Stefaan Verhulst
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)”.
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)”.
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)”.
Article by Zoë Schlanger: “In the United States, as in most other countries, weather forecasts are a freely accessible government amenity. The National Weather Service issues alerts and predictions, warning of hurricanes and excessive heat and rainfall, all at the total cost to American taxpayers of roughly $4 per person per year. Anyone with a TV, smartphone, radio, or newspaper can know what tomorrow’s weather will look like, whether a hurricane is heading toward their town, or if a drought has been forecast for the next season. Even if they get that news from a privately owned app or TV station, much of the underlying weather data are courtesy of meteorologists working for the federal government.
Charging for popular services that were previously free isn’t generally a winning political strategy. But hard-right policy makers appear poised to try to do just that should Republicans gain power in the next term. Project 2025—a nearly 900-page book of policy proposals published by the conservative think tank the Heritage Foundation—states that an incoming administration should all but dissolve the National Oceanic and Atmospheric Administration, under which the National Weather Service operates….NOAA “should be dismantled and many of its functions eliminated, sent to other agencies, privatized, or placed under the control of states and territories,” Project 2025 reads. … “The preponderance of its climate-change research should be disbanded,” the document says. It further notes that scientific agencies such as NOAA are “vulnerable to obstructionism of an Administration’s aims,” so appointees should be screened to ensure that their views are “wholly in sync” with the president’s…(More)”.
Report by the divinAI (Diversity in Artificial Intelligence) Project: “…provides a set of diversity indicators for seven core artificial intelligence (AI) conferences from 2007 to 2023: the International Joint Conference on Artificial Intelligence (IJCAI), the Annual Association for the Advancement of Artificial Intelligence (AAAI) Conference, the International Conference on Machine Learning (ICML), Neural Information Processing Systems (NeurIPS) Conference, the Association for Computing Machinery (ACM) Recommender Systems (RecSys) Conference, the European Conference on Artificial Intelligence (ECAI) and the European Conference on Machine Learning/Practice of Knowledge Discovery in Databases (ECML/PKDD) .
We observe that, in general, Conference Diversity Index (CDI) values are still low for the selected conferences, although showing a slight temporal improvement thanks to diversity initiatives in the AI field. We also note slight differences between conferences, being RecSys the one with higher comparative diversity indicators, followed by general AI conferences (IJCAI, ECAI and AAAI). The selected Machine Learning conferences NeurIPS and ICML seem to provide lower values for diversity indicators.
Regarding the different dimensions of diversity, gender diversity reflects a low proportion of female authors in all considered conferences, even given current gender diversity efforts in the field, which is in line with the low presence of women in technological fields. In terms of country distribution, we observe a notable presence of researchers from the EU, US and China in the selected conferences, where the presence of Chinese authors has increased in the last few years. Regarding institutions, universities and research centers or institutes play a central role in the AI scientific conferences under analysis, and the presence of industry seems to be more notable in machine learning conferences. An online dashboard that allows exploration and reproducibility complements the report…(More)”.
Report by the Institute for Government and Nesta: “… set out a recommended approach for how government could effectively organise itself to deliver missions. It should act as a guide for public servants at the start of a new administration that has pledged to do things differently.
Missions are designed to set bold visions for change, inspiring collaboration across the system and society to break down silos and work towards a common goal. They represent the ultimate purpose of the Government, and the story it aims to tell by the end of the Parliament.
To succeed, government will need to adopt three key roles: driving public service innovation, shaping markets and harnessing collective intelligence to improve decision-making. Achieving these missions will require strong foundations and well-recognised enablers of good government, pursued in a specific manner to bring about a cultural change in Whitehall…(More)”.
Article by Afifa Waheed: “Artificial intelligence (AI) and robotics have enormous potential in healthcare and are quickly shifting the landscape – emerging as a transformative force. They offer a new dimension to the way healthcare professionals approach disease diagnosis, treatment and monitoring. AI is being used in healthcare to help diagnose patients, for drug discovery and development, to improve physician-patient communication, to transcribe voluminous medical documents, and to analyse genomics and genetics. Labs are conducting research work faster than ever before, work that otherwise would have taken decades without the assistance of AI. AI-driven research in life sciences has included applications looking to address broad-based areas, such as diabetes, cancer, chronic kidney disease and maternal health.
In addition to increasing the knowledge of access to postnatal and neonatal care, AI can predict the risk of adverse events in antenatal and postnatal women and their neonatal care. It can be trained to identify those at risk of adverse events, by using patients’ health information such as nutrition status, age, existing health conditions and lifestyle factors.
