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. Learn to drive a car, cook, analyze a large amount of data, any job task. And this is the goal of all development.

Artificial intelligence = Support intelligence

Artificial intelligence should rather be referred to as support intelligence. It works brilliantly as an assistant, ideator, sparring partner, mentor, enhancer, etc. However, it does not remove the substantive knowledge of the subject from the user, but on the contrary requires it so that we can check the correctness of the information produced by artificial intelligence. It also supports independent thinking.

Language models

Large Language Models (LLM) are artificial intelligence tools that can read, summarize and translate texts. They predict future words in a string of words based on probabilities generated by machine learning, allowing them to create sentences similar to how humans speak and write. The task of the language model is to generate human-like fluent text based on the input (prompt) given to it. The most common input method for an artificial intelligence application is text written in 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.

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 can handle prompt content in dozens of languages, including programming languages, but this varies from application to application.

Language models serve as a basis for generative artificial intelligence applications. That is, they are able to generate answers according to the wishes given by the inputs. For example, ChatGPT is able to produce text that, at best, cannot be distinguished from text written by a human. In addition, image generators such as DALL-E 2 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 field of artificial intelligence, the purpose of which is to make the application work even better with basic information and a potential user based on activity.

Limitations of generative artificial intelligence

Artificial intelligence reflects the source material fed to it. As a limitation to use, you have to remember the possible incorrectness of the information, i.e. hallucinations in the answer. In addition, it may offer biased or damaging information due to the source material used in teaching the language model. This is because the vast majority of the source material used to teach the language model comes from Western countries. The fact that materials from, for example, China or Africa have not been used as source material, weakens the quality of the application and the ability to produce unbiased information taking into account different cultures. The responsibility for the correctness of the written information rests with the artificial intelligence user. AI itself doesn’t care if something is true that it generates for the user.

GPT-3

GPT-3 (Generative Pre-training Transformer 3) is the third version of the language model developed by OpenAI, released in spring 2020. It is trained with a large amount of text to predict the next word in a sequence of words based on what words are before it (“if-then”- rule parameters) . For example, if the model is given the words “The man listens”, it will predict the next word to be “music”. There are 175 billion of these parameters in this language model.

The limitation is the incorrectness of the information, i.e. hallucination. In addition, it may offer biased or damaging information due to the source material used in the teaching of the language model. In addition, the data in the dataset has not been updated after December 2021.

Version 3.5 of this language model was the engine of the application ChatGPT, which was released in late November 2022.

GPT-4

This latest version of the language model was published on March 14, 2023. The number of parameters used by the language model or the size of the data model have not been disclosed, but it is said to be more creative, understand more complex instructions and be able to solve more complex problems than previous language models. Public data (internet) and licensed third-party libraries have been used as source material for the training of the language model.

It is believed to dominate and can help in even more demanding and complex creative and technical writing tasks, such as composing songs, writing scripts or learning the user’s writing style. In addition, GPT-4 accepts images as input and can create description texts from the content of the images, classifications and analyses. The ability to process larger amounts of text as input has also improved. GPT-4 can handle over 25,000 words of text, enabling use cases such as long-form content creation, extended conversations, and document search and analysis.

In addition to efficiency, GPT-4 is more accurate in terms of data accuracy. The accuracy has been raised to 70-80% depending on the subject. GPT-3.5 got 50-60% of the facts correct on average. But may still provide false or biased information like its predecessor. Its data set covers until December 2022.

This new language model is currently used in the paid version of ChatGPT, ChatGPT Plus.

Links

• OpenAI
  • Read what the company OpenAI is like
• GPT-4
• ChatGPT
• DALL-E 2