Problems of using neural networks for scientific work
Posted: Sun Dec 22, 2024 5:18 am
ChatGPT gets confused in complex and multi-level tasks. Thus, one of the unsuccessful sections of the scientific article being created was the literature review and hypothesis testing.
The authors concluded that ChatGPT is just a tool that needs philippine whatsapp number to be used wisely. It is no worse than Wikipedia or any other electronic source of information. And it also needs to be verified by a human.
The main problem with using ChatGPT is that the neural network does not understand what exactly it is writing. It takes a large array of text and imitates it. This is the difference between a person and AI: a person tries to convey his thoughts through text, to prove something, while a neural network formulates a coherent text on a given topic, without having its own goal.
This is where the main problems arise. The first and most important is the lack of fact-checking or fact-checking. Here are some examples of how ChatGPT is used.
Request for conclusions for a scientific article on the inadmissibility of using neural networks:
Neural network models may make incorrect decisions or have uncertain or unjustified beliefs, which may lead to negative consequences. Therefore, serious consideration of the ethical and legal issues associated with the use of neural networks is necessary before their use in critical areas.
Request for an abstract for a research paper on the use of slang words.
A study of slang words in everyday speech shows that these words, which are derived from the vocabulary of specific socio-cultural groups, are used as a means of identification and expression of collective identity.
As you can see, we get a completely coherent text that could be used in a scientific paper. However, let's look at the other two fragments.
The authors concluded that ChatGPT is just a tool that needs philippine whatsapp number to be used wisely. It is no worse than Wikipedia or any other electronic source of information. And it also needs to be verified by a human.
The main problem with using ChatGPT is that the neural network does not understand what exactly it is writing. It takes a large array of text and imitates it. This is the difference between a person and AI: a person tries to convey his thoughts through text, to prove something, while a neural network formulates a coherent text on a given topic, without having its own goal.
This is where the main problems arise. The first and most important is the lack of fact-checking or fact-checking. Here are some examples of how ChatGPT is used.
Request for conclusions for a scientific article on the inadmissibility of using neural networks:
Neural network models may make incorrect decisions or have uncertain or unjustified beliefs, which may lead to negative consequences. Therefore, serious consideration of the ethical and legal issues associated with the use of neural networks is necessary before their use in critical areas.
Request for an abstract for a research paper on the use of slang words.
A study of slang words in everyday speech shows that these words, which are derived from the vocabulary of specific socio-cultural groups, are used as a means of identification and expression of collective identity.
As you can see, we get a completely coherent text that could be used in a scientific paper. However, let's look at the other two fragments.