Amid all the hype and hysteria about ChatGPT, Bard, and other generative large language models (LLMs), it’s worth looking back at the spectrum of AI algorithms and their uses. After all, many “traditional” machine learning algorithms have been solving important problems for decades—and they’re still going strong.
Companies nowadays run the risk of becoming obsolete if they refuse to adopt AI and machine learning. This is because AI algorithms can help sharpen decision-making, make predictions in real-time, and save companies hours of time by automating key business workflows. They can improve customer service, bubble up new ideas and bring other business benefits.
However, this can only work if organizations understand how AI algorithms work, know which type is best suited to the problem at hand, and take steps to minimize AI risks. AI systems run on algorithms, but not every AI algorithm is the same. By understanding how AI algorithms work, you can ease your business processes, saving hours of manual work.
What are AI algorithms?
AI algorithms are a set of instructions or rules that enable machines to learn, analyze data and make decisions based on that knowledge. These algorithms can perform tasks that would typically require human intelligence, such as recognizing patterns, understanding natural language, problem-solving, and decision-making.
All the tasks that AI performs work on specific algorithms. From when you turn on your system to when you browse the internet, AI algorithms work with other machine learning algorithms to perform and complete each task. AI and machine learning algorithms enable computers to predict patterns, evaluate trends, calculate accuracy, and optimize processes.
It is important in any discussion of AI algorithms to also underscore the value of using the right data and not so much the amount of data in the training of algorithms. We’ll dive into why quality data testing is so critical. With that said, the following are some general types of AI algorithms and their use cases.
How do AI algorithms work?
AI algorithms work by identifying the patterns, recognizing the behaviors, and empowering the machines to make decisions.
If, for example, you tell your voice assistant like Alexa or Google Home to stream your favorite music. The AI algorithm will first recognize and remember your voice, get familiar with your choice of music, and then remember and play your most streamed music just by acknowledging it. Similarly, AI content editor tools work on algorithms like natural language generation (NLG) and natural language processing (NLP) models that follow certain rules and patterns to achieve desired results.
This isn’t rocket science but a simple formula: “The more you learn, the more you grow.”
As you serve the computer systems with rich data, the algorithms use it to gain knowledge and perform tasks more efficiently. At the most basic level, an AI algorithm collects the data for training and then uses it to modify its knowledge. Next, it uses this knowledge to complete the tasks and improve accuracy.
Techniques used in AI algorithms
There are several techniques that are widely used in AI algorithms, including the following:
Natural language processing
NLP is a field of AI that deals with the interaction between computers and human language. NLP techniques enable machines to understand, interpret and generate human language in textual and spoken forms.
Common NLP techniques include sentiment analysis, named-entity recognition and machine translation.
Machine learning
Machine learning is a subset of AI and is the most prevalent approach for training AI algorithms. ML uses statistical methods to enable machines to learn from data without being explicitly programmed. ML algorithms, as explained above, can be broadly classified into three types: supervised learning, unsupervised learning and reinforcement learning.
Common machine-learning techniques include linear regression, decision trees, support vector machines and neural networks.
Deep learning
Deep learning is a subset of machine learning that involves the use of artificial neural networks with multiple layers to learn complex patterns in large amounts of data. It has been successful in a wide range of applications, such as computer vision, speech recognition and natural language processing.
Popular deep learning techniques include convolutional neural networks and recurrent neural networks.
Types of AI algorithms
There are three main types of AI algorithms.
Supervised learning algorithms
In supervised learning, the algorithm learns from a labeled data set, where the input data is associated with the correct output. This approach is used for tasks such as classification and regression problems such as linear regression, time-series regression and logistic regression. Supervised learning is used in various applications, such as image classification, speech recognition and sentiment analysis.
Examples of supervised learning algorithms include decision trees, support vector machines and neural networks.
Unsupervised learning algorithms
In unsupervised learning, an area that is evolving quickly due in part to new generative AI techniques, the algorithm learns from an unlabeled data set by identifying patterns, correlations or clusters within the data. This approach is commonly used for tasks like clustering, dimensionality reduction and anomaly detection. Unsupervised learning is used in various applications, such as customer segmentation, image compression and feature extraction.
Examples of unsupervised learning algorithms include k-means clustering, principal component analysis (PCA) and autoencoders.
Reinforcement learning algorithms
In reinforcement learning, the algorithm learns by interacting with an environment, receiving feedback in the form of rewards or penalties, and adjusting its actions to maximize the cumulative rewards. This approach is commonly used for tasks like game playing, robotics and autonomous vehicles.
Examples of reinforcement learning algorithms include Q-learning, SARSA (state-action-reward-state-action) and policy gradients.
Use cases for AI algorithms
AI algorithms have numerous applications across all industries, making it safe to say that the state of AI is near-ubiquitous in business.
