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  • Queen Camilla and how she deals with being the Firm’s ‘black sheep’ to Diana’s uncle

    Queen Camilla and how she deals with being the Firm’s ‘black sheep’ to Diana’s uncle

    Queen Camilla’s chat with Princess Diana’s uncle about being ‘black sheep’

    Experts have just recounted a conversation between Queen Camilla and the uncle of Princess Diana, Gary Goldsmith. 

    For those unversed, the conversation was around the days leading up to her wedding.

    The Daily Mail’s Royal Editor Rebecca English revealed this comment in an episode of the Palace Confidential podcast.

    “It was very illuminating,” Ms English began by saying. because “He [Gary] was talking about being at a palace function, I think probably in the run-up to the wedding and feeling slightly like a fish out of water.”

    At that moment “Camilla, the then Duchess of Cornwall, came up to talk to him. And he kind of said words to the effect of ‘Well, I’m not sure you want to be seen talking to me because I’m the black sheep of the family’.”

    But the now-Queen “she kind of smiled and laughed and went: ‘Oh don’t worry, so have I been over the years, you know, you just roll with, it’s fine We’ll be kind of two black sheep together!’.”

    She even hailed the Queen for her frankness in that moment and said, “I thought that showed her quite amusing reaction to someone who has definitely been on the receiving end of her fair share of brickbats over the years.”


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  • All-cause and cause-specific mortality attributable to educational inequalities in Spain | BMC Public Health

    All-cause and cause-specific mortality attributable to educational inequalities in Spain | BMC Public Health

    In this study, we estimate the number of deaths, proportions of deaths, and years of life lost attributable to educational inequality using data from Spain. During the study period, there were approximately 420,000 average annual deaths in Spain, of which more than 80,000 were estimated to be attributable to educational inequality (AF = 19,1%). We find important differences between education groups, sexes, age groups, and causes of death. In absolute terms, older groups contributed most to the mortality attributable to educational inequality, while in relative terms, younger age groups had a higher share of deaths attributable to inequality (e.g., 50% in males aged 35–39). This inequality is explained by specific causes of death, led by circulatory diseases in females and by neoplasms and circulatory diseases in males.

    Strengths and limitations

    Using recent data for the period 2016-21, containing more than 2.5 million deaths, we give details on mortality attributable to educational inequality for broad and granular sets of causes. However, our study relies on an assumption that deserves attention. In our counterfactual, we assume that the whole population would die at the same rate as the highly educated, as has been done similarly in previous studies [14,15,16,17]. This is somewhat unrealistic in the short term, but it is possible in the medium term. Such a massive change does not imply population shifts between nominal education groups but rather the elimination of circumstances leading to mortality differences across educational groups. In other words, educational attainment does not necessarily directly kill (or protect) per se, but it is a proxy for or determinant of other variables that are linked to mortality risks (e.g., income, occupation, health behaviours, racism, and myriad others). Our findings are sensitive to the education definitions used: The number and distribution of population and death counts across education groups have a strong impact on the potential number of deaths attributable to inequality. For instance, if we could distinguish more socioeconomic groups, the number of estimated deaths attributable to inequality would tend to increase, and vice versa. Finally, we did not analyse trends over our study period, and we suggest these should be evaluated in further studies.

    Comparison and explanation of results

    Our results showing that 19% of mortality is attributable to educational inequality are similar to those obtained for the populations of South Korea and the USA [14, 16], but the differences in study settings and educational groupings are a barrier to accurate comparison of estimates. Our estimates of the attributable fractions for ages 35–74 were 19% and 31% for females and males, respectively, and appear to be lower than those derived using area-level socioeconomic indicators in the UK in 2013-18, which found that this explained 33% and 37% [17], respectively, and compared with the Japanese study for ages 30–79, which found that 11–16% of mortality was attributable to educational inequality [15]. Direct comparison of these figures may be misleading due to differences in the choice of socioeconomic variables and groupings, age ranges, group-specific mortality conditions, and population age structures. Yet, our results suggest that in Spain, a country with relatively low (educational) inequalities in mortality [2, 7], the magnitude of these inequalities and the potential for improving mortality at the population level mortality are substantial.

