Microsoft has recently introduced a new version of its software, incorporating an artificial intelligence (AI) assistant known as Copilot. This AI assistant is capable of performing a wide range of tasks, including summarizing conversations, presenting arguments, and writing computer code. Such advancements in AI technology appear to bring us closer to a future where machines can alleviate the mundane and repetitive aspects of our lives. However, despite their impressive capabilities, it is important to approach the use of large language models (LLMs) with caution. While these models are designed to understand user intent and generate responses based on prompts, they still require careful and skillful usage to ensure accuracy, reliability, and safety.
LLMs, a type of “deep learning” neural network, operate by analyzing the probability of different responses based on the provided prompt. For example, ChatGPT, a popular LLM, can provide answers on a wide range of topics. However, it is crucial to recognize that these responses are not based on actual knowledge possessed by the AI model. Instead, they are merely the most probable outcomes based on the given prompt. When users provide detailed descriptions of tasks, LLMs like ChatGPT and Copilot can excel at generating high-quality responses, such as text, images, and computer code. Nevertheless, blind trust in the intelligence of LLMs can lead to inaccuracies and unreliable outputs. It is essential to carefully evaluate and verify the responses, ensuring that the initial prompts are accurately reflected in the generated content.
The Importance of Subject Matter Expertise
To effectively verify and validate LLM outputs, it is necessary to possess a strong understanding of the subject matter. Expertise plays a crucial role in providing adequate quality assurance. This becomes particularly critical when using LLMs to bridge gaps in our own knowledge. In situations where users lack subject matter expertise, it becomes challenging to ascertain the accuracy of the generated outputs. This challenge is particularly evident in tasks involving text generation and coding, where the lack of expertise may prevent the user from determining whether the output is correct or not.
Risks in Reliability and Interpretation
One area where the reliability of LLMs is particularly critical is during meetings and discussions. While the AI-generated meeting notes are based on transcripts, they still rely on language patterns and probabilities, which can lead to interpretation problems. Homophones, words with similar pronunciation but different meanings, pose a challenge for AI systems lacking contextual understanding and nuance. Consequently, using AI to formulate arguments based on potentially erroneous transcripts introduces further problems with reliability. Verification becomes even more challenging when employing AI to generate computer code. While testing with data can validate technical functionality, it does not guarantee that the behavior aligns with real-world expectations. For example, an AI-generated sentiment analysis tool could classify sarcastic product reviews as positive, lacking the contextual knowledge to identify sarcasm. Verifying code output in nuanced situations like this requires expertise in software engineering principles.
The Complex Discipline of Programming
Programming is a complex discipline that encompasses various principles and practices aimed at ensuring code quality. Software engineering emerged as a field dedicated to managing and enhancing software quality. Non-programmers, lacking knowledge in programming and software engineering principles, may overlook critical steps in the software design process when relying solely on AI models for generating code. This overlook may lead to code of unknown quality, posing risks in real-world scenarios where accurate behavior is crucial.
While LLMs like ChatGPT and Copilot offer powerful capabilities, blind trust in their outputs can be risky. It is crucial to recognize that AI models are still in early stages, and their outputs need to be shaped, checked, and verified by humans. The potential of AI is vast, but it is our responsibility as humans to ensure that its applications are reliable and accurate. As we continue on the path of this technological revolution, human involvement and expertise remain essential for verifying and validating the outputs of AI language models. Only through human verification can we harness the full potential of AI while mitigating risks and ensuring reliability.
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