Concepts

Artificial Intelligence (AI) is according to Wikipedia‘s definition, it is a computer or a computer program capable of performing actions considered intelligent. A more precise definition of artificial intelligence is open, because intelligence itself is difficult to define.

Artificial Narrow Intelligence (ANI) is a type of artificial intelligence designed to perform a specific task or set of tasks. It is also known as weak artificial intelligence or adaptive artificial intelligence. All artificial intelligence systems in use today, such as voice assistants Alexa and Siri, Tesla’s driving assistant or ChatGPT, are so-called narrow artificial intelligence applications.

Artificial General Intelligence (AGI) is a type of artificial intelligence capable of learning any intellectual task performed by a person. It is a hypothetical concept that has yet to be achieved in practice, but is often used as a benchmark when evaluating the capabilities of current AI systems. Learning to drive a car, cook, analyze a large amount of data, any work task independently is the goal of all development.

Generative AI is a branch of artificial intelligence that focuses on creating new data or content rather than simply analyzing or classifying existing data. This can include, for example, generating content in the form of text, images, audio, or other forms. Generative AI utilizes deep learning models, meaning they improve themselves with use, for example through feedback.

Generative AI works with two main components: a generator and a discriminator. The generator creates new, authentic-feeling results, such as paintings in our example. The discriminator, on the other hand, evaluates these creations and compares them to the original learned models – it tries to distinguish real artworks painted by real artists from images created by artificial intelligence. The generator then tries to improve its creations based on the discriminator’s feedback until it produces something that the discriminator thinks is the right piece.

This process is like an evolving game where the generator and the discriminator compete with each other. This “game” helps AI learn and develop new, creative ideas that can mimic or even surpass original designs. And this is the essence of generative AI: it not only learns to understand data, but also creates new, innovative ideas based on what it has learned.

Artificial intelligence = Support intelligence

We should rather talk about artificial intelligence as support intelligence. It functions excellently as an assistant, ideator, sparring partner, mentor, enhancer, etc. However, it does not remove the subject matter expertise from the user but, on the contrary, emphasizes it, so that we can verify the accuracy of the information produced by artificial intelligence. It also supports independent thinking.

Language models

Language models (LLM) are the ‘engines’ of generative artificial intelligences, which, among other things, can read, summarize, and translate texts (Watch a video about the technology behind language models (YouTube, opens in a new window)). They are capable of processing human-generated text. They predict future characters in a string based on probabilities formed through machine learning, allowing them to create sentences similar to those spoken and written by humans. The task of a language model is thus to generate human-like, fluent text based on the input (prompt) given to it. The most common way to provide input to an AI application is through text typed into a text field.

A grammatically correct and reasonable-sounding text creates an illusion of the correctness of the answer, even though it may be completely distorted. So substantial knowledge of the subject is still needed. Often, checking the accuracy of the text written by artificial intelligence and marking the sources is more laborious than actually producing the text based on the sources.

Artikkeli: Näin ChatGPT syntyi – kukaan ei täysin ymmärrä, miten kielimallit toimivat (Tivi 7.9.2023) – avautuu HAMK:n tunnuksilla (only in Finnish)

The language model is not intelligent

A Language model is just a program that can generate text based on probabilities based on the source material in response to the input it is given. It has no knowledge or understanding of the content, although a fluent and grammatically correct answer may give a vague picture. The responsibility for the correctness of the written information rests with the artificial intelligence user.

Language models are generally able to process input content in almost all languages ​​in the world, including programming languages.

Language models serve as the basis for generative artificial intelligence applications. That is, they are able to create answers based on wishes given as inputs. For example, ChatGPT produces text that is at best indistinguishable from text written by a human. In addition, image generators, such as Nano Banana, are able to create and edit images based on text inputs given to them. Each application can be taught its own specific task using machine learning. Machine learning is a branch of artificial intelligence that aims to make an application work better based on background information and possible user actions. The apparent intelligence is due to the large resource to calculate and compare existing statistical results in its language model with things and patterns connecting things, which it has formed from a huge amount of content material.

Limitations of generative artificial intelligence

Artificial intelligence reflects the source material fed to it. As a limitation to its use, it is important to remember that the information may not be accurate, i.e. hallucination in the answer. In addition, it may provide biased or damaging information, which is due to the source material used in teaching the language model. This is because the already biased source material used to teach the language model. The fact that content materials from Africa, for example, have not been used as source material weakens the quality of the application and its ability to produce impartial and equal information about African cultures. The responsibility for the accuracy of the written information lies entirely with the user of the artificial intelligence. Artificial intelligence itself does not care whether something is true, which it generates for the user, because its task is only to generate text for example. The limitations, such as hallucination, appear in the details of the generated information.