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01 / 05
Digital Representation Drives Progress

Blog Post | Science & Technology

Digital Representation Drives Progress

This technological revolution is pushing humanity forward on countless interconnected fronts.

Summary: Digital representation has revolutionized the economy and daily life by converting analog information into digital formats, enabling vast efficiencies and new business models. This has made products like music and services more accessible and has advanced production and waste management in contexts such as agriculture and recycling. As digital representation evolves, it plays a crucial role in addressing global challenges, from poverty in developing countries to environmental change.


Back in the “good old days,” if someone wanted to listen to Michael Jackson’s Thriller, they had to visit their local music store and see if it had the album in cassettes. Alternatively, they could pick up a vinyl record to play on their record player. Nowadays, cassettes and vinyl records have become relics of the past due to the rise in one gigantic innovation: digital representation.

Digital representation starts with the process of digitization, which involves converting analog information or data into digital signals. While analog signals are represented in continuous waves, digital signals are discrete units of information that consist of binary values, or 0s and 1s. Because computers perform using binary logic, the conversion from analog to binary enables computers and mobile devices to process, store, and transfer data digitally.

Digitization is just one of the key components of digital representation, which is the process of converting real-world objects, processes, and information into a digital format. Some examples of digital representation include medical images, cryptocurrency, YouTube videos, and many, many more. Businesses have moved toward using digital representation applications such as Excel to create data visualizations, Microsoft Azure for cloud storage, and SAP for automating business procedures.

One of the reasons for the extensive use of digital representation is that businesses that use digital representation have near-zero marginal costs per new unit produced or user added. Spotify and Amazon are prime examples of corporations taking advantage of digital representation. Spotify’s streaming service uses a “freemium” model, which has a limited, free service to casual listeners and a premium option for music connoisseurs. This model has expanded Spotify’s user base so that it can spread its fixed costs across its 615 million users, minimizing the costs of adding more users. Spotify also uses a data center that contains millions of digitized songs, all of which maintain their audio quality indefinitely and are distributed at zero additional cost. On the other hand, Amazon has its Prime subscription. Unlike a traditional business model, which contains fixed costs for each layered process, Amazon runs its distribution digitally and enables its “massive bundling” strategy through the Prime subscription, which combines services including movie streaming, music streaming, groceries, and more. Both corporations have revolutionized entertainment and shopping for millions of people worldwide.

Besides innovative business models, digital representation’s greatest benefit has come in advancing the efficiency of production. One such example is the creation of digitalized mobile phones, which has helped the populations of developing countries overcome bottlenecks in infrastructure, most prominently in Sub-Saharan Africa. According to a Pew Research Center study in 2015, 97 percent of those surveyed from countries such as Senegal, Kenya, and Nigeria did not have a working landline telephone due to failed landline development across the continent. However, this lack of service has been alleviated by the massive increase in mobile phone usage. In 2022, the number of mobile cellular subscriptions in Sub-Saharan Africa per 100 people was nearly 52 times larger than it was in 2000, growing from less than 2 to around 89.

Mobile cellular subscriptions, per 100 people

Not only has mobile phone usage skyrocketed, but the mobile money movement has taken over Africa. Fintech—which is composed of software, mobile applications, and other technologies that automate and digitalize financing—facilitated this process. In 2008, fintech was first introduced in Africa with M-Pesa in Kenya. It enables users to withdraw, transfer, and deposit funds into their accounts all through a mobile device. A study done in 2016 found that since its inception, M-Pesa has helped increase daily per capita consumption levels of 194,000 households, and that number has increased since then. With M-Pesa’s rapid success, the number of fintechs have increased across Africa and includes Fawry in Egypt, Yoco in South Africa, and Interswitch in Nigeria. Mobile money helps millions of unbanked people in Africa gain access to financial services. Those include small business owners, who make up 90 percent of businesses in Africa. Certainly, gaining access to financial tools such as M-Pesa and other mobile money services has expanded the possibilities for Africans to contribute to the continent’s growth.

Digital representation has also generated greater efficiencies in production. In agriculture, for example, the advancements of farming tools combined with the adoption of digital technologies has skyrocketed production. According to US Department of Agriculture data, between 1996 and 2017, the US average corn yield increased 42 percent, from 130 to 185 bushels an acre. It is not just corn either. Apples, wheat, and other crops have also seen significant growth in production worldwide since the mid–20th century. The increased use of technologies such as yield monitors and digital mapping has helped farmers manage their crop yields in greater capacity by highlighting what areas are prone to soil erosion and pests, promoting uniform crop growth and reducing the risk of lost crop production.

With increased production comes a significant amount of waste. In response, digital representation has revolutionized recycling and waste management. This development has been especially prominent in Europe. Some of the technologies developed improve logistics, such as routing systems and centralized Enterprise Resource Planning databases to manage and track waste data. Other innovations have helped sorting out waste for recycling, including robotic sorters and artificial intelligence image processing to recognize and pick out different types of waste using data algorithms. This digital revolution has led to improved waste management and an increase in recycling across Europe.

