so assume that these are also requirements unless we explicitly say otherwise.

LightGBM

Like XGBoost, LightGBM is a gradient-boosting framework that uses tree-based learning algorithms. In fact, you could consider it XGBoost’s main competitor. It is designed to be efficient and easy to use, and it supports various types of data, including categorical, text, and numerical data. 

The origins of LightGBM can be traced back to Microsoft Research, where the algorithm was developed by Guolin Ke and other researchers. The algorithm was then open-sourced in 2016 under an MIT license. Since then, LightGBM has become one of the most popular gradient-boosting frameworks, with a growing community of users and contributors. 

The project has been adopted by many companies and organizations, including Microsoft, Amazon, Facebook, Google, and others. LightGBM is constantly evolving and improving, with new features being added regularly. The latest version (3.3.2) was released in January 2022.

While LightGBM presents itself as a faster alternative to XGBoost, the truth is that this really depends on the nature of the task. The difference between the algorithms with small data sets is barely noticeable. But since LightGBM uses a histogram-based algorithm instead of XGBoost’s gradient-based algorithm, LightGBM is more efficient when dealing with large data sets.

This is in part because LightGBM supports parallel training, which can speed up training times considerably on large data sets. On top of that, LightGBM can handle categorical data natively, while XGBoost requires the use of one-hot encoding for categorical variables. This makes LightGBM more convenient to use since it doesn’t require additional preprocessing steps. 

But the extra speed does come with a few limitations, and in the long run, XGBoost tends to yield more reliable results. If your process needs speed, then you won’t find a better alternative than LightGBM. On the other hand, if precision is what you’re after, you would do well to stick with XGBoost.

Neural Networks: Long Short-Term Memory Networks

Long short-term memory (LSTM) is a type of recurrent neural network that is well-suited to learning from sequences of data. LSTMs can remember information for long periods. 

LSTMs were first proposed in 1997 by Sepp Hochreiter and Jürgen Schmidhuber. Since then, LSTMs have been used for a variety of tasks including speech recognition, machine translation, and handwriting recognition. The key difference between LSTMs and other types of recurrent neural networks is the use of gates within the cells of the LSTM. 

These gates control how much information flows into and out of the cell state at each timestep. This allows LSTMs to better handle situations where the input data is very noisy or where there are long-term dependencies in the data. This is because LSTM networks also have a special type of memory cell called a “memory block” that can retain information for long periods.

LSTM networks are composed of three main components: input gates, output gates, and forget gates. Each gate has a corresponding weight matrix that is learned during training. 

The input gate controls how much information from the current input will be allowed into the memory block. The output gate controls how much information from the memory block will be allowed to pass out to the next layer or prediction. And finally, the forget gate controls how much information from previous inputs will be forgotten or erased from the memory block.

So, why put this neural network on this list instead of other (and more famous and modern) alternatives? To be honest, I have a soft spot for LSTM networks (they are a personal favorite of mine). But there is actually more to it. As you work with your AI models, there is a very high possibility of the model forgetting as more info is put into it.

LSTM provides a solution for this problem, making sure, as mentioned above, that key data remains in place for a very long period. 

Generative Adversarial Networks

In 2014, Ian Goodfellow and his colleagues at the University of Montreal introduced the world to generative adversarial networks (GANs). GANs are a type of artificial intelligence algorithm where two neural networks compete against each other in a zero-sum game. The first network, called the generator, creates fake data that is realistic enough to fool the second network, called the discriminator. 

As the generator gets better at creating fake data, the discriminator gets better at identifying it. This competition drives both networks to improve their performance until eventually, the generator produces data that is indistinguishable from real data. GANs have been used to generate realistic images, videos, and even text. 

They are also being used for more practical applications, such as generating synthetic training data for machine learning models or improving image compression algorithms. Since their creation in 2014, GANs have become one of the most popular methods for generative modeling due to their flexibility and success in generating high-quality samples. 

Despite their successes on many data sets and architectures, training GANs remains challenging because they are often unstable and can be mode collapsed. What does that mean?

