‘We were just trying to get it to work’: The failure that started the internet


Article by Scott Nover: “At the height of the Cold War, Charley Kline and Bill Duvall were two bright-eyed engineers on the front lines of one of technology’s most ambitious experiments. Kline, a 21-year-old graduate student at the University of California, Los Angeles (UCLA), and Duvall, a 29-year-old systems programmer at Stanford Research Institute (SRI), were working on a system called Arpanet, short for the Advanced Research Projects Agency Network. Funded by the US Department of Defense, the project aimed to create a network that could directly share data without relying on telephone lines. Instead, this system used a method of data delivery called “packet switching” that would later form the basis for the modern internet.

It was the first test of a technology that would change almost every facet of human life. But before it could work, you had to log in.

Kline sat at his keyboard between the lime-green walls of UCLA’s Boelter Hall Room 3420, prepared to connect with Duvall, who was working a computer halfway across the state of California. But Kline didn’t even make it all the way through the word “L-O-G-I-N” before Duvall told him over the phone that his system crashed. Thanks to that error, the first “message” that Kline sent Duvall on that autumn day in 1969 was simply the letters “L-O”…(More)”.

Artificial Intelligence, Scientific Discovery, and Product Innovation


Paper by Aidan Toner-Rodgers: “… studies the impact of artificial intelligence on innovation, exploiting the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of “idea-generation” tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives. Together, these findings demonstrate the potential of AI-augmented research and highlight the complementarity between algorithms and expertise in the innovative process. Survey evidence reveals that these gains come at a cost, however, as 82% of scientists report reduced satisfaction with their work due to decreased creativity and skill underutilization…(More)”.

Digital Media Metaphors


Book edited by Johan Farkas and Marcus Maloney: “Bringing together leading scholars from media studies and digital sociology, this edited volume provides a comprehensive introduction to digital media metaphors, unpacking their power and limitations.

Digital technologies have reshaped our way of life. To grasp their dynamics and implications, people often rely on metaphors to provide a shared frame of reference. Scholars, journalists, tech companies, and policymakers alike speak of digital clouds, bubbles, frontiers, platforms, trolls, and rabbit holes. Some of these metaphors distort the workings of the digital realm and neglect key consequences. This collection, structured in three parts, explores metaphors across digital infrastructures, content, and users. Within these parts, each chapter examines a specific metaphor that has become near-ubiquitous in public debate. Doing so, the book engages not only with the technological, but also the social, political, and environmental implications of digital technologies and relations.

This unique collection will interest students and scholars of digital media and the broader fields of media and communication studies, sociology, and science and technology studies…(More)”.

Federated Data Infrastructures for Scientific Use


Policy paper by the German Council for Scientific Information Infrastructures: “…provides an overview and a comparative in-depth analysis of the emerging research (and research related) data infrastructures NFDI, EOSC, Gaia-X and the European Data Spaces. In addition, the Council makes recommendations for their future development and coordination. The RfII notes that access to genuine high-quality research data and related core services is a matter of basic public supply and strongly advises to achieve coherence between the various initiatives and approaches…(More)”.

Effective Data Stewardship in Higher Education: Skills, Competences, and the Emerging Role of Open Data Stewards


Paper by Panos Fitsilis et al: “The significance of open data in higher education stems from the changing tendencies towards open science, and open research in higher education encourages new ways of making scientific inquiry more transparent, collaborative and accessible. This study focuses on the critical role of open data stewards in this transition, essential for managing and disseminating research data effectively in universities, while it also highlights the increasing demand for structured training and professional policies for data stewards in academic settings. Building upon this context, the paper investigates the essential skills and competences required for effective data stewardship in higher education institutions by elaborating on a critical literature review, coupled with practical engagement in open data stewardship at universities, provided insights into the roles and responsibilities of data stewards. In response to these identified needs, the paper proposes a structured training framework and comprehensive curriculum for data stewardship, a direct response to the gaps identified in the literature. It addresses five key competence categories for open data stewards, aligning them with current trends and essential skills and knowledge in the field. By advocating for a structured approach to data stewardship education, this work sets the foundation for improved data management in universities and serves as a critical step towards professionalizing the role of data stewards in higher education. The emphasis on the role of open data stewards is expected to advance data accessibility and sharing practices, fostering increased transparency, collaboration, and innovation in academic research. This approach contributes to the evolution of universities into open ecosystems, where there is free flow of data for global education and research advancement…(More)”.

Commission launches public consultation on the rules for researchers to access online platform data under the Digital Services Act


Press Release: “Today, the Commission launched a public consultation on the draft delegated act on access to online platform data for vetted researchers under the Digital Services Act (DSA).

text Digital Services Act inside a white triangle against a blue background

With the Digital Services Act, researchers will for the first time have access to data to study systemic risks and to assess online platforms’ risk mitigation measures in the EU. It will allow the research community to play a vital role in scrutinising and safeguarding the online environment.

