On Forgetting
by Mechas
A recent essay by Melissa Kirsch of the New York Times highlights what seems to be a prevalent concern: forgetfulness. The constant exposure to an ever-growing barrage of media has its toll, especially as we grow old(er). Even though remembering and forgetting are essential to processing information and learning as we meander through life, an excess of data can result in overflow and storage problems.
Fig.1. A Day in Data. Source. Frontispiece: Forgetting. Source
The world today runs on data – the new global currency. According to some estimates, we generate more than 0.4 zettabytes (ZB; 1 ZB = 1021 bytes) of data daily. By the end of 2024 we will have an estimated 147 ZB of data, a substantial increase from the 2 ZB in 2010. Much of this information is generated by users around the world in the form of emails, videos, computer work, and social media. Scientific data in physics and the biological sciences also represents an important chunk of this equation. The flow of digital information has become a distinctive feature of humanity today, a concept that resonates nicely with the flow of information that underlies living systems as organisms interact with each other and with their environments.
Fig. 2. Increased number of scholarly journals from their birth (1665). The predicted number of journals was calculated based on the assumption that the number of journals doubles about every 20 years. Source
How to process all this information, let alone retain it? One important resource is, of course, the scientific literature. Here again, the number of journals has risen dramatically in recent decades. Perusing through tables of contents is no longer an easy task when journals now number in the thousands. Is there an efficient way to access information distributed in so many journals and sources? A quick search led me to identify tools that could be helpful, in addition to my regular go-to search engine in PubMed. You may have your own favorite system of identifying and retrieving information for your work or personal interests. But again, how can we make sense of so much information with our limited time? To address this in some measure, tools now incorporate AI to systematize and facilitate information processing and aid in tasks like summarizing meetings and articles. You have probably seen the AI assistants that now pop up during virtual meetings and those that provide summaries and highlights of journal articles. Even more powerful is the idea of embracing AI tools to assist in the peer review process, which is time-consuming and increasingly difficult due to the growing volume of research and large and complex datasets and analyses generated by interdisciplinary teams. The significance of AI in the sciences is underscored by this year's Nobel Prizes in Physics and Chemistry, awarded for groundbreaking methods in machine learning and for advancements in predicting protein structures, respectively.
The very existence of such an assortment of methods for searching, retrieving, and processing scientific literature and data is symptomatic of the complexity underlying the growing ecosystem of scientific information. It is practically impossible to recall the specifics of the numerous texts we read, from research articles to news outlets and literature. Yet do not despair, for it has been argued that despite forgetting the details, engaging in activities that stimulate the mind will strengthen our cognitive processes.
Forgetting is an inevitable side effect of learning, especially in these data-driven times. And remember (or forget) that the choices we make on how to navigate the constant influx of information shape our understanding and challenge our ability to thrive within the very information ecosystem we have helped create.
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