If it is the first time you read about artificial intelligence modeling, you’re likely to imagine this technology in the form of a science fiction robot with human-like characteristics. However, at the current stage of AI development, this is not the case. AI is not robotic technology aimed to conquer the world and compete with humans. Instead, it can be a helpful tool that will facilitate your work, streamline your business processes, and automate a lot of tasks for your team.
Keep reading to learn more about major AI models, AI modeling applications in different spheres, and the benefits AI development can bring to your company.
What Is an AI Model?
In 2022, the AI market size was valued at $120 billion. Artificial intelligence is a subfield of computer science that aims to teach machines to perform activities and tasks that are typically done by humans. Artificial intelligence modeling works in analogy to human intelligence. Some of the functions that artificial intelligence performs include planning, learning, reasoning, problem-solving, and decision-making.
Artificial intelligence modeling works based on algorithms such as machine learning, deep learning model, rule-based learning, etc. The machine learning algorithm activates a certain data processing mechanism that enables artificial intelligence to process data and learn from it. With the help of machine learning, artificial intelligence models learn without being programmed to do so.
AI models have penetrated many fields, from Google search to self-driving cars and autonomous weapons. They can significantly reduce the time needed for performing tedious tasks and unlock access to insightful data that humans can use when making informed business decisions. Let’s discuss three main types of artificial intelligence and their applications.
3 Perspectives on Artificial Intelligence
AI technologies can be classified into three main groups:
- Artificial narrow intelligence (ANI), which is characterized by a narrow list of abilities;
- Artificial general intelligence (AGI), which resembles human intelligence and covers most human capabilities;
- Artificial superintelligence (ASI), which is expected to perform more complex tasks than humans.
Artificial Narrow Intelligence (ANI)
Artificial narrow intelligence (ANI) is also known as weak AI or narrow AI. It is the most developed type of artificial intelligence that performs singular tasks and has a narrow focus. For example, narrow AI can be involved in the facial recognition process, image recognition, voice recognition, search engine processes, and more. Artificial narrow intelligence is very intelligent at performing particular tasks that AI is programmed to perform.
At first glance, it seems that some complex AI algorithms lie at the core of artificial narrow intelligence. However, it is the simplest AI type that has a lot of limitations and drawbacks. This is why ANI is also called weak AI.
In the case of ANI, the technology does not mimic human brain processes. It just simulates human behavior based on the context and the limited set of parameters configured at the start.
An example of narrow AI in action is the work of recommendation engines. This technology recommends products to customers based on their purchase history and behavior on the ecommerce website. The engine is not programmed to perform anything apart from this task.
Narrow artificial intelligence grows along with machine learning and deep learning models. This AI type is implemented in many industries. For example, artificial narrow intelligence is used in medicine for disease detection and disease mapping. The technology is also used in e-mail filtering, self-driving, manufacturing, and other fields.
Artificial General Intelligence (AGI)
Artificial general intelligence (AGI) is also known as strong AI or deep AI. As said before, AGI can mimic human intelligence, learn from historical data, and use these insights for problem-solving. If you are not an AI developer or data scientist, it will be hard for you to differentiate the output generated by artificial general intelligence from the human-made output.
While weak AI is gaining momentum, strong AI is only starting its path to growth. The goal of AI professionals today is to expand the capabilities and cognitive abilities of strong AI by making it conscious. They aim to make machines capable of performing more complex tasks and taking learning initiatives. The knowledge produced as a result of AI work can be used for solving a wide range of tasks, not just the tasks the machine is programmed to solve.
Artificial general intelligence also has its limitations. Mainly, these relate to the lack of knowledge about the human brain and its work. Because there is not enough data on the human brain processes, scientists cannot fully replicate them and program artificial intelligence in a similar way.
Artificial Superintelligence (ASI)
Under the ASI concept, AI is supposed to fully understand human emotions, mimic human creativity, and understand the implications even if they are not expressed directly. Besides, ASI is expected to outperform humans in all spheres, including science, sports, art, medicine, etc. Among other characteristics that will differentiate artificial superintelligence will be supreme performance and fast training data processing.
The hypothetical growth of artificial superintelligence has many constraints and limitations. First and foremost, it’s the ethical dilemma of growing the technology able to compete with humans. In this regard, we can talk about the unpredicted consequences for humanity that may even impact our survival. Fortunately, things like artificial superintelligence are now more of a movie idea than a close reality.
3-Step AI Modeling Process
Before we move to the AI modeling process, let’s quickly go through the data pipeline. To enable any AI model, we need data. AI experts start with data collection and preparing it for AI processing. The next stage of the data pipeline is implementing smart ML techniques that will enable artificial intelligence and advanced analytics. If you are interested in learning more about major ML techniques, check out our recent blog post.