The draft delegated act clarifies the procedures on how researchers can access Very Large Operating Platforms’ and Search Engines’ data. It also sets out rules on data formats and data documentation requirements. Lastly, it establishes the DSA data access portal, a one-stop-shop for researchers, data providers, and DSCs to exchange information on data access requests. The consultation follows a first call for evidence.

The consultation will run until 26 November 2024. After gathering public feedback, the Commission plans to adopt the rules in the first quarter of 2025…(More)”.

Science and technology’s contribution to the UK economy


UK House of Lords Primer: “It is difficult to accurately pinpoint the economic contribution of science and technology to the UK economy. This is because of the way sectors are divided up and reported in financial statistics. 

 For example, in September 2024 the Office for National Statistics (ONS) reported the following gross value added (GVA) figures by industry/sector for 2023:

  • £71bn for IT and other information service activities 
  • £20.6bn for scientific research and development 

This would amount to £91.6bn, forming approximately 3.9% of the total UK GVA of £2,368.7bn for 2023. However, a number of other sectors could also be included in these figures, for example: 

  • the manufacture of computer, certain machinery and electrical components (valued at £38bn in 2023) 
  • telecommunications (valued at £34.5bn) 

If these two sectors were included too, GVA across all four sectors would total £164.1bn, approximately 6.9% of the UK’s 2023 GVA. However, this would likely still exclude relevant contributions that happen to fall within the definitions of different industries. For example, the manufacture of spacecraft and related machinery falls within the same sector as the manufacture of aircraft in the ONS’s data (this sector was valued at £10.8bn for 2023).  

Alternatively, others have made estimates of the economic contribution of more specific sectors connected to science and technology. For example: 

  • Oxford Economics, an economic advisory firm, has estimated that, in 2023, the life sciences sector contributed over £13bn to the UK economy and employed one in every 121 employed people 
  • the government has estimated the value of the digital sector (comprising information technology and digital content and media) at £158.3bn for 2022
  • a 2023 government report estimated the value of the UK’s artificial intelligence (AI) sector at around £3.7bn (in terms of GVA) and that the sector employed around 50,040 people
  • the Energy and Climate Intelligence Unit, a non-profit organisation, reported estimates that the GVA of the UK’s net zero economy (encompassing sectors such as renewables, carbon capture, green and certain manufacturing) was £74bn in 2022/23 and that it supported approximately 765,700 full-time equivalent (FTE) jobs…(More)”.

Veridical Data Science


Book by Bin Yu and Rebecca L. Barter: “Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs.
Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science…(More)”.

Statistical Significance—and Why It Matters for Parenting


Blog by Emily Oster: “…When we say an effect is “statistically significant at the 5% level,” what this means is that there is less than a 5% chance that we’d see an effect of this size if the true effect were zero. (The “5% level” is a common cutoff, but things can be significant at the 1% or 10% level also.) 

The natural follow-up question is: Why would any effect we see occur by chance? The answer lies in the fact that data is “noisy”: it comes with error. To see this a bit more, we can think about what would happen if we studied a setting where we know our true effect is zero. 

My fake study 

Imagine the following (fake) study. Participants are randomly assigned to eat a package of either blue or green M&Ms, and then they flip a (fair) coin and you see if it is heads. Your analysis will compare the number of heads that people flip after eating blue versus green M&Ms and report whether this is “statistically significant at the 5% level.”…(More)”.

External Researcher Access to Closed Foundation Models


Report by Esme Harrington and Dr. Mathias Vermeulen: “…addresses a pressing issue: independent researchers need better conditions for accessing and studying the AI models that big companies have developed. Foundation models — the core technology behind many AI applications — are controlled mainly by a few major players who decide who can study or use them.

What’s the problem with access?

  • Limited access: Companies like OpenAI, Google and others are the gatekeepers. They often restrict access to researchers whose work aligns with their priorities, which means independent, public-interest research can be left out in the cold.
  • High-end costs: Even when access is granted, it often comes with a hefty price tag that smaller or less-funded teams can’t afford.
  • Lack of transparency: These companies don’t always share how their models are updated or moderated, making it nearly impossible for researchers to replicate studies or fully understand the technology.
  • Legal risks: When researchers try to scrutinize these models, they sometimes face legal threats if their work uncovers flaws or vulnerabilities in the AI systems.

The research suggests that companies need to offer more affordable and transparent access to improve AI research. Additionally, governments should provide legal protections for researchers, especially when they are acting in the public interest by investigating potential risks…(More)”.