About: “This tool helps you estimate and fact-check the maximum number of people standing in a given area…(More)”

OECD Report: “Governments worldwide are increasingly adopting behavioural science methodologies to address “sludge” – the unjustified frictions impeding people’ access to government services and exacerbating psychological burdens. Sludge audits, grounded in behavioural science, provide a structured approach for identifying, quantifying, and preventing sludge in public services and government processes. This document delineates Good Practice Principles, derived from ten case studies conducted during the International Sludge Academy, aimed at promoting the integration of sludge audit methodologies into public governance and service design. By enhancing government efficiency and bolstering public trust in government, these principles contribute to the broader agenda on administrative simplification, digital services, and public sector innovation…(More)”.
Article by Julia Angwin: “…If you use a navigation app, you probably have felt helpless anger when your stupid phone endangers your life, and the lives of all the drivers around you, to potentially shave a minute or two from your drive time. Or maybe it’s stuck you on an ugly freeway when a glorious, ocean-hugging alternative lies a few miles away. Or maybe it’s trapped you on a route with no four-way stops, ignoring a less stressful solution that doesn’t leave you worried about a car barreling out of nowhere.
For all the discussion of the many extraordinary ways algorithms have changed our society and our lives, one of the most impactful, and most infuriating, often escapes notice. Dominated by a couple of enormously powerful tech monopolists that have better things to worry about, our leading online mapping systems from Google and Apple are not nearly as good as they could be.
You may have heard the extreme stories, such as when navigation apps like Waze and Google Maps apparently steered drivers into lakes and onto impassable dirt roads, or when jurisdictions beg Waze to stop dumping traffic onto their residential streets. But the reality is these apps affect us, our roads and our communities every minute of the day. Primarily programmed to find the fastest route, they endanger and infuriate us on a remarkably regular basis….
The best hope for competition relies on the success of OpenStreetMap. Its data underpins most maps other than Google, including Amazon, Facebook and Apple, but it is so under-resourced that it only recently hired paid systems administrators to ensure its back-end machines kept running….In addition, we can promote competition by using the few available alternatives. To navigate cities with public transit, try apps such as Citymapper that offer bike, transit and walking directions. Or use the privacy-focused Organic Maps…(More)”.
Report by the Tony Blair Institute: “The new government will need to lean in to support the diffusion of AI-era tech across the economy by adopting a pro-innovation, pro-technology stance, as advocated by the Tony Blair Institute for Global Change in our paper Accelerating the Future: Industrial Strategy in the Era of AI.
AI-era tech can also transform public services, creating a smaller, lower-cost state that delivers better outcomes for citizens. New TBI analysis suggests:
The four public-sector use cases outlined above could create substantial fiscal savings for the new government worth £12 billion a year (0.4 per cent of GDP) by the end of this parliamentary term, £37 billion (1.3 per cent of GDP) by the end of the next, and more than £40 billion (1.5 per cent of GDP) by 2040…(More)”.
Paper by Xin Chan, et al: “We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce Persona Hub — a collection of 1 billion diverse personas automatically curated from web data. These 1 billion personas (~13% of the world’s total population), acting as distributed carriers of world knowledge, can tap into almost every perspective encapsulated within the LLM, thereby facilitating the creation of diverse synthetic data at scale for various scenarios. By showcasing Persona Hub’s use cases in synthesizing high-quality mathematical and logical reasoning problems, instructions (i.e., user prompts), knowledge-rich texts, game NPCs and tools (functions) at scale, we demonstrate persona-driven data synthesis is versatile, scalable, flexible, and easy to use, potentially driving a paradigm shift in synthetic data creation and applications in practice, which may have a profound impact on LLM research and development…(More)”.
Blog by Louis Rosenberg: “This begs the question — could large human groups deliberate in real-time with the efficiency of fish schools and quickly reach optimized decisions?
For years this goal seemed impossible. That’s because conversational deliberations have been shown to be most productive in small groups of 4 to 7 people and quickly degrade as groups grow larger. This is because the “airtime per person” gets progressively squeezed and the wait-time to respond to others steadily increases. By 12 to 15 people, the conversational dynamics change from thoughtful debate to a series of monologues that become increasingly disjointed. By 20 people, the dialog ceases to be a conversation at all. This problem seemed impenetrable until recent advances in Generative AI opened up new solutions.
