What Artificial Intelligence, LLMs, and AI Tools Really Are

Artificial Intelligence Explained Simply: Origins, Types, LLMs, AI Chips, and the Future

Artificial intelligence, or AI for short, is one of the most important technologies of our time. Many people encounter it on a daily basis, for example in search engines, translation services, chatbots, voice assistants, image generators, or recommendation systems. Nevertheless, it is often unclear exactly what AI is, where it comes from, and what technically distinguishes it from traditional software. Artificial intelligence is defined as a technology that enables computers to perform tasks that previously required human abilities such as learning, understanding, problem-solving, or language processing.

This article explains the history of artificial intelligence from its early beginnings to the present day, describes the main types of AI, highlights the differences between AI chips, CPUs, and other processors, explains large language models and APIs, and defines what an AI tool actually is.

The Origins of AI: When Did the Concept of Artificial Intelligence Begin?

The origins of artificial intelligence do not lie in a single invention, but rather in the combination of mathematics, logic, computer science, and the question of whether machines can replicate mental processes. An early milestone was Alan Turing’s 1950 essay “Computing Machinery and Intelligence.” In it, Turing posed the famous question of whether machines can think and formulated the so-called Imitation Game, which later gave rise to the Turing Test.

The 1955 Dartmouth proposal for the Dartmouth Summer Research Project on Artificial Intelligence, which was scheduled to take place in the summer of 1956, is generally considered the true starting point of AI research as a distinct field. The term “artificial intelligence” was explicitly used in this proposal. For this reason, Dartmouth is often referred to today as the birthplace of modern AI research.

It is important to note that artificial intelligence was not “discovered” like a law of nature. It emerged as a scientific concept based on the idea that certain aspects of human intelligence can be described mathematically and replicated technologically. It was precisely from this that a distinct field of research and application later developed.

The History of AI: From Its Beginnings to the Present Day

Following the early sense of optimism in the 1950s and 1960s, the first programs were developed that demonstrated logical reasoning, natural language processing, and symbolic problem-solving. Even back then, there were early milestones such as early chatbots, expert systems, and robotic systems. A well-known example is MYCIN from the 1970s, one of the first expert systems designed to assist with medical diagnoses.

However, the initial euphoria was followed by disillusionment and setbacks. In 1973, a critical report in the United Kingdom played a major role in the reduction of funding. This phase is now referred to as the first AI winter. In the 1980s, expectations and investments declined further after many hopes for rapid breakthroughs had not been fulfilled.

Machine learning began to gain prominence in the 1980s and 1990s. Of particular importance was the development and subsequent practical application of backpropagation, which made it possible to train multilayer neural networks. At the time, this development was considered the foundation for what would later become deep learning. In the 2000s and 2010s, increased computing power, large amounts of data, and GPUs then led to the well-known breakthroughs in image, speech, and pattern recognition.

Generative AI and large language models brought about a major shift in public perception in the 2020s. An early prominent example of a very large language model was GPT-3, which could handle many language tasks with little additional training. Since then, AI systems have become widespread and are now found in everyday software, business applications, and digital devices.

What is AI? Explained simply

Simply put, artificial intelligence is a technology that enables computers to perform tasks that previously required primarily human skills. These include, for example, understanding language, recognizing patterns in images, translating text, predicting trends, and answering questions. IBM describes AI as a technology that enables machines to mimic human learning, understanding, problem-solving, decision-making, creativity, and a certain degree of autonomy.

For beginners, AI can be summarized as follows: An AI system receives data, identifies patterns within it, and uses those patterns to solve a problem. A spam filter recognizes typical characteristics of unwanted emails. An image recognition system identifies specific shapes and structures. A language model recognizes relationships between words and sentences.

What types of AI are there?

AI can be classified in various ways. Two perspectives are particularly common:

• based on the scope of services
• based on how it works

This distinction is helpful because it answers two different questions:
first, how widely AI can be applied, and second, how it fundamentally works.

Types of AI that are already in use today

Weak AI or Narrow AI

Narrow AI is the form of AI that is actually in use today. It is specialized for specific tasks and does not possess general human-like intelligence. Such systems are often very good at solving individual problems, but they are not universally applicable.

Practical examples:
search engines, spam filters, chatbots, image generators, voice assistants, automatic transcription, product recommendations in online stores, translation services.

Reactive AI

Reactive AI processes only the information available at the present moment. It has no true memory of past experiences or previous states. Such systems therefore respond directly to the current input without learning from the past.

