GPT Has Become An Indispensable Cognitive Filter
More than ever, recognizing the signal from the noise can be a game-changer.
GPT Summary: Claude Shannon revolutionized digital communication with his 1948 paper on information theory, which introduced concepts like channel capacity and signal-to-noise ratio. These insights have shaped modern telecommunications and digital media, but as we consume more information than ever before, our brains may become overwhelmed. The principles of information theory can help us make sense of this deluge, and AI platforms like GPT can help us process and filter relevant information.
Claude Shannon, often regarded as the father of information theory, revolutionized the field of digital communication with his groundbreaking 1948 paper, “A Mathematical Theory of Communication.” His work focused on the transmission of information through communication channels, introducing key concepts such as signal-to-noise ratio, channel capacity, and error-free transmission. Shannon’s insights have played a pivotal role in shaping modern telecommunications, computer science, and digital media, and continue to influence how we understand and navigate the increasingly complex world of information today.
Simply put, Shannon’s work demonstrated that a communication channel has a limit to the amount of information that can be transmitted error-free, known as the channel capacity or Shannon capacity. The channel capacity is determined by bandwidth and signal-to-noise (SNR), and increasing either of these can improve the capacity.
The concept of a Shannon capacity for the human brain is an intriguing proposition. While the human brain is an incredibly complex and adaptable organ, it still has limits in terms of cognitive load, attention, and processing capacity. As the volume of information we consume daily continues to grow, we may reach a point where the human brain’s capacity to process and filter signal from noise becomes overwhelmed.
The concept of cognitive load plays a crucial role in understanding these limitations. Our working memory, responsible for temporarily storing and manipulating information, has a limited capacity. When the cognitive load exceeds this capacity, our ability to process and retain new information suffers, leading to decreased comprehension and learning.
Signal-to-noise ratio can be applied to the context of human cognition by considering signal as meaningful, relevant information and noise as irrelevant or distracting information. When the amount of noise increases, it becomes more challenging for our cognitive system to filter out the noise and focus on the signal. This results in increased cognitive load and diminished effectiveness in processing and retaining information.
But there’s another critical perspective that is both part of the problem and the solution: technology.
Claude Shannon’s information theory is still highly relevant in today’s technological society. The theory provides a mathematical framework for understanding how information can be measured, stored, and transmitted across different mediums. In today’s world, where we are inundated with an incredible amount of data and information, the principles of information theory can help us make sense of it all.
One of the major challenges of the digital age is the sheer volume of data that we generate and consume. In 2021, people created 2.5 quintillion bytes of data every day. This deluge of information can be overwhelming and make it difficult to distinguish signal from noise. However, technology, particularly AI, can be—even must be—part of the solution.
Platforms like GPT, which is based on deep learning algorithms, can help us process and understand information in ways that were previously impossible. These systems can sift through vast amounts of data and identify patterns, trends, and insights that might otherwise be missed. They can also help us filter out noise and focus on the signals that are most relevant to us.
For example, in business, GPT can be used to analyze customer feedback, social media posts, and market data to identify emerging trends and customer preferences. This can help businesses make more informed decisions about product development, marketing strategies, and customer service.
In medicine, GPT can be trained on electronic medical records, clinical trial data, and scientific literature to identify potential drug interactions, diagnose medical conditions, and predict patient outcomes. This can help medical professionals make more accurate diagnoses, develop personalized treatment plans, and improve patient outcomes.
Moreover, GPT can also be used to generate summaries of large documents or data sets, which can help decision-makers quickly grasp the key takeaways without having to sift through large volumes of text. These summaries can be customized to focus on specific areas of interest or key performance indicators, providing a clear and concise summary of important information—directly providing the signal.
Recently, science and academia have been recognizing the powerful and valuable role LLMs can have in driving discovery by assisting in the “heavy lifting” of finding critical and enlightening connections in vast bodies of data.
This new generation of search engines, powered by machine learning and large language models, is moving beyond keyword searches to pull connections from the tangled web of the scientific literature.
However, it is also important to recognize that technology is part of the problem. The ease of generating, sharing, and storing information has contributed to the overwhelming amount of data that we face today. In addition, the algorithms that underpin many AI systems can be biased and perpetuate existing inequalities and prejudices.
Claude Shannon’s information theory provides a fascinating framework for understanding how we process and communicate information in today’s technological society. While technology has contributed to the problem of information overload, it can also be part of the solution. Platforms like GPT and AI systems can help us make sense of the vast amounts of data that we generate and consume and identify the unique signals that are most relevant and important to us.
Thanks to Brian Roemmele for his introduction to Claude Shannon’s information theory.
This story was updated on August 9, 2023 with the new paper from the journal Nature.