A GAN is successfully trained when these goals are achieved:

  1. The generator can reliably generate data that fools the discriminator.
  2. The generator generates data samples that are as diverse as the distribution of real-world data.

A mode collapse is when the second goal is never met. In other words, the solution the generator designs collapses into a small sample of data or even just a single data output that fools the discriminator over and over again. Things going off the rails is always a risk with machine learning algorithms since we have very little control over their behavior once training starts.

Naive Bayes Algorithm

A Naive Bayes algorithm is a probabilistic machine learning algorithm that is often used in text classification. It is called “naive” because it makes the assumption that all features are independent of each other. This assumption simplifies the math behind the algorithm, but it may not always be accurate. 

The Naive Bayes algorithm works by calculating the probability of each class (e.g., spam or not spam) given a set of features (e.g., words in an email). It then chooses the class with the highest probability as its prediction. This approach is very effective in many applications, especially when there is a large amount of data and limited resources for training a more sophisticated model.

The Naive Bayes algorithm has been around for centuries, with early versions being developed in the 18th century by statistician Thomas Bayes. It was first used for predicting events such as horse races but later came to be used in more sophisticated applications such as medical diagnosis and stock market analysis. 

Despite its simplicity, the Naive Bayes algorithm can be very effective. It is often used as a baseline method for comparison against more complex models. In many cases, it outperforms more complicated methods even when those methods are given more information about the data!

While less powerful than its brethren, Naive Bayes is very lightweight and very fast. It’s one of those algorithms you can comfortably run on your personal computer without worrying about it catching fire.

Advantage Weighted Actor-Critic

Advantage weighted actor-critic (AWAC) is a reinforcement learning algorithm that combines aspects of both the actor-critic and advantages actor-critic methods. Like the actor-critic algorithm, AWAC uses value functions to approximate the optimal policy and value function. 

However, unlike the actor-critic algorithm, AWAC also uses an advantage function to help learn the optimal policy. The advantage function is used to weigh the importance of each state-action pair when updating the policy. This weighting allows for faster learning by prioritizing states that are more likely to lead to successful outcomes. 

In addition, because the advantage function takes into account both future rewards and current rewards, it can help mitigate some of the issues with delayed reward signals that are common in reinforcement learning problems. Overall, AWAC is a promising reinforcement learning algorithm that has shown good results in a variety of environments.

Out of all the algorithms on this list, AWAC is the newest, and also one of the most promising ones in the field of robotics, offering an incredibly fast alternative to other learning algorithms. If you’re looking for an algorithm that can help you learn complex policies quickly, then AWAC may be worth considering.

Why Should You Implement Algorithms in Your Business?

As a business owner, you are always looking for ways to improve your bottom line. Implementing algorithms can be a great way to do this. Algorithms can help you automate tasks, make better decisions, and optimize your resources. Here are some specific reasons why you should implement algorithms in your business: 

  1. Automate repetitive tasks. If there are any tasks that you or your employees find yourself doing on a daily or weekly basis, chances are there is an algorithm that can handle them for you. Automating these types of tasks can free up time for more important work.
  2. Make better decisions with data-driven insights. Algorithms can take a large amount of data and turn it into actionable insights. This can be helpful in everything from marketing to product development to customer service. By using data to make decisions, you can increase your chances of making the right decision and achieving success with your business goals. 
  3. Optimize your resources with predictive analytics. Predictive analytics is a type of algorithm that uses past data to predict future outcomes. This information can be used to optimize everything from inventory levels to staff schedules. Having this type of insight allows you to make proactive decisions that could save your business money and increase efficiency.

Just keep in mind that no algorithm is perfect and that computers won’t be replacing our intuition and our ability to adapt and make logical jumps. These tools can be extremely powerful, and we must use them responsibly, but if we grow reliant, there may come a time when we forget about our ability to make decisions and have agency.

Nate Dow

By Nate Dow

As a Solutions Architect, Nate Dow helps BairesDev provide the highest quality software delivery and products by overcoming technical challenges and defining internal teams. His creative approaches help solve clients' business problems with technology.

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