Artificial Intelligence in Brief: History, Types, and Generative AI
Introduction: what is artificial intelligence and what is generative AI
Artificial intelligence (AI) is a field of science and technology that builds programs and systems capable of performing tasks that normally require human intelligence. This includes speech and image recognition, decision-making, the analysis of vast amounts of data, and even the ability to learn from experience.
Although it sometimes seems like a new phenomenon, AI has long been part of our daily lives. When we use Google Translate for a quick translation, when our email automatically filters out spam, or when our phone corrects and predicts the next words we type, we are in fact relying on artificial intelligence.
In recent years a new direction has emerged - generative artificial intelligence (Generative AI, or GenAI). These are models that do not merely analyze data but create new content: text, images, music, program code, and even legal documents. The best-known example of such a model is, of course, GPT, which can write articles, hold a conversation, or generate images. In short, the difference is as follows:
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Classical AI is used primarily for recognition, classification, and analysis - for example, to detect whether an image contains a face, or to predict a trend in a financial market.
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Generative AI goes further - it does not just understand data but creates new content based on large databases of information.
A brief history of artificial intelligence
The history of artificial intelligence is full of phases of enthusiasm, skepticism, and breakthroughs. To understand where we stand today, it helps to trace its development across the decades.
Image: Tarjomyar, CC-BY-SA-4.0
The beginning: 1940-1960
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Alan Turing and the Turing test (1950) - The British mathematician Alan Turing laid the foundations of the idea of thinking machines and proposed a test to determine whether a computer could imitate human responses in a way indistinguishable from a human.
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First experiments - During this period the first programs capable of solving mathematical equations and logical problems were created, giving birth to the very discipline of "artificial intelligence."
First wave of optimism: 1960-1970
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Symbolic AI - Systems were built that used logical rules to prove theorems and to play games such as chess. This raised expectations that artificial intelligence was on the verge of reaching human-level thought.
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DARPA funding - In the United States, the military agency DARPA invested heavily in AI research, hoping for rapid practical results.
First "AI winter": 1970-1980
- Hardware limitations and the inefficiency of the algorithms led to a decline in enthusiasm. Expectations went unmet, funding shrank, and many projects were discontinued.
Second wave of optimism: 1980-1987
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Expert systems - Programs built on a base of expert rules found applications in medicine, engineering, and business. For the first time, companies began to use AI in practice.
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Limitations - Even so, these systems were too expensive and difficult to maintain. They could not adapt to new knowledge beyond their predefined rules.
Second "AI winter": 1987-1993
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After the initial enthusiasm for expert systems came a new period of disappointment. Their complexity and high costs led to a decline in interest.
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Nevertheless, academic circles continued research into machine learning and statistical methods, which would later become the foundation of the next resurgence.
Machine learning and big data: 1993-2010
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With the spread of the internet and ever more powerful computers, artificial intelligence began to use enormous datasets for training.
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Machine learning became a core approach and found applications in search engines, online recommendations, spam filters, and more. This was the period when AI began to become an invisible but indispensable part of millions of people's daily lives.
Deep learning: 2010 - to the present
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Neural networks - Inspired by the human brain, deep neural networks allow computers to recognize images, speech, and text with high accuracy.
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Breakthroughs such as Google DeepMind's AlphaGo, which defeated a world champion at the game of Go, proved that machines can surpass humans in complex strategies.
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The generative AI boom after 2022 - with the arrival of a new generation of generative models developed by organizations such as OpenAI (with GPT and ChatGPT) and DeepMind (with Gemini), artificial intelligence entered popular culture and the business world directly.
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Today, artificial intelligence is used in almost every domain - from self-driving cars to AI assistants for lawyers such as Praven Intelekt.
Types of artificial intelligence models
Artificial intelligence can be divided into several main categories that build on one another. Each successive group is more specialized, more powerful, and requires greater computational resources.
Artificial intelligence (AI)
This is the broadest category. It encompasses all the technologies and methods by which computers can imitate human abilities - to learn, recognize patterns, make decisions, and solve complex problems.
Machine learning (ML)
Machine learning is a subcategory of AI. It uses algorithms that analyze large datasets and discover patterns. Thanks to this, machines "learn" from examples and can adapt without everything having to be programmed by hand. Examples include spam filters, recommendations in online stores, and weather forecasts.
Deep learning (DL)
Deep learning is a special branch of machine learning that uses neural networks with many layers. They are inspired by the structure of the human brain and enable more in-depth data processing - for example, recognizing objects in photographs, voice control of devices, or medical diagnosis.
Generative artificial intelligence (Generative AI)
Generative AI is a subset of deep learning. Its strength lies in creating new content - text, images, music, or program code. These models do not merely analyze existing data but produce original results based on their training. ChatGPT, DALL·E, and MidJourney are examples of generative models that have changed the way we work and create.
Large Language Models (LLMs)
LLMs are a specific type of generative AI trained on enormous volumes of text data. They can understand, summarize, and create text that sounds natural and close to human speech. Such models are already in wide use - including in the legal field, through AI assistants like Praven Intelekt, which analyzes case law and generates legal texts tailored to Bulgarian law.
Artificial intelligence has come a long way - from the first experiments and the periods of disappointment to today's boom in generative models, which now create texts, images, and legal documents. Today AI is not only part of our daily lives but also a strategic tool for business and law.
With solutions like Praven Intelekt, Bulgarian lawyers and firms can harness the power of generative AI to work faster, more precisely, and with the confidence that they are keeping pace with global trends.