The figure shows that municipal (including household) waste recycling rates, measured as the percentage of total waste generated that is recycled, increased in Europe between 1995 and 2015.
Source: https://ourworldindata.org/grapher/municipal-waste-recycling-rate?tab=chart&country=~OECD+-+Europe

Beyond shaping businesses, digital representation creates a technological revolution that contributes to alleviating poverty, reducing waste, and improving lives across the globe. As humanity progresses, the power of digital representation will only expand as people continue to find ways to achieve what was not possible before.

UCL | Communications

UK Neuralink Patient Uses Thought to Control Computer

“A patient with motor neurone disease was able to control a computer just by using his thoughts following the UK’s first Neuralink implant surgery in a study led by UCL and UCLH clinical researchers.

The surgery is part of the GB-PRIME study evaluating the safety and functionality of Neuralink’s robotically implanted brain-computer interface (BCI), which aims to improve independence for people who are paralysed. 

The surgery, which took place at UCLH’s National Hospital for Neurology and Neurosurgery (NHNN) in October 2025, went as planned, and on the day following the procedure, the patient was able to begin using their BCI implant to move a computer cursor with their thoughts and to return home from the hospital.”

From UCL.

New York Times | Computing

Google’s Quantum Computer Makes a Big Technical Leap

“On Wednesday, Dr. Devoret and his colleagues at a Google lab near Santa Barbara, Calif., said their quantum computer had successfully run a new algorithm capable of accelerating advances in drug discovery, the design of new building materials and other fields.

Leveraging the counterintuitive powers of quantum mechanics, Google’s machine ran this algorithm 13,000 times as fast as a top supercomputer executing similar code in the realm of classical physics, according to a paper written by the Google researchers in the scientific journal Nature…

In another paper published on Wednesday on the research site arXiv, the company showed that its algorithm could help improve what is called nuclear magnetic resonance, or N.M.R., which is a technique used to understand the structure of tiny molecules and how they interact with one another.

N.M.R. is a vital part of effort to develop new medicines for fighting disease and new materials for building everything from cars to buildings. It can help understand Alzheimer’s disease or drive the creation of entirely new metals, said Ashok Ajoy, an assistant professor of chemistry at Berkeley who specializes in N.M.R. and worked with Google’s researchers on the new paper.”

From New York Times.

Nature | Science & Technology

OpenAI’s GPT-5 Hallucinates Less than Previous Models Do

“In one literature-review benchmark known as ScholarQA-CS, GPT-5 ‘performs well’ when it is allowed to access the web, says Akari Asai, an AI researcher at the Allen Institute for Artificial Intelligence, based in Seattle, Washington, who ran the tests for Nature. In producing answers to open-ended computer-science questions, for example, the model performed marginally better than human experts did, with a correctness score of 55% (based on measures such as how well its statements are supported by citations) compared with 54% for scientists, but just behind a version of institute’s own LLM-based system for literature review, OpenScholar, which achieved 57%.

However, GPT-5 suffered when the model was unable to get online, says Asai. The ability to cross-check with academic databases is a key feature of most AI-powered systems designed to help with literature reviews. Without Internet access, GPT-5 fabricated or muddled half the number of citations that one of its predecessors, GPT-4o, did. But it still got them wrong 39% of the time, she says.

On the LongFact benchmark, which tests accuracy in long-form responses to prompts, OpenAI reported that GPT-5 hallucinated 0.8% of claims in responses about people or places when it was allowed to browse the web, compared with 5.1% for OpenAI’s reasoning model o3. Performance dropped when browsing was not permitted, with GPT-5’s error rate climbing to 1.4% compared with 7.9% for o3. Both models showed worse performance than did the non-reasoning model GPT-4o, which had an error rate of 1.1% when offline.”

From Nature.

Wired | Science & Technology

OpenAI Just Released Its First Open-Weight Models Since GPT-2

“OpenAI just dropped its first open-weight models in over five years. The two language models, gpt-oss-120b and gpt-oss-20b, can run locally on consumer devices and be fine-tuned for specific purposes. For OpenAI, they represent a shift away from its recent strategy of focusing on proprietary releases, as the company moves towards a wider, and more open, group of AI models that are available for users…

What sets apart an open-weight model is the fact that its ‘weights’ are publicly available, meaning that anyone can peek at the internal parameters to get an idea of how it processes information. Rather than undercutting OpenAI’s proprietary models with a free option, cofounder Greg Brockman sees this release as ‘complementary’ to the company’s paid services, like the application programming interface currently used by many developers. ‘Open-weight models have a very different set of strengths,’ said Brockman in a briefing with reporters. Unlike ChatGPT, you can run a gpt-oss model without a connection to the internet and behind a firewall.”

From Wired.