Artificial Intelligence is now taking meetings and writing code

Microsoft has recently launched a new version of its software with an artificial intelligence (AI) assistant called Copilot. This AI assistant can perform a variety of tasks for you, such as summarizing verbal conversations in Teams online meetings, presenting arguments for or against a particular point based on verbal discussions, answering a portion of your emails, and even writing computer code.

This technology is developing rapidly and seems to be bringing us closer to a future where AI can make our lives easier and take away the boring and repetitive things we have to do as humans. However, we must use such large language models (LLMs) with caution. Despite their intuitive nature, they still require skill to use effectively, reliably and safely.

LLMs, which are a type of “deep learning” neural network, are designed to understand the user’s intent by analyzing the probability of different responses based on the prompt provided. When a person inputs a prompt, the LLM examines the text and determines the most likely response.

ChatGPT, an LLM example, can provide answers to prompts on a wide range of subjects. However, despite its seemingly knowledgeable responses, ChatGPT does not actually possess knowledge. Its responses are simply the most probable outcomes based on the given prompt.

When people provide detailed descriptions of the tasks they want to accomplish to ChatGPT, Copilot, and other LLMs, these models can excel at providing high-quality responses. This could include generating text, images, or computer code. However, we must be careful not to over-rely on AI and push the boundaries of what technology can do. We should use these systems to assist us in our work, not as a substitute for our own efforts.

Despite their seemingly intelligent responses, we cannot blindly trust LLMs to be accurate or reliable. We must carefully evaluate and verify their outputs, ensuring that our initial prompts are reflected in the answers provided. To effectively verify and validate LLM outputs, we need to have a strong understanding of the subject matter. Without expertise, we cannot provide the necessary quality assurance.

This becomes particularly critical in situations where we are using LLMs to bridge gaps in our own knowledge. Here, our lack of knowledge may lead us to a situation where we are simply unable to determine whether the output is correct or not. This situation can arise in the generation of text and coding.
Using AI to attend meetings and summarize the discussion presents obvious risks around reliability. While the record of the meeting is based on a transcript, the meeting notes are still generated in the same fashion as other text from LLMs. They are still based on language patterns and probabilities of what was said, so they require verification before they can be acted upon.

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They also suffer from interpretation problems due to homophones, words that are pronounced the same but have different meanings. People are good at understanding what is meant in such circumstances due to the context of the conversation. But AI is not good at deducing context nor does it understand nuance. So, expecting it to formulate arguments based upon a potentially erroneous transcript poses further problems still.

Verification is even harder if we are using AI to generate computer code. Testing computer code with test data is the only reliable method for validating its functionality. While this demonstrates that the code operates as intended, it doesn’t guarantee that its behavior aligns with real-world expectations.

Suppose we use generative AI to create code for a sentiment analysis tool. The goal is to analyze product reviews and categorize sentiments as positive, neutral, or negative. We can test the functionality of the system and validate the code functions correctly – that it is sound from a technical programming point of view. However, imagine that we deploy such software in the real world and it starts to classify sarcastic product reviews as positive. The sentiment analysis system lacks the contextual knowledge necessary to understand that sarcasm is not used as positive feedback, and quite the opposite.

Verifying that a code’s output matches the desired outcomes in nuanced situations such as this requires expertise. Non-programmers will have no knowledge of software engineering principles that are used to ensure the code is correct, such as planning, methodology, testing, and documentation. Programming is a complex discipline, and software engineering emerged as a field to manage software quality.

LLMs such as ChatGPT and Copilot are powerful tools that we can all benefit from. But we must be careful not to blindly trust the outputs given to us. We are right at the start of a great technological revolution, and we must be responsible in our use of these systems.

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