Practical examples:
Early game systems such as Deep Blue, simple rule-based systems, and clearly defined decision-making systems without a long-term context.

AI with limited memory

This type of AI uses current data and also incorporates a limited amount of past information. Many of today’s AI systems fall into this category. This means they can draw on previous inputs or observations to a certain extent without actually possessing a comprehensive understanding or awareness.

Practical examples:
voice assistants, numerous chatbots, driver assistance systems, recommendation systems, autonomous driving features, and modern generative AI applications.

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Superintelligence or ASI

ASI, or Artificial Superintelligence, refers to a hypothetical form of AI that would significantly surpass humans in nearly all intellectual domains. This concept is frequently discussed in debates about the future, but has not yet been technically realized.

ASI is, for now, a theoretical future scenario.

Theory of Mind AI

"Theory of mind AI" refers to AI that could truly understand human thoughts, intentions, emotions, and social contexts. Such AI would therefore not only have to react to data, but also be able to recognize the inner states of others and interpret them meaningfully.

At most, there are some early research efforts in the field of social or emotional interaction.

Confident AI

Self-aware AI would be AI with its own consciousness and a genuine sense of self. It would not merely process information, but would possess its own internal experience or a conscious self-model.

In practice, almost all AI used today is of the "weak" variety. This primarily includes reactive systems and systems with limited memory. While this type of AI can already be very useful and powerful, its applications remain limited to specific tasks or fields.

General AI, superintelligence, theory-of-mind AI, and self-aware AI, on the other hand, remain largely in the realm of theory or research. They are important conceptual models, but not real technologies available today.

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What is an AI chip?

An AI chip is a specially designed chip or hardware accelerator for AI models. These chips are designed to perform typical AI computations particularly quickly and efficiently.

The reason is simple: Modern AI, particularly neural networks and large language models, requires enormous amounts of computational power. This means that AI chips use parallel processing to accelerate the work of neural networks and, as a result, boost the performance of applications such as chatbots and generative AI.

How is an AI chip constructed?

There is no single standard architecture for every AI chip, but typical AI accelerators feature many specialized processing units designed for parallel processing. NPUs are specialized microprocessors optimized for neural networks, deep learning, and machine learning. This means that NPUs process large amounts of data in parallel and are designed to execute AI tasks locally and efficiently.

Simply put, an AI chip typically consists of many processing units for matrix and tensor operations, fast data paths, and memory structures tailored to these operations. The focus is less on general control logic and more on massively parallel mathematics. That is precisely what makes such chips so valuable for AI models.

What is the difference between an AI chip, a CPU, and other chips?

The CPU (Central Processing Unit) is the classic general-purpose processor. It excels at general system control and sequential processing. IBM describes CPUs as processors that execute instructions one after another, making them well-suited for many general-purpose tasks.

The GPU (Graphics Processing Unit) was originally designed for graphics, but is particularly well-suited for parallel computing. That is why it is now central to many machine learning and deep learning tasks. IBM emphasizes that GPUs are better at breaking down large tasks into parallel components and are therefore often faster and more efficient than CPUs for computationally intensive AI applications.

The NPU (Neural Processing Unit) or, more generally, the AI chip is even more specifically designed for AI tasks. In other words, the NPU is a processor that accelerates AI-based operations and can offload the CPU and GPU for other tasks. In practical terms, this means:

• CPU = All-purpose processor for general computing and control tasks
• GPU = powerful for large-scale parallel processing
• NPU / AI chip = highly efficient for AI operations, often used locally on devices or as a specialized accelerator

What is a large language model?

A Large Language Model, or LLM for short, is a large language model. More specifically, LLMs are deep learning models trained on very large datasets, enabling them to understand and generate natural language. LLMs are based on the Transformer architecture, which is particularly adept at handling word sequences and language patterns.

Simply put, an LLM learns statistical patterns of language from a vast amount of text. It recognizes which words, terms, or parts of a sentence are highly likely to go together. This enables it to answer questions, write text, summarize content, translate, or assist with programming. At the same time, this also means that an LLM is not a human consciousness, but rather a mathematical model that responds to patterns and probabilities.

What LLMs are available?

