5 AI operating models and how they work

Although AI operating models only started to be widely recognized over the last few years with cutting edge use cases, the first developments have been made centuries ago. Looking at the history of AI operating models, there is no clear consensus on what constitutes the first AI operating model. However, one of the earliest and most influential AI models was the perceptron, which was developed by Frank Rosenblatt in the late 1950s.

The perceptron is a type of neural network that is capable of learning and making decisions based on inputs from the environment. It consists of a single layer of artificial neurons, each of which receives inputs from the environment and produces an output based on a set of learned weights. The outputs from the neurons are then combined to produce a final decision.The model was initially developed to recognize simple visual patterns such as lines and curves. However, it quickly gained attention for its potential to solve more complex tasks, such as speech recognition and natural language processing. Rosenblatt’s work on the perceptron laid the groundwork for modern neural networks and deep learning how we know it today, and it remains a landmark achievement in the history of AI research.

Although Rosenblatt´s work gained huge popularity, many other researchers made significant contributions to the development of AI models in the years that followed. Some promiment examples include John McCarthy, Marvin Minsky, and Arthur Samuel, among many others. These researchers laid the foundation for the AI operating models we know today and this is where this article will dvelve into. It will introduce five commonly used AI operating models and how they work.

1. Rule-Based Model: A rule-based model involves creating a set of rules and logical statements that the system follows to make decisions. It is usually used for simple, straightforward tasks.
2. Decision Tree Model: In this model, the system uses a tree-like structure to make decisions based on a set of predefined conditions. It is commonly used in machine learning and data mining applications.
3. Neural Network Model: A neural network model uses a set of algorithms to recognize patterns and relationships in data. It is often used in image and speech recognition applications.
4. Bayesian Model: In this model, the system makes decisions based on probability theory and statistical analysis. It is commonly used in natural language processing and information retrieval applications.
5. Reinforcement Learning Model: With this approach, the system learns through trial and error, receiving feedback in the form of rewards and punishments for its actions. It is often used in robotics and game playing applications.

5 Types of AI Operating Models

Rule-Based Model

A rule-based model is a type of artificial intelligence (AI) model that relies on a set of rules to make decisions. These rules are typically created by humans and are based on a set of logical statements that the system follows. For example, a rule-based system might be used to control a traffic light. The system would have a set of rules that determine when the light should change from green to yellow, and when it should change from yellow to red. While this model is easy to understand and implement, it is limited in its ability to handle complex tasks and may not be able to adapt to changing circumstances.

Decision Tree Model

A decision tree model is a type of machine learning model that uses a tree-like structure to make decisions. The tree structure is created by dividing the data into smaller and smaller subsets, based on a set of predefined conditions. Each subset is then split again until a final decision is reached. For example, a decision tree model might be used to predict whether a customer will buy a product based on their demographic information. The model would use a set of conditions, such as age, income, and gender, to divide the data into subsets and make a prediction. Another illustrative example of a decision tree model is fraud detection in financial transactions. In this case, the model is trained on a dataset of past transactions that have been labeled as either fraudulent or legitimate. Its tree-like structure helps to make decisions about whether a new transaction is fraudulent or not, based on a set of predefined conditions such as the transaction amount, location, and timing. If a transaction is made for an unusually large amount at an unusual time of day and from a new location, the model may flag it as potentially fraudulent and require additional verification. The model can be continually updated and refined as new data becomes available, improving its accuracy over time. Decision tree models are popular because they are easy to understand and can handle both categorical and numerical data.

Neural Network Model

A neural network model is one, that uses a set of algorithms to recognize patterns and relationships in data.The model is inspired by the structure of the human brain, with layers of interconnected nodes that process information. Neural networks are commonly used in image and speech recognition applications, as well as in natural language processing. They are powerful models that can learn from large amounts of data, but they can be difficult to interpret and require a lot of computing power. One great example on the usage of a neural network can be found in the healthcare sector, where medical image recognition plays a critical role for detecting diseases, such as tumors, cancer or diabetic retinopathy. The neural network model is used to process image data and identify the shapes of organs from MRI images or CT scans to find anomalies. This approach supports the doctors in finding diseases early on and providing the patients with the best medical treatment as early as possible.

Bayesian Model

A Bayesian model uses probability theory and statistical analysis to make decisions. The model is based on Bayes’ theorem, which describes the probability of an event based on prior knowledge. Bayesian models are commonly used in natural language processing and information retrieval applications. For example, a Bayesian model might be used to predict the likelihood that a customer will click on a particular advertisement based on their search history. These models can handle uncertainty and incomplete data and therefore are quite powerful, but they can be difficult to implement and may require a lot of computational resources.

