TESTING THE ARTIFICIAL INTELLIGENCE SERVICE DEEPAI.ORG ON ITS ABILITY TO FOLLOW THE RULES OF FORMAL LOGIC

Actual scientific researches and issues analysis. Research on large language models (LLM) compliance with the rules of formal logic has been conducted by various groups of scientists led by R. Nishant, J. Wan, M. Amirizani, T. Liu, and H. Chi. The work of S. Wang demonstrates that AI's mastery of the fundamental rules of inference still falls short of human capabilities and suggests approaches for testing and enhancing AI's logical skills. E. Larson shows why artificial intelligence cannot understand texts; however, he does not specifically investigate artificial intelligence's adherence to the rules of formal logic. B. Lin explores the use of mathematical methods to improve artificial intelligence accuracy. Research indicates that artificial intelligence can be trained to follow the rules of formal logic, but this process may require significant effort and resources. The research objective. The task of this article is to test the artificial intelligence service at https://deepai.org/chat for its ability to adhere to the rules of formal logic, specifically solving problems related to modus ponens and modus tollens. Understanding this aspect will allow for a better understanding of the capabilities of language-based artificial intelligence and assist users in obtaining more accurate results. The statement of basic materials. The article is dedicated to analyzing logical inferences, particularly the rules of modus ponens and modus tollens in the context of interaction with artificial intelligence (AI). Two examples are examined. In the first case, from two premises (“If it is raining outside, then the pavement is wet” and “The pavement outside is wet”), artificial intelligence makes an incorrect conclusion, asserting that “it is raining outside,” despite this being an incorrect application of modus ponens. The authors emphasize that common sense indicates that the pavement could be wet for other reasons. In the second example, concerning modus tollens, artificial intelligence reaches yet another incorrect conclusion. Again, it is noted that a correct conclusion can only be drawn from proper logical thinking schemes. Although artificial intelligence has succeeded in some logical tasks, the author considers this a coincidence rather than confirmation of logical thinking ability. Testing for errors in conclusions shows that even when artificial intelligence formulates conclusions incorrectly, it can indicate the need for additional information to make a correct inference. Thus, the article highlights the limitations of contemporary artificial intelligence models in understanding logic and underscores the necessity for critical thinking on the part of users when interacting with them.