Microsoft has recently unveiled an updated version of its software lineup, introducing a cutting-edge artificial intelligence (AI) assistant designed to perform a multitude of tasks on your behalf. Learn more about this exciting development here.
The Copilot feature is equipped to condense spoken conversations during Teams online meetings, provide arguments supporting or opposing specific points based on verbal exchanges, and handle a portion of your email correspondence. Impressively, it is also capable of generating computer code.
This rapidly advancing technology seems to bring us one step closer to a future where AI simplifies our lives, eliminating mundane and repetitive tasks that we, as humans, often face.
While these remarkable advancements are undeniably beneficial, it is crucial to exercise caution in the utilization of large language models (LLMs). Despite their user-friendly interface, mastering their effective, reliable, and safe use still demands a certain level of skill.
Large language models
Large Language Models (LLMs), a subset of “deep learning” neural networks, are crafted to comprehend user intent by assessing the likelihood of various responses derived from the given prompt. When a user inputs a prompt, the LLM scrutinizes the text to identify the most probable response.
ChatGPT, a notable instance of a Large Language Model (LLM), can offer responses to prompts covering a broad spectrum of topics. Nevertheless, despite its appearance of knowledge, it’s crucial to note that ChatGPT doesn’t possess actual knowledge, as highlighted in this article. Instead, its responses are merely the most probable outcomes based on the provided prompt.
By furnishing ChatGPT, Copilot, and other Large Language Models (LLMs) with elaborate task descriptions, users enable these models to deliver exceptional responses of high quality. This proficiency extends to generating various forms of content, such as text, images, or computer code.
However, as humans, we frequently test the limits of technology, surpassing its original design intentions. Consequently, we find ourselves relying on these systems to perform tasks that we should have undertaken personally.
Why over-reliance on AI could be a problem
Despite their responses appearing intelligent, it’s imperative not to unquestioningly rely on the accuracy or reliability of Large Language Models (LLMs). Rigorous evaluation and verification of their outputs are essential to ensure that the answers align with the initial prompts provided.
To adequately authenticate and confirm LLM outputs, a comprehensive understanding of the subject matter is crucial. Without expertise, it becomes challenging to offer the required quality assurance.
This becomes especially crucial when employing LLMs to fill gaps in our knowledge. In such instances, our limited understanding may result in a scenario where we are unable to definitively ascertain the accuracy of the output. This challenge can manifest in both text generation and coding tasks.
Employing AI for meeting attendance and discussion summarization introduces evident concerns regarding reliability.
Although meeting records rely on transcripts, the meeting notes are created using the same methodology as other text generated by LLMs. They hinge on language patterns and probabilities of the spoken content, necessitating verification before any action can be taken based on them.
They also face challenges in interpretation, particularly with homophones—words pronounced similarly but with distinct meanings. Humans excel at grasping intended meanings in such instances, primarily due to the context of the conversation.
However, AI lacks proficiency in deducing context and grasping nuance. Anticipating its ability to construct arguments based on a possibly flawed transcript introduces additional challenges.
Verification becomes even more challenging when employing AI for code generation. The sole dependable method for validating code functionality is testing it with relevant data. While this confirms that the code functions as designed, it doesn’t ensure its behavior aligns with real-world expectations.
Let’s say we leverage generative AI to generate code for a sentiment analysis tool. The objective is to examine product reviews and classify sentiments into positive, neutral, or negative categories. Testing the system’s functionality allows us to validate that the code operates correctly, ensuring its technical programming soundness.
Yet, consider a scenario where we implement such software in practical settings, and it begins categorizing sarcastic product reviews as positive. The sentiment analysis system lacks the contextual comprehension needed to discern that sarcasm is not employed as positive feedback; in fact, it often signifies the opposite.
Ensuring that a code’s results align with the intended outcomes in nuanced scenarios like this demands a level of expertise.
Individuals without programming backgrounds may lack awareness of software engineering principles crucial for ensuring code correctness, including planning, methodology, testing, and documentation. Programming, being a complex discipline, gave rise to software engineering as a field to oversee and enhance software quality.
There exists a significant risk, as evidenced by my research, that individuals lacking expertise may inadvertently overlook or bypass crucial steps in the software design process, resulting in code of uncertain quality.
Validation and verification
Large Language Models (LLMs) like ChatGPT and Copilot are potent resources that offer widespread advantages. However, it’s crucial not to unquestioningly rely on the outputs provided and to exercise caution in their interpretation.
We are at the inception of a significant revolution driven by this technology. AI holds limitless potential, but it requires shaping, scrutiny, and verification. Currently, it is only humans who possess the capability to perform these essential tasks.
This content has been reissued from The Conversation under the terms of a Creative Commons license.
