Why AI makes mistakes when performing tasks

Text from the Pitch Avatar team to help avoid “miscommunication” when working with artificial intelligence.

Anyone who interacts with conversational AI has likely encountered that it’s far from always up to the task. It may sometimes provide incomplete answers, fail to retrieve specific information, or produce stylistically awkward responses with cumbersome phrasing, logical inconsistencies, and repetitive elements. A significant issue is ‘machine hallucinations,’ where the AI generates deliberately erroneous information, including fictitious names, works, quotes, and references.

Why does this happen? For clarity, let’s outline the main reasons for errors in conversational AI interactions in the form of a list:

• Limitations related to the training data. Artificial intelligence learns from vast datasets but lacks human-like understanding. It learns to reproduce the types of relationships and structures it sees in the information it receives. From this, it tries to predict which words or phrases are most likely to be followed by others. As large as the amount of data used to train dialog AI is, it still contains solid gaps. It is theoretically impossible for AI to have comprehensive knowledge of everything in the world, as humanity’s ‘database’ is expanding too rapidly.

• Lack of fact-checking capability. AI lacks the ability to critically analyze facts or verify information in the way humans do. It generates responses based on the data it has been trained on, meaning that if the training data contains inaccuracies, AI can reproduce those errors. Additionally, conflicting information within the data can lead to inconsistent responses. To address these issues, conversational AI typically needs to be retrained with updated and corrected data.

• Limitations of specific AI models. Virtually all conversational AI has inherent limits to its capabilities. The most common example is learning only from data available up to a certain point in time and not being able to learn or adapt in real-time.

• The complexity of natural language. Natural language is an incredibly complex system, ill-equipped to reflect absolute truth. Too much depends on the context of the conversation and the worldview of the interlocutors. The multifaceted and ever-evolving nature of human language poses a significant challenge for AI. Many nuances that can be understood only in a certain context often lead to the generation of erroneous information. Because of the ambiguity of natural language, AI can misinterpret a user’s query. It’s a good time to reiterate one of the most common tips for communicating with conversational AI: Keep tasks as short and unambiguous as possible, avoiding slang, ambiguity, and subtext.

• Lack of worldview. Unlike humans, AI lacks a common understanding of the world shaped by upbringing, societal culture, and personal experience. As a result, AI cannot rely on a coherent worldview when generating responses. This often leads to off-topic or irrelevant information, particularly in response to broad or general inquiries.

• Desire to fill knowledge gaps (“machine delusions”). One of the main reasons for so-called “machine hallucinations” is that when a dialog AI receives a user query, it tries to generate a response that is most likely to match that query based on its learning. If the AI encounters lack of information to generate a complete answer, it may try to “fill in the gap” based on what it has seen in the data. This can lead to generating information that is a kind of assumption. It seems plausible, but is actually fictitious. Unfortunately, unlike humans, modern AI does not yet have the skill to test its assumptions based on personal experience, intuition, or contextual understanding.

We hope this information will help you use AI-based tools more effectively, such as our online content assistant, Pitch Avatar.

Wishing you good luck, success, and high profits!