The following are some examples of AI’s reach:
Finance
AI is used for fraud detection, credit scoring, algorithmic trading and financial forecasting. In finance, AI algorithms can analyze large amounts of financial data to identify patterns or anomalies that might indicate fraudulent activity. AI algorithms can also help banks and financial institutions make better decisions by providing insight into customer behavior or market trends.
Healthcare
AI algorithms can assist in diagnosis, drug discovery, personalized medicine and remote patient monitoring. In healthcare, AI algorithms can help doctors and healthcare professionals make better decisions by providing insights from large amounts of data. For example, AI algorithms can analyze medical images to identify anomalies or predict disease progression.
Retail and e-commerce
AI enables personalized recommendations, inventory management and customer service automation. In retail and e-commerce, AI algorithms can analyze customer behavior to provide personalized recommendations or optimize pricing.
AI algorithms can also help automate customer service by providing chat functions.
Tips for training your AI algorithm
The success of your AI algorithms depends mainly on the training process it undertakes and how often it is trained. There’s a reason why giant tech companies spend millions preparing their AI algorithms. However, the cost of training AI is substantial.
For instance, training a large AI model such as GPT-3 amounted to $4 million, as reported by CNBC. Even the algorithm that Netflix’s recommendation engine is based on was estimated to cost around $1 million. After all, it’s the most substantial part of the lifecycle of your AI system.
However, the processes and best practices for training your AI algorithm may vary slightly for different algorithms.
Here are the best tips to train and implement your AI algorithms
Determine the use cases
The basis for creating and training your AI model is the problem you want to solve. Considering the situation, you can seamlessly determine what type of data this AI model needs.
For example, food giant McDonald’s wanted a solution for creating digital menus with variable pricing in real-time. As the customer places the order, the price of each product will depend on the weather conditions, demand, and distance.
Another use case in which they’ve incorporated using AI is order-based recommendations. Let’s say someone places an order for a salad. The AI model detects and suggests including a healthy drink with the meal.
It’s important to see how your peers or competitors have leveraged AI algorithms in problem-solving to get a better understanding of how you can, too.
Collect and prepare your data.
AI systems need data to thrive and grow as much as humans need air. The prerequisite for AI algorithm training is gathering and preparing your data. By data, we mean the raw data that will be used as a base for training your AI algorithm.
Most organizations adopting AI algorithms rely on this raw data to fuel their digital systems. Companies adopt data collection methods such as web scraping and crowdsourcing, then use APIs to extract and use this data. However, mere data collection isn’t enough. The next crucial step is the data preprocessing and preparation, which involves cleaning and formatting the raw data.
Instagram uses the process of data mining by preprocessing the given data based on the user’s behavior and sending recommendations based on the formatted data.
Select your AI model
Developers have to choose their model based on the type of data available — the model that can efficiently solve their problems firsthand. According to Oberlo, around 83% of companies emphasize understanding AI algorithms.
The model selection depends on whether you have labeled, unlabeled, or data you can serve to get feedback from the environment. However, other factors decide the AI model architecture. The choice of AI model also depends on:
- The size and structure of the data.
- Complexity of the available dataset.
- Desired level of accuracy.
Based on these factors and the type of problem to be solved, there are various AI models such as Linear Regression, Decision Trees AI, Naive Bayes, Random Forest, Neural Networks, and more. Hence, if the problem is related to solving image processing and object identification, the best AI model choice would be Convolutional Neural Networks (CNNs).
Train your AI model
The basis of your AI algorithm relies on the training, testing, and validation of the dataset. Hence, it is the most crucial step in training your AI algorithm.
The first step is the initial training process. The prepared data is fed into the model to check for abnormalities and detect potential errors. The predominant error in the AI model is overfitting. This means the error occurs when a particular trained dataset becomes too biased.
One example of overfitting is seen in self-driven cars with a particular dataset. The vehicles perform better in clear weather and roads as they were trained more on that dataset. Consequently, vehicles fail to perform in extreme weather conditions and crowded places.
When fed with a new data set, the AI model will fail to recognize the data set. The subsequent steps in the training process are validation and testing. While the validation re-examines and assesses the data before it is pushed to the final stage, the testing stage implements the datasets and their functionalities in real-world applications. The testing stage is when the training wheels come off, and the model is analyzed on how it performs in the real world using the unstructured data.
If it fails to perform and return the desired results, the AI algorithm is sent back to the training stage, and the process is repeated until it produces satisfactory results.
Measure and track the results
The final test is the basis of tracking your AI model. AI algorithms are measured using specific metrics to garner the results. Calculate relevant evaluation metrics, such as accuracy, precision, recall, F1 score, or mean squared error, depending on your problem type. Set a goal or a threshold value for each metric to determine the results.
If the results aren’t satisfactory, iterate and refine your algorithm based on the insights gained from monitoring and analysis. Always test your algorithm in different environments and train them to perfection.
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