The resulting technology is called Conversational Swarm Intelligence and it promises to allow groups of almost any size (200, 2000, or even 2 million people) to discuss complex problems in real-time and quickly converge on solutions with significantly amplified intelligence. The first step is to divide the population into small subgroups, each sized for thoughtful dialog. For example, a 1000-person group could be divided into 200 subgroups of 5, each routed into their own chat room or video conferencing session. Of course, this does not create a single unified conversation — it creates 200 parallel conversations…(More)”.
Paper by Murthy Dhiraj et al: “Methods to meaningfully integrate journalists into crisis informatics remain lacking. We explored the feasibility of generating a real-time, priority-driven map of infrastructure damage during a natural disaster by strategically selecting journalist networks to identify sources of image-based infrastructure-damage data. Using the REST Twitter API, 1,000,522 tweets were collected from September 13-18, 2018, during and after Hurricane Florence made landfall in the United States. Tweets were classified by source (e.g., news organizations or citizen journalists), and 11,638 images were extracted. We utilized Google’s AutoML Vision software to successfully develop a machine learning image classification model to interpret this sample of images. As a result, 80% of our labeled data was used for training, 10% for validation, and 10% for testing. The model achieved an average precision of 90.6%, an average recall of 77.2%, and an F1 score of .834. In the future, establishing strategic networks of journalists ahead of disasters will reduce the time needed to identify disaster-response targets, thereby focusing relief and recovery efforts in real-time. This approach ultimately aims to save lives and mitigate harm…(More)”.
European Union Report: “…considers how to approach citizen engagement for the EU missions. Engagement and social dialogue should aim to ensure that innovation is human-centred and that missions maintain wide public legitimacy. But citizen engagement is complex and significantly changes the traditional responsibilities of the research and innovation community and calls for new capabilities. This report provides insights to build these capabilities and explores effective ways to help citizens understand their role within the EU missions, showing how to engage them throughout the various stages of implementation. The report considers both the challenges and administrative burdens of citizen engagement and sets out how to overcome them, as well as demonstrated the wider opportunity of “double additionality” where citizen engagement methods serve to fundamentally transform an entire research and innovation portfolio…(More)”.
Article by Jeff Cox: “Measuring the strength of the sprawling U.S. economy is no easy task, so one firm is sending artificial intelligence in to do the job.
The Zeta Economic Index, launched Monday, uses generative AI to analyze what its developers call “trillions of behavioral signals,” largely focused on consumer activity, to score growth on both a broad level of health and a separate measure on stability.
At its core, the index will gauge online and offline activity across eight categories, aiming to give a comprehensive look that incorporates standard economic data points such as unemployment and retail sales combined with high-frequency information for the AI age.
“The algorithm is looking at traditional economic indicators that you would normally look at. But then inside of our proprietary algorithm, we’re ingesting the behavioral data and transaction data of 240 million Americans, which nobody else has,” said David Steinberg, co-founder, chairman and CEO of Zeta Global.
“So instead of looking at the data in the rearview mirror like everybody else, we’re trying to put it out in advance to give a 30-day advanced snapshot of where the economy is going,” he added…(More)”.
Paper by Yuezhou Zhang et al: “With the advent of ubiquitous sensors and mobile technologies, wearables and smartphones offer a cost-effective means for monitoring mental health conditions, particularly depression. These devices enable the continuous collection of behavioral data, providing novel insights into the daily manifestations of depressive symptoms.
We found several significant links between depression severity and various behavioral biomarkers: elevated depression levels were associated with diminished sleep quality (assessed through Fitbit metrics), reduced sociability (approximated by Bluetooth), decreased levels of physical activity (quantified by step counts and GPS data), a slower cadence of daily walking (captured by smartphone accelerometers), and disturbances in circadian rhythms (analyzed across various data streams).
Leveraging digital biomarkers for assessing and continuously monitoring depression introduces a new paradigm in early detection and development of customized intervention strategies. Findings from these studies not only enhance our comprehension of depression in real-world settings but also underscore the potential of mobile technologies in the prevention and management of mental health issues…(More)”