Now, the data is ready, and we can start the AI modeling process. It involves three steps:
- AI modeling. The first task is to develop an AI model based on the chosen machine learning algorithm. Artificial intelligence modeling can also combine several algorithms that will compose a layer.
- Model training. The next task is to train our AI model. For this, we will need the data that goes through the data pipeline and is ready for processing. Artificial intelligence will process big volumes of data in iterative test loops. The testing results will determine the success of model training. If you get the results as expected after thorough testing, you can integrate the model. If the results are not accurate, your AI development team will improve the AI model as it learns.
- Inference. This is the final step in the AI modeling process. Inference means implementing your AI models in real-life to improve decision-making and get access to insightful data.
Why are we talking about AI? Forbytes is a pro software development partner for businesses across the globe. If you need help with AI development, AI model creation, or AI deployment — leave us a note and get a prompt response from our AI experts.
AI Models Explained
There are numerous artificial intelligence models available for data scientists and software engineering experts. We will discuss the main ones.
ANN, or Artificial Neural Network
ANN is also known as neural sets or neural networks. Neural networks are inspired by the way brain neurons work — ANN mimics the work of the human brain. Neurons composing ANN are grouped into nodes. The connection between them resembles synapses in the biological brain and can transfer the signal from one neural to another to further process it. In the ANN context, the signal stands for the input value that is processed and manipulated by different layers of neurons.
CNN, or Convolutional Neural Network
CNN is typically used for processing pixel data. Instead of general matrix multiplication, CNN uses another mathematical technique at least at one of the layers — convolution. This AI model can differentiate various aspects and objects in the input image by importance. The AI model is used for image recognition, face recognition, recommender systems, natural language processing, and more.
RNN, or Recurrent Neural Network
In Recurrent Neural Networks, nodes composed of neurons connect into cycles. As a result, the output from one node can affect the input of subsequent nodes. This enables the AI model to work dynamically by using its internal memory for input processing. RNN models are applied to speech recognition and handwriting recognition.
GAN, or Generative Adversarial Network
Generative Adversarial Network is one of the artificial intelligence models that can recognize patterns in the input data. The model uses the results to produce the output by analogy with the input patterns. By doing so, the artificial intelligence model produces the output that could plausibly be produced based on the same input.
SVM, or Support Vector Machines
This artificial intelligence model is used for logistic regression analysis and classification. Under this algorithm, AI constructs the set of hyperplanes to classify data points in the high-dimensional space. The model aims at determining the hyperplane that maximizes the gap between different data classes. By operating data in a high-dimensional space, AI can classify it. SVM is used for solving complex classification tasks, including problems in bioinformatics, image recognition, as well as text classification.
Other Important Classifications
AI models differ in terms of other characteristics and can be categorized into many groups. Let’s discuss the main two.
Functionality-Based Classification
Based on the functionality, artificial intelligence models can be grouped into 4 categories:
Reactive AI
Reactive artificial intelligence cannot store data and perform historical data analysis. It just responds to the current stimuli in the way the human brain does. Reactive artificial intelligence modeling uses only present data and is unable to leverage the previous data for making future decisions. Their capabilities are limited to particular tasks.
Limited Memory AI
Limited memory AI is capable of storing data and learning from it to make informed decisions. By learning from past experience, the AI model evaluates potential decisions and uses these insights to guide future actions.
Theory of Mind AI
Strong AI is based on the theory of mind framework. The AI that is based on the theory of mind is speculated to get the ability to identify and differentiate the intentions, beliefs, needs, and states of intelligent entities. Strong AI is not aimed at performing narrow tasks. Instead, it is taught to understand the context and the intention humans imply in their message.
Self-aware AI
This is not one more type of existing AI technology. It’s just a hypothetical type of artificial neural networks that is self-aware and can outperform the human brain many times. Artificial superintelligence is rather a science fiction notion than a promising AI development area.
Knowledge-Based Classification
AI models can also be classified based on the learning type. Here are 2 categories we may talk about:
Inductive AI
Under this model, AI experts can use multiple datasets consisting of input and output variables to teach the machine. Using the datasets and the limited input, the machine tries to form an inductive hypothesis. That is, technology creates a hypothesis about the general based on what it knows about the specific.
Deductive AI
In this case, the AI model will move from general to specific reasoning. Deductive reasoning is making a conclusion based on the assumption applied to the input. In other words, AI models that work on this reasoning type are aimed to make inferences based on what is already considered true.
Interested in developing or integrating an AI model?
In this article, our AI experts mentioned only some of the most popular AI models. As you see, there are numerous artificial intelligence models that you can benefit from. Depending on the sphere, goals, and business constraints, companies choose a particular AI development strategy and start cutting the operation costs or increasing performance.
To choose the best for your business, get in touch with AI developers. Forbytes can help you audit your business, study your challenges, and decide on the aspects of your business where AI can have a positive impact.
Contact us to get an AI-powered solution that will make your business smarter, data-driven, and more efficient.
