6 min.

Types of AI

AI is playing an increasingly important role in today's business world. Companies are using it to optimize processes, make data-driven decisions, offer innovative solutions and push the boundaries of technological possibilities. The diversity and applications of AI, including generative AI and large-scale language models, reflect its potential. There are different types of AI with different functions and application areas. In this article, we look at the different types of AI and how and where they are being used.
Auf einer Platine liegen dreidimensional die Buchstaben AI für Artificial Intelligence
5/5 - (6 votes)

Definitions of the types of artificial intelligence

Weak AI (weak/narrow AI): 

  • Weak AI can solve specific problems because it has been trained or built to do so. This form is used to perform specific tasks. The AI systems work on the basis of algorithms and are limited to certain areas or functions. Examples include chatbots or personalized recommendation systems.
 

You have already encountered weak AI systems in everyday life. They can be found, for example, in:

  • Character or text recognition programs
  • navigation systems
  • Speech recognition
  • Individual display of advertising
 

Strong AI (strong/general AI):

  • Strong AI can solve any problem without having previously gained experience with this exact problem. It can independently acquire new knowledge and solve more complex problems. Strong AI is therefore able to master the control of self-driving cars and react independently to changing situations in road traffic.
  • Research into this form of AI is continuing. The aim is to imitate the human brain, with this form of AI even simulating consciousness, understanding and emotions. 
 

If it were possible to develop such powerful artificial intelligence in the future, it would probably have the following characteristics:

  • Logical reasoning
  • Ability to make decisions despite possible uncertainty
  • Ability to plan and learn
  • Ability to communicate in natural language
  • Combination of all abilities to achieve an overarching goal

Machine learning and deep learning

Broadly speaking, deep learning is a sub-area of machine learning and machine learning is a sub-area of artificial intelligence. They can be thought of as a series of overlapping concentric circles, with AI being the largest, followed by machine learning and deep learning. 

Machine learning and deep learning are both types of AI. In short, machine learning is AI that can adapt automatically with minimal human intervention. Deep learning is a branch of machine learning that uses artificial neural networks to mimic the learning process of the human brain.

The following graphic provides an overview of these important differences:

AI definition

Machine Learning (ML): 

  • An area of artificial intelligence where systems learn and improve from data without being explicitly programmed. ML algorithms use historical data to recognize patterns and make predictions.
 
  • When is machine learning used? Machine learning (ML) is particularly effective when processing structured data that can come from various sources, such as databases or Excel spreadsheets. In such cases, the data fields have a defined meaning and structure. Using these structures, machine learning develops its algorithms to analyze this data and recognize patterns. These algorithms can then be applied to new, unknown data to generate insights and predictions.
 

Deep learning: 

  • A specialized form of machine learning that relies on neural networks modeled after the human brain. Deep learning enables systems to learn from unstructured data and recognize complex patterns.
 
  • When is deep learning used? Deep learning is particularly effective when processing unstructured data such as text, images, music or speech. In this area, it is characteristic that deep learning independently identifies the required structures. An essential prerequisite for the development of a high-quality model is extensive data collection, as deep learning processes require a large amount of data in order to achieve a high level of model accuracy.

Generative AI and large language models (LLMs):

The world of Generative AI is diverse and refers to a broad category of AI applications that are capable of creating content on their own. And although not all tools are based on Large Language Models (LLMs), all LLMs belong to the Generative AI family. More precisely, they represent the part that generates text.

Large Language Models (LLMs): 

  • Large language models (LLMs) represent a specialized form of generative AI that offers revolutionary capabilities in dealing with text and language. They are trained to understand, interpret and generate human language at a level that was unimaginable until recently. These models, which include prominent examples such as GPT (Generative Pre-trained Transformer), can handle complex language tasks, from text generation to question answering and translation between languages.
 
  • The development and functioning of LLMs is based on training with gigantic amounts of text data, through which they learn to recognize patterns, nuances and contexts of human language. This ability enables LLMs to interact with people in natural language and generate high-quality content. By applying deep learning and machine learning, as discussed in the section above, LLMs can continuously learn from new data and adapt to improve their performance and accuracy.
 