AI can further be used to improve access to women located in rural areas with a lack of trained professionals – AI-enabled ultrasound can assist front-line workers with image interpretation for a comprehensive set of obstetrics measurements, increasing quality access to early foetal ultrasound scans. The use of AI assistants and chatbots can also improve pregnant mothers’ experience by helping them find available physicians, schedule appointments and even answer some patient questions…
Many healthcare professionals I have spoken to emphasised that pre-existing conditions such as high blood pressure that leads to preeclampsia, iron deficiency, cardiovascular disease, age-related issues for those over 35, various other existing health conditions, and failure in the progress of labour that might lead to Caesarean (C-section), could all cause maternal deaths. Training AI models to detect these diseases early on and accurately for women could prove to be beneficial. AI algorithms can leverage advanced algorithms, machine learning (ML) techniques, and predictive models to enhance decision-making, optimise healthcare delivery, and ultimately improve patient outcomes in foeto-maternal health…(More)”.
Paper by Alexander A. Guerrero: “It is widely accepted that electoral representative democracy is better — along a number of different normative dimensions — than any other alternative lawmaking political arrangement. It is not typically seen as much of a competition: it is also widely accepted that the only legitimate alternative to electoral representative democracy is some form of direct democracy, but direct democracy — we are told — would lead to bad policy. This article makes the case that there is a legitimate alternative system — one that uses lotteries, not elections, to select political officials — that would be better than electoral representative democracy. Part I diagnoses two significant failings of modern-day systems of electoral representative government: the failure of responsiveness and the failure of good governance. The argument offered suggests that these flaws run deep, so that even significant and politically unlikely reforms with respect to campaign finance and election law would make little difference. Although my distillation of the argument is novel, the basic themes will likely be familiar. I anticipate the initial response to the argument may be familiar as well: the Churchillian shrug. Parts II, III, and IV of this article represent the beginning of an effort to move past that response, to think about alternative political systems that might avoid some of the problems with the electoral representative system without introducing new and worse problems. In the second and third parts of the article, I outline an alternative political system, the lottocratic system, and present some of the virtues of such a system. In the fourth part of the article, I consider some possible problems for the system. The overall aims of this article are to raise worries for electoral systems of government, to present the lottocratic system and to defend the view that this system might be a normatively attractive alternative, removing a significant hurdle to taking a non-electoral system of government seriously as a possible improvement to electoral democracy…(More)”
Article by Jason Koebler: “Investment giant Goldman Sachs published a research paper about the economic viability of generative AI which notes that there is “little to show for” the huge amount of spending on generative AI infrastructure and questions “whether this large spend will ever pay off in terms of AI benefits and returns.”
The paper, called “Gen AI: too much spend, too little benefit?” is based on a series of interviews with Goldman Sachs economists and researchers, MIT professor Daron Acemoglu, and infrastructure experts. The paper ultimately questions whether generative AI will ever become the transformative technology that Silicon Valley and large portions of the stock market are currently betting on, but says investors may continue to get rich anyway. “Despite these concerns and constraints, we still see room for the AI theme to run, either because AI starts to deliver on its promise, or because bubbles take a long time to burst,” the paper notes.
Goldman Sachs researchers also say that AI optimism is driving large growth in stocks like Nvidia and other S&P 500 companies (the largest companies in the stock market), but say that the stock price gains we’ve seen are based on the assumption that generative AI is going to lead to higher productivity (which necessarily means automation, layoffs, lower labor costs, and higher efficiency). These stock gains are already baked in, Goldman Sachs argues in the paper: “Although the productivity pick-up that AI promises could benefit equities via higher profit growth, we find that stocks often anticipate higher productivity growth before it materializes, raising the risk of overpaying. And using our new long-term return forecasting framework, we find that a very favorable AI scenario may be required for the S&P 500 to deliver above-average returns in the coming decade.”…(More)“
Article by Shobita Parthasarathy and Jared Katzman: “…To this end, public and philanthropic research funders, universities, and the tech industry should be seeking out partnerships with struggling communities, to learn what they need from AI and build it. Regulators, too, should have their ears to the ground, not just the C-suite. Typical members of a marginalized community—or, indeed, any nonexpert community—may not know the technical details of AI, but they understand better than anyone else the power imbalances at the root of concerns surrounding AI bias and discrimination. And so it is from communities marginalized by AI, and from scholars and organizations focused on understanding and ameliorating social disadvantage, that AI designers and regulators most need to hear.
Progress toward AI equity begins at the agenda-setting stage, when funders, engineers, and corporate leaders make decisions about research and development priorities. This is usually seen as a technical or management task, to be carried out by experts who understand the state of scientific play and the unmet needs of the market… A heartening example comes from Carnegie Mellon University, where computer scientists worked with residents in the institution’s home city of Pittsburgh to build a technology that monitored and visualized local air quality. The collaboration began when researchers attended community meetings where they heard from residents who were suffering the effects of air pollution from a nearby factory. The residents had struggled to get the attention of local and national officials because they were unable to provide the sort of data that would motivate interest in their case. The researchers got to work on prototype systems that could produce the needed data and refined their technology in response to community input. Eventually their system brought together heterogeneous information, including crowdsourced smell reports, video footage of factory smokestacks, and air-quality and wind data, which the residents then submitted to government entities. After reviewing the data, administrators at the Environmental Protection Agency agreed to review the factory’s compliance, and within a year the factory’s parent company announced that the facility would close…(More)”.