Reinforcement Learning Model

A reinforcement learning model is a type of AI model that learns through trial and error. The model receives feedback in the form of rewards or punishments for its actions and uses this feedback to improve its performance. Reinforcement learning is widely used in robotics and game playing applications. A reinforcement learning model might be used to teach a robot to navigate a maze. The robot would receive a reward for reaching the end of the maze and a punishment for hitting a wall. Reinforcement learning models can adapt to changing circumstances, but they can be slow to learn and may require a lot of data. Another example of such a model in practise is training the model to play a game like chess. In this scenario, the AI starts with no knowledge of the game rules or how to win. As the model continues to play, it receives positive or negative feedback based on the outcome of each move. It learns from this feedback and adjusts its strategy accordingly, gradually improving its performance over time. With enough practice and feedback, the model can become skilled enough to beat even the best human players.

What is the most commonly used AI operating model?

There is no single “most commonly used” AI operating model, as the choice of model depends on the specific task at hand and the available data. Different AI models have different strengths and weaknesses, and some models may be more appropriate for certain applications than others. Ultimately, the choice of AI operating model depends on the nature of the problem being solved, the available data, and the desired outcomes. Therefore, its crucial to clearly identify the problem which should be solved and determine the desired outcome first, before considering using any type of model.

But be careful, AI operating models can also fail badly

Such as humans, AI models are far from perfect. There have been several instances where AI operating models have failed badly, often due to biased or incomplete training data, unexpected edge cases, or errors in the design or implementation of the model.

One prominent example was when Microsoft released a Twitter chatbot named Tay, designed to learn from user interactions and become better at responding to tweets over time. However, within hours of its release, Tay began to make offensive and inappropriate tweets, including racist and sexist remarks, and even expressed support for Adolf Hitler. The reason for Tay’s inappropriate behavior was due to the fact that the chatbot was designed to learn from its interactions with Twitter users, and some users began to deliberately feed it with offensive content in order to provoke it.

While the failure of Tay was due to a combination of design flaws and human factors, it highlights the risks associated with releasing AI models into uncontrolled environments where they can interact with potentially biased or malicious users. The incident with Tay illustrates the importance of careful testing and validation of AI models before they are released into the wild, as well as ongoing monitoring and supervision to ensure that they operate as intended.

How do I identify the best AI operating model for my use case?

Identifying the best AI operating model for a particular use case can prove to be difficult, as the choice of model depends on a few factors such as the nature of the problem being solved, the available data, and the desired outcomes. To get a better understanding for which model should be used, there are a few key aspects to consider.

1. What´s the problem? – Start by clearly defining the problem you want to solve and the desired outcome.
2. What type of problem are you facing? – Determine whether the problem is a classification, regression, clustering, or reinforcement learning problem.
3. What kind of data is available? – Look at the data you have available and assess its quality, quantity, and relevance to the problem you are trying to solve.
4. How complex is your problem? – Evaluate the complexity of the problem and the level of accuracy required. Some models are better suited for complex problems with high accuracy requirements, while others are better suited for simpler problems.
5. Has anyone done this before? – Once you have identified the above factors, research and evaluate different models that are used for similar use cases. Evaluate their strengths, weaknesses, and suitability for your specific use case.
6. How do different models perform with my dataset? – Experiment with the models and evaluate their performance on your dataset. This may also vary in timing and it might take you several runs over the course of a few months to get a reliable outcome to work with.

Summary

You’ve now learned 5 common AI operating models and examples, where they can be used, but also the fact, that AI operating systems are far from perfect. So although these models can be useful and a quite powerful technology to gain some efficiency, the human factor can’t be left out and every model needs to be checked in terms of plausibility, fit and biases.

Last but not least, you’ve got a few starting points on how to find the most suitable model for your use case and ensure sufficient outcomes with it.

So now it’s up-to-you to get started.

Nicole Lontzek

Nicole Lontzek ist seit über einer Dekade in der Digitalbranche tätig. Ihre Karriere brachte sie unter anderem nach New York, Dublin und Zürich. Sie ist spezialisiert auf die digitale Vermarktung von B2B-Software Unternehmen. Derzeit ist sie in München als Head of Marketing bei CELUS, dem Pionier in der Elektronikentwicklungsautomatisierung für die Gesamtvermarktungstrategie verantwortlich. In ihrem Buch "Digitale Zeitmacher - was wir jetzt gewinnen" erläutert sie anhand positiver Beispiele die Möglichkeiten der Digitalisierung und zeigt auf, in welchen Bereichen wertvolle Lebenszeit eingespart werden kann. www.digitalezeitmacher.de