  • In practice, LLMs open up new horizons. For example, they can be used to improve chatbots by enabling them to provide more natural and contextualized responses. This significantly increases customer satisfaction and efficiency in customer contact. In addition, LLMs can be used in the analysis and processing of customer data to gain deeper insights and create personalized content. The ability to learn from unstructured data sets makes LLMs an invaluable tool for creating and optimizing content tailored to the specific needs and requirements of users
 

Generative AI: 

  • This type of AI technology has fundamentally changed the way we think about machine creativity and content production. Generative AI models are designed to learn from existing data and use this information to generate new content that resembles the learned patterns. This technology has a wide range of applications, from generating text and creating visual art to composing music and creating realistic video footage.
 
  • A key element of generative AI is its ability to recognize complex patterns and structures in the data it has been trained with. By training with large data sets, a generative model can learn to produce text, images or music that are meaningful and aesthetically pleasing to humans. These models utilize a variety of techniques including, but not limited to, generative adversarial networks (GANs), autoencoders and transformer architectures, to name a few.
 
  • In practice, generative AI offers revolutionary opportunities for companies. Here, generative AI can be used to automatically create precise summaries from long documents. This not only saves valuable time, but also increases the efficiency of information processing and analysis. In addition, generative AI can be used in customer communication to respond to customer inquiries with generated answers that are so precise and helpful that they are almost indistinguishable from human-written responses.

The role of AI in Intelligent Document Processing (IDP)

Our IDP solution uses advanced AI technologies to automate your document processing processes. This involves the use of machine learning algorithms and deep learning algorithms. The latter make it possible to analyze and understand large volumes of unstructured data and convert it into useful information. Our AI models continuously learn and improve with every interaction, significantly increasing the accuracy and efficiency of your document processing.

Benefits of AI for businesses:

  • Increased efficiency: workflow automation of your routine tasks reduces processing time and increases your productivity.
  • Data-driven decisions: By analyzing large amounts of data, such as unstructured data, you can make informed decisions.
  • Driving innovation: AI technologies enable digital transformation and the development of new products and services.
One platform,
endless possibilities.

ExB is an Intelligent Document Processing platform that transforms unstructured data from any type of document into structured results. Our AI-based software can not only extract all relevant information from your documents, but also understand them. This allows you to automate your processes and save both time & money, while improving your customer experience and employee satisfaction. Win-win. 

illustratio-exb-product_demo-g35-loy

The four types of artificial intelligence

The development of artificial intelligence technology has led to the categorization of four main types that illustrate the range of its capabilities and stages of development. This classification helps to understand how AI systems work and what potential they hold for the future.

Type 1 – reactive machines:

  • Reactive machines are the most basic form of artificial intelligence, designed to solve specific tasks or problems without storing or learning from past experience. They respond to direct stimuli and can handle very complex situations as long as they are within their programming framework (weak AI). A well-known example is the chess program Deep Blue, which defeated the world champion Garry Kasparov.
 

Type 2 – limited memory:

  • AI systems with limited memory can go beyond their reactive capabilities by using data from the recent past to make decisions. This type of AI integrates historical data into its algorithms to improve its behavior for future tasks. Through machine learning, for example, it is trained with data and by using neural networks and supervised learning, it is able to develop an extensive collection of data and courses of action (strong AI).
 
  • In contrast to reactive machines, it can therefore draw on past experience to make better decisions. However, it is important to note that it can only access limited information. Autonomous vehicles that process traffic data in real time are an example of this category.
 
  • Another example is your smartphone. It most likely contains a voice assistant such as Siri or “Ok Google”. These assistants also belong to the second type of AI (limited memory) and can respond to voice commands, for example to retrieve data from the internet, organize your calendar or manage other apps and information. AI-supported applications such as ChatGPT also fall into this category.
 
  • It is obvious that artificial intelligences of the second type can be extremely diverse in their functions. However, an AI is not categorized solely by its range of functions, but rather by its functionality. A chess computer (type 1) can directly calculate the best possible counter-move using the AI algorithms when you make a move. However, a chess computer can only fulfill this one task, namely playing chess. A type 1 chess computer also does not build up character types of opponents such as: Opponent A generally plays more defensively, so strategy A must be chosen. For player B, who plays more offensively, strategy A or B can be chosen.
 
  • The AI of the second type, on the other hand, can plan ahead to a certain extent. For example, a voice assistant can make suggestions on other topics that could be relevant to you, and a self-driving car can reach its destination safely despite unforeseen events. ChatGPT can also select the right ones for your text from a wealth of data. Although ChatGPT may appear more powerful than Siri, it lacks the crucial characteristic of a type 3 AI: the ability to understand or even feel emotions. Therefore, even the supposedly powerful ChatGPT software is “only” classified as a type 2 AI.
 

