Report by Canning Malkin and Nardine Alnemr: “Google, Meta, OpenAI and Anthropic have commissioned projects based on deliberative democracy. What was the purpose of each project? How was deliberation designed and implemented, and what were the outcomes? In this Technical Paper, Malkin and Alnemr describe the commissioning context, the purpose and remit, and the outcomes of these deliberative projects. Finally, they offer insights on contextualising projects within the broader aspirations of deliberative democracy…(More)”.
10 profound answers about the math behind AI
Article by Ethan Siegel: “Why do machines learn? Even in the recent past, this would have been a ridiculous question, as machines — i.e., computers — were only capable of executing whatever instructions a human programmer had programmed into them. With the rise of generative AI, or artificial intelligence, however, machines truly appear to be gifted with the ability to learn, refining their answers based on continued interactions with both human and non-human users. Large language model-based artificial intelligence programs, such as ChatGPT, Claude, Gemini and more, are now so widespread that they’re replacing traditional tools, including Google searches, in applications all across the world.
How did this come to be? How did we so swiftly come to live in an era where many of us are happy to turn over aspects of our lives that traditionally needed a human expert to a computer program? From financial to medical decisions, from quantum systems to protein folding, and from sorting data to finding signals in a sea of noise, many programs that leverage artificial intelligence (AI) and machine learning (ML) are far superior at these tasks compared with even the greatest human experts.
In his new book, Why Machines Learn: The Elegant Math Behind Modern AI, science writer Anil Ananthaswamy explores all of these aspects and more. I was fortunate enough to get to do a question-and-answer interview with him, and here are the 10 most profound responses he was generous enough to give….(More)”
The Great Scrape: The Clash Between Scraping and Privacy
Paper by Daniel J. Solove and Woodrow Hartzog: “Artificial intelligence (AI) systems depend on massive quantities of data, often gathered by “scraping” – the automated extraction of large amounts of data from the internet. A great deal of scraped data is about people. This personal data provides the grist for AI tools such as facial recognition, deep fakes, and generative AI. Although scraping enables web searching, archival, and meaningful scientific research, scraping for AI can also be objectionable or even harmful to individuals and society.
Organizations are scraping at an escalating pace and scale, even though many privacy laws are seemingly incongruous with the practice. In this Article, we contend that scraping must undergo a serious reckoning with privacy law. Scraping violates nearly all of the key principles in privacy laws, including fairness; individual rights and control; transparency; consent; purpose specification and secondary use restrictions; data minimization; onward transfer; and data security. With scraping, data protection laws built around these requirements are ignored.
Scraping has evaded a reckoning with privacy law largely because scrapers act as if all publicly available data were free for the taking. But the public availability of scraped data shouldn’t give scrapers a free pass. Privacy law regularly protects publicly available data, and privacy principles are implicated even when personal data is accessible to others.
This Article explores the fundamental tension between scraping and privacy law. With the zealous pursuit and astronomical growth of AI, we are in the midst of what we call the “great scrape.” There must now be a great reconciliation…(More)”.
Kenya’s biggest protest in recent history played out on a walkie-talkie app
Article by Stephanie Wangari: “Betty had never heard of the Zello app until June 18.
But as she participated in Kenya’s “GenZ protests” that month — one of the biggest in the country’s history — the app became her savior.
On Zello, “we were getting updates and also updating others on where the tear-gas canisters were being lobbed and which streets had been cordoned off,” Betty, 27, told Rest of World, requesting to be identified by a pseudonym as she feared backlash from the police. “At one point, I also alerted the group [about] suspected undercover investigative officers who were wearing balaclavas.”
The speed of communicating over Zello made it the primary tool to mobilize crowds and coordinate logistics during the protests. Stephanie Wangari
Nairobi witnessed massive protests in June as thousands of young Kenyans came out on the streets against a proposed bill that would increase taxes on staple foods and other essential goods and services. At least 39 people were killed, 361 were injured, and more than 335 were arrested by the police during the protests, according to human rights groups.
Amid the mayhem, Zello, an app developed by U.S. engineer Alexey Gavrilov in 2007, became the primary tool for protestors to communicate, mobilize crowds, and coordinate logistics. Six protesters told Rest of World that Zello, which allows smartphones to be used as walkie-talkies, helped them find meeting points, evade the police, and alert each other to potential dangers.
Digital services experts and political analysts said the app helped the protests become one of the most effective in the country’s history.
According to Herman Manyora, a political analyst and lecturer at the University of Nairobi, mobilization had always been the greatest challenge in organizing previous protests in Kenya. The ability to turn their “phones into walkie-talkies” made the difference for protesters, he told Rest of World.
“The government realized that the young people were able to navigate technological challenges. You switch off one app, such as [X], they move to another,” Manyora said.
Zello was downloaded over 40,000 times on the Google Play store in Kenya between June 17 and June 25, according to data from the company. This was “well above our usual numbers,” a company spokesperson told Rest of World. Zello did not respond to additional requests for comment…(More)“
Digital Ethology
Book edited by Tomáš Paus and Hye-Chung Kum: “Countless permutations of physical, built, and social environments surround us in space and time, influencing the air we breathe, how hot or cold we are, how many steps we take, and with whom we interact as we go about our daily lives. Assessing the dynamic processes that play out between humans and the environment is challenging. …explores how aggregate area-level data, produced at multiple locations and points in time, can reveal bidirectional—and iterative—relationships between human behavior and the environment through their digital footprints.