The list of large language models changes regularly. According to official documentation, the major model families currently include OpenAI GPT, Anthropic Claude, Google Gemini, Mistral, and Cohere Command (or Aya). In its current API documentation, OpenAI recommends gpt-5.4 as the flagship model for complex reasoning and coding. On April 16, 2026, Anthropic introduced Claude Opus 4.7 as its current model; the Claude API documentation also lists successors such as Claude Sonnet 4.6 and Claude Haiku 4.5. Google lists Gemini 3.1 Pro and Gemini 3 Flash, among others, in its current documentation. Mistral and Cohere also document their own model overviews and API platforms.

It is important to note that an LLM is not a single brand, but rather a class of models. Different providers develop different models with different strengths, such as fast response times, complex reasoning, coding, document analysis, multilingual capabilities, or multimodal processing.

Typical applications of LLMs

Large language models are used in many fields today. Typical applications include text generation, summarization, question-answering systems, customer support, knowledge assistants, code assistance, translation, document analysis, and research support. IBM generally describes LLMs as models that can understand and generate language and other content to perform a wide variety of tasks.

In practice, this means, for example, that a company might use an LLM for a support chatbot, a marketing team for drafting copy, a development department for code assistance, a law firm for analyzing long documents, or a knowledge platform for searching internal documents. It is precisely this versatility that has made LLMs so relevant in such a short time.

What is an API, and why is it needed for LLMs?

An API is an application programming interface. It enables software to communicate automatically with another service. In the case of large language models, this means: A custom website, app, WordPress plugin, CRM system, or internal enterprise software can directly access a language model without users having to manually interact with the model provider’s interface. According to official documentation, OpenAI provides its models via the Responses API and client SDKs. Anthropic describes the Claude API as a RESTful API for programmatic access to Claude models and Claude Managed Agents. Google explains that the Gemini API requires an API key, which is created and managed in Google AI Studio.

APIs are essential whenever AI is intended to be more than just a chat window and becomes part of a real-world application. Typical examples include website chatbots, automated email processing, AI-powered search functions, document analysis, AI features in apps, and internal corporate assistants. Cohere puts it very simply: With an API and SDK, developers can integrate LLMs into applications with just a few lines of code and an API key.

What is an AI tool?

An AI tool is the specific application that makes AI usable for a particular purpose. The AI model is the technical engine in the background, the API is the interface for integration, and the tool is the finished product that users see and use. This classification stems directly from the way AI models and APIs are used in products.

An AI tool can be, for example, a chatbot, a translation tool, an image generator, a meeting assistant, a transcription service, a document analysis system, or a coding assistant. An AI tool is therefore not a separate basic technical category like “LLM” or “NPU,” but rather the practical product form of AI.

Benefits of AI

Artificial intelligence can assist people in many areas. Among its key benefits are faster information processing, automation of repetitive tasks, improved pattern recognition, language and translation support, accessibility aids, and new opportunities in research, medicine, and education. Google explicitly describes AI as a technology with the potential to bring about positive change for people and societies.

The key benefit often lies not in completely replacing humans, but in lightening their workload and complementing their work. AI can analyze large amounts of data more quickly, organize complex information, and save time on routine tasks. This creates more room for evaluation, responsibility, creativity, and interpersonal decision-making. This positive outlook is also consistent with the broad fields of application for AI and LLMs described by official providers and foundational sources.

Risks of AI

Alongside the opportunities, there are also real risks. These include incorrect answers, fabricated answers (so-called hallucinations), biases in training data, a lack of transparency, misuse, data protection issues, and an overreliance on automated decisions. It is already clear from the official model and API documentation that providers explicitly address security, model behavior, and controlled use. Anthropic, for example, describes safety assessments and differences in the behavior of its models.

When it comes to language models in particular, it is important to understand that well-formulated answers are not automatically correct. An LLM can sound very convincing yet still generate factually incorrect content. That is why human review remains indispensable in many fields, particularly in education, medicine, law, technology, and finance.

Outlook for the Future of Humanity

A fact-based, positive outlook on AI is this: When artificial intelligence is developed and used responsibly, it can significantly support people in many areas of life and work. Official sources already show that AI can understand language, recognize patterns, structure information, make knowledge accessible, and assist with complex tasks. This opens up great opportunities for education, research, accessibility, productivity, and medical innovation.

The vision for the future, then, is not that machines will replace humans, but that AI will become a tool that enhances human capabilities. It can help make knowledge more accessible, facilitate communication, make complex relationships easier to understand, and reduce repetitive work. At the same time, it remains crucial that humans set goals, establish rules, and assume responsibility.

That is precisely the major challenge we face in the coming years: to use AI in a way that maximizes its benefits without ignoring the risks.