Type 3 – theory of mind:

  • “Theory of mind” is an advanced type of AI that researchers are working on, but is not yet fully realized. These AI systems should be able to understand human thoughts and emotions and make decisions based on them. The aim is to develop machines that understand human psychology and can interact in social contexts.
 

Type 4 – Self-awareness:

  • Self-aware AI represents the pinnacle of AI development, a stage where machines would have their own consciousness, feelings and self-awareness. Such systems would not only understand the world around them, but also their own existence and role in it. This type of AI remains largely theoretical and is the subject of science fiction and future research.

Application of AI types in our IDP solution

Our IDP solution relies in particular on AI type 2 (Limited Memory) and partly on AI type 1 (Reactive Machines) to automate document processing tasks. By integrating algorithms that learn and adapt from data, we offer you a dynamic and efficient solution for processing unstructured data in your organization. 

While the visions of “theory of mind” and “self-awareness” represent the long-term ambitions of AI research, we focus on offering you practical and ready-to-use solutions today that will help you optimize document processes and increase your efficiency.

With over 20 years of experience and a team led by Europe’s leading AI expert Dr. Ramin Assadollahi, we have made it our mission to develop the ultimate understanding machine. A machine that can not only process data, but also understand it and thus support each company individually.

Would you like to experience how intelligent document processing can help your company speed up processes and make data-driven decisions? Then click on book a demo now or contact us to find out more about our AI-powered IDP solution.

Index

Written by:

Simon Rauch

Content Creator bei ExB

Simon is responsible for creating marketing content at ExB. With his expertise in the areas of AI trends and editing, he enriches ExB’s information offering – on our blog, on LinkedIn and YouTube.
Stay up to date:

Was this article useful?

5/5 - (6 votes)

These articles might also interest you

Process automation

Data plays a fundamental role in today's business world, which is why a comprehensive understanding of data is more important than ever in the age of digital transformation. A basic distinction can be made between three main types of data: structured, semi-structured (semistructured) and unstructured data. This article will focus on the latter type of data: As a pioneer in AI-based data processing of unstructured formats, we have summarised everything you need to know about this topic.

Process automation

Workflow automation is the key to efficient business processes: The approach describes the use of tech­no­logies to auto­matize work­processes. The technologies, which are often AI-based, enable the seamless inte­gration, execution and monitoring of tasks and steps within a work­flow. The main goal of work­flow auto­mation is to identify work­tasks that can be auto­mati­ed with tools. This leads to faster, more precise execution of tasks, which increases efficiency and productivity.

AI

Natural Language Processing is a subcategory of Artificial Intelligence: NLP enables machines to understand and generate human language. NLP deals with the interaction between human language and computers. It is about enabling machines to understand and process natural language (i.e. language generated by humans) in the same way that humans do. Find out everything you need to know about this topic in our guide.

Kostenloser Download:

Whitepaper: Die Zukunft der Logistik

Erfahren Sie, wie Intelligent Document Processing (IDP) die Lieferkette revolutioniert.

Unser Whitepaper behandelt:

  • Aktuelle Herausforderungen in der Logistik
  • Was ist IDP?
  • Vorteile von IDP in der Logistik
  • Use Cases aus der Praxis
  • Stolperfallen und Herausforderungen

 

Laden Sie hier gleich Ihr kostenloses Whitepaper-Exemplar herunter und revolutionieren Sie Ihre Lieferkette mithilfe von KI!

Kostenloser Download:

Whitepaper: Lohnt sich KI?

Sieben typische Fragen über KI beantwortet:

  1. Kann uns KI dabei helfen, unsere eingespielten Prozesse zu digitalisieren?
  2. Gibt es bereits KI-Lösungen für administrative Prozesse?
  3. Was ist der Unterschied von OCR und KI?
  4. Worin besteht der Unterschied zwischen regelbasierten und KI-Lösungen?
  5. Können historische Daten zum Antrainieren verwendet werden?
  6. Muss KI-gestützte Dokumentenverarbeitung immer teuer sein?
  7. Wie berechnet man die Kosten und den ROI eines KI-Projekts?

Laden Sie hier gleich Ihr kostenloses Whitepaper-Exemplar herunter und erfahren Sie die Antworten auf diese Fragen!