Experts from geospatial and data science, behavioral and brain science, epidemiology and public health, ethics, law, and urban planning consider how humans transform their environments and how environments shape human behavior…(More)”.
Mapping the Landscape of AI-Powered Nonprofits
Article by Kevin Barenblat: “Visualize the year 2050. How do you see AI having impacted the world? Whatever you’re picturing… the reality will probably be quite a bit different. Just think about the personal computer. In its early days circa the 1980s, tech companies marketed the devices for the best use cases they could imagine: reducing paperwork, doing math, and keeping track of forgettable things like birthdays and recipes. It was impossible to imagine that decades later, the larger-than-a-toaster-sized devices would be smaller than the size of Pop-Tarts, connect with billions of other devices, and respond to voice and touch.
It can be hard for us to see how new technologies will ultimately be used. The same is true of artificial intelligence. With new use cases popping up every day, we are early in the age of AI. To make sense of all the action, many landscapes have been published to organize the tech stacks and private sector applications of AI. We could not, however, find an overview of how nonprofits are using AI for impact…
AI-powered nonprofits (APNs) are already advancing solutions to many social problems, and Google.org’s recent research brief AI in Action: Accelerating Progress Towards the Sustainable Development Goals shows that AI is driving progress towards all 17 SDGs. Three goals that stand out with especially strong potential to be transformed by AI are SDG 3 (Good Health and Well-Being), SDG 4 (Quality Education), and SDG 13 (Climate Action). As such, this series focuses on how AI-powered nonprofits are transforming the climate, health care, and education sectors…(More)”.
Everyone Has A Price — And Corporations Know Yours
Article by David Dayen: “Six years ago, I was at a conference at the University of Chicago, the intellectual heart of corporate-friendly capitalism, when my eyes found the cover of the Chicago Booth Review, the business school’s flagship publication. “Are You Ready for Personalized Pricing?” the headline asked. I wasn’t, so I started reading.
The story looked at how online shopping, persistent data collection, and machine-learning algorithms could combine to generate the stuff of economists’ dreams: individual prices for each customer. It even recounted an experiment in 2015, where online employment website ZipRecruiter essentially outsourced its pricing strategy to two University of Chicago economists, Sanjog Misra and Jean-Pierre Dubé…(More)”.
(Almost) 200 Years of News-Based Economic Sentiment
Paper by Jules H. van Binsbergen, Svetlana Bryzgalova, Mayukh Mukhopadhyay & Varun Sharma: “Using text from 200 million pages of 13,000 US local newspapers and machine learning methods, we construct a 170-year-long measure of economic sentiment at the country and state levels, that expands existing measures in both the time series (by more than a century) and the cross-section. Our measure predicts GDP (both nationally and locally), consumption, and employment growth, even after controlling for commonly-used predictors, as well as monetary policy decisions. Our measure is distinct from the information in expert forecasts and leads its consensus value. Interestingly, news coverage has become increasingly negative across all states in the past half-century…(More)”.
The Collaboverse: A Collaborative Data-Sharing and Speech Analysis Platform
Paper by Justin D. Dvorak and Frank R. Boutsen: “Collaboration in the field of speech-language pathology occurs across a variety of digital devices and can entail the usage of multiple software tools, systems, file formats, and even programming languages. Unfortunately, gaps between the laboratory, clinic, and classroom can emerge in part because of siloing of data and workflows, as well as the digital divide between users. The purpose of this tutorial is to present the Collaboverse, a web-based collaborative system that unifies these domains, and describe the application of this tool to common tasks in speech-language pathology. In addition, we demonstrate its utility in machine learning (ML) applications…
This tutorial outlines key concepts in the digital divide, data management, distributed computing, and ML. It introduces the Collaboverse workspace for researchers, clinicians, and educators in speech-language pathology who wish to improve their collaborative network and leverage advanced computation abilities. It also details an ML approach to prosodic analysis….
The Collaboverse shows promise in narrowing the digital divide and is capable of generating clinically relevant data, specifically in the area of prosody, whose computational complexity has limited widespread analysis in research and clinic alike. In addition, it includes an augmentative and alternative communication app allowing visual, nontextual communication…(More)”.
Increasing The “Policy Readiness” Of Ideas
Article by Tom Kalil: “NASA and the Defense Department have developed an analytical framework called the “technology readiness level” for assessing the maturity of a technology – from basic research to a technology that is ready to be deployed.
A policy entrepreneur (anyone with an idea for a policy solution that will drive positive change) needs to realize that it is also possible to increase the “policy readiness” level of an idea by taking steps to increase the chances that a policy idea is successful, if adopted and implemented. Given that policy-makers are often time constrained, they are more likely to consider ideas where more thought has been given to the core questions that they may need to answer as part of the policy process.
A good first step is to ask questions about the policy landscape surrounding a particular idea:
1. What is a clear description of the problem or opportunity? What is the case for policymakers to devote time, energy, and political capital to the problem?
2. Is there a credible rationale for government involvement or policy change?
Economists have developed frameworks for both market failure (such as public goods, positive and negative externalities, information asymmetries, and monopolies) and government failure (such as regulatory capture, the role of interest groups in supporting policies that have concentrated benefits and diffuse costs, limited state capacity, and the inherent difficulty of aggregating timely, relevant information to make and implement policy decisions.)
3. Is there a root cause analysis of the problem? …(More)”.