Can we truly rely on AI to solve our problems? The answer is a resounding yes and no. While AI has made tremendous strides in recent years, it also has its limitations, which can be frustratingly apparent in everyday life. One of the most significant drawbacks of relying solely on AI is that it often lacks human intuition and creativity, which are essential components of problem-solving.

Nowadays, massive language models like ChatGPT and Claude have become a regular phenomenon globally. Individuals are understandably concerned that language models based on large language models (LLMs) struggle with fundamental tasks: accurately counting repeating letters within phrases. For instance, attempts to count “r” instances in “strawberry” or “m” instances in “mammal” and “p” instances in “hippopotamus” consistently fall short.

Trained on vast amounts of textual data to comprehend and create natural-sounding language. With their expertise, AI models shine in tasks such as responding to inquiries, rendering linguistic conversions, condensing complex content into concise summaries, and generating creative writing that anticipates and constructs logical answers grounded in the input received. Large Language Models are engineered to identify patterns within written text, enabling them to tackle a diverse range of linguistic tasks with exceptional precision.

Despite the impressive capabilities of language models, their inability to accurately count the instances of “r” in the phrase “regardless of their prowess” underscores their limitations in replicating human thought processes, particularly when it comes to pondering complex ideas. Artificial intelligence systems do not process information in the same way that humans do, failing to utilize our cognitive abilities and creative thinking.

Nearly all current excessively efficient large language models (LLMs) are built upon. The deep learning structure does not immediately process text data as its input. Developing algorithms that employ a process known as tokenization, where written content is converted into numerical representations, or tokens. Some tokens are likely to be full phrases (e.g., “monkey”), whereas others might comprise parts of a phrase (e.g., “mon” and “key”). Each token constitutes a unique code comprehensible to the mannequin. By decomposing sentences into individual components, the model is able to more accurately forecast the forthcoming word in a sequence. 

While LLMs may not literally memorize phrases, they excel at recognizing patterns and relationships between tokens, allowing them to effectively predict what follows. Without contextual information or linguistic cues, the mannequin might struggle to comprehend that the phrase “hippopotamus” consists of a sequence of individual letters – precisely: h-i-p-p-o-t-a-m-u-s.

While a mannequin structure capable of directly examining individual person letters without tokenization wouldn’t be plagued by this issue, today’s transformer architectures unfortunately lack the necessary computational feasibility.

Large language models (LLMs) generate output textual content by anticipating subsequent phrases based on the preceding input and output tokens. While this approach yields human-like textual output with contextual awareness, its limitations are evident in straightforward tasks such as letter counting. Without considering linguistic context, language models like LLMs predict the occurrence of “r”s in a sentence based solely on statistical patterns and syntactical analysis.

Right here’s a workaround

While LLMs are not designed to assume or logically reason, they excel in processing and interpreting structured text data.

Structured textual content excels in a specific example: computer code from various programming languages. If you were to query ChatGPT on using Python to count the various “r”s in “strawberry”, it would likely provide the correct response. When LLMs are tasked with performing calculations or logical tasks, the underlying software framework is structured to enable them to process queries using a programming language.

Conclusion

A straightforward letter-counting exercise reveals a fundamental constraint of large language models (LLMs) such as ChatGPT and Claude. While AI models exhibit impressive capabilities in generating human-like text, coding, and responding to queries, they still inherently “simulate” human thought processes. The experiment exposes the superficiality of fashion trends, merely serving as a testing ground for predictive models, rather than any genuine display of “intelligence” capable of comprehension or logical thinking. While having prior knowledge of effective prompts can mitigate the problem to some degree. As artificial intelligence permeates every aspect of our lives, acknowledging its inherent limitations is crucial for responsible deployment and realistic anticipation of these technologies.