    To give a better sense of the magnitudes we report, we translate these results into other intuitive metrics. Estimated mortality due to educational inequality would represent, for instance, the third cause of death for both females and males, with comparable results to those from COVID-19 causes in 2020, which accounted for 16.4% of deaths according to official statistics [26]. Counterfactual remaining life expectancy at age 35 (that of the high education group) would be 52.9 and 48.6 years for females and males, respectively, 1.6 and 5.5 years higher than that observed in 2019 (see Table S4). These life expectancy gaps are equivalent to 10 and 30 years of recent life expectancy progress before the pandemic (2009–2019, and 1989–2019)for females and males, respectively. Additionally, our estimates are higher than those from the COVID-19 pandemic in 2020 [27], mainly due to the younger age profiles for deaths due to education inequality, particularly for males.

    Additionally, our results can be put into context by comparing them with estimates of attributable fractions for major risk factors, for instance smoking. According to recent estimates, the AFs of smoking-related mortality were 4.3% and 14.1% in females and males, respectively [28]. These figures contrast with the higher figures (19% for both sexes) we estimate for education, suggesting that reducing educational inequalities in mortality would potentially have a greater beneficial effect on mortality than eliminating smoking-related mortality. Nevertheless, we should acknowledge that the AF difference between smoking and education owes to the relative sizes between age-specific population groups (current smokers vs. low educated) rather than to the relative mortality penalty, which is higher for smoking. Again, although the underlying assumptions used to obtain these estimates are strong and both factors tend to be correlated, these figures help contextualize the large impact of educational inequalities on mortality in Spain.

    The detailed cause-of-death information we use in this study allows us to discuss potential health determinants. Cardiovascular causes accounted for an important number of deaths due to inequality for both males and females and were led by ischaemic heart disease and stroke. Although both males and females have relatively similar estimates of mortality attributable to educational inequality, important differences in age patterns and causes of death exist. For females, deaths attributable to inequality occurred at older ages and were led by cardiovascular diseases both in terms of deaths and years of life lost, while male deaths tended to occur at relatively younger ages, and there was more variability in the leading causes (neoplasms and circulatory causes accounted for 56% of all deaths attributable to inequality). Beyond cardiovascular causes, deaths attributable to inequality among females were dominated by Alzheimer’s disease (1,800 annual deaths) and other dementias (3,350 annual deaths), genitourinary diseases (over 3,000 annual deaths), and due to diabetes, were dominant (see Table S3). A glance at the cause-of-death contributions to the observed AF reveals these to be largely considered amenable to healthcare or avoidable through primary intervention (e.g. lung cancer, diabetes, hypertensive heart disease, and ischemic heart disease), highlighting the significance of socioeconomic inequality for reducing mortality.

    For males, the figures for Alzheimer’s disease, other dementias, and genitourinary diseases were less than half the estimates compared to those for females, which is consistent with evidence of sex differences in the prevalence of dementia [29]. On the other hand, male educational gradient is important for causes that are related to smoking behavior: lung cancer or chronic lung diseases, as well as liver diseases and external causes of death (Table S3). Beyond smoking, the magnitude of the described socioeconomic inequalities in mortality suggests that lower socioeconomic classes are exposed to additional risks. Well-established cardiovascular risk factors, such as the prevalence of obesity, hypertension, diabetes, or high blood cholesterol in blood, are among the mechanisms through which socioeconomic gradients exist [30]. In both absolute terms and in attributable fraction terms, hypertension and diabetes have a greater impact on females compared to males. Other mechanisms that may discriminate more against lower socioeconomic groups include food choices, housing quality or health care [3], although their quantification is beyond the scope of this study. Overall, the differential impact of well-established risk factors between males and females seems to be explained by the different composition of mortality, as the differences between males and females in these main factors disappear when focusing on premature mortality (see Table S3).

    The AF age patterns, higher at young ages, discernable in Fig. 2 merit some speculation. First, the decreasing pattern over age is driven entirely by the underlying pattern of relative risk (see Figure S1). Second, education outcomes partly depend on health [31], which has the effect of increasing relative risk, and by extension AF, in younger ages. This pattern is seen more broadly over a range of acquired health conditions or social statuses, where interactions between socioeconomic status and health are postulated as key drivers [32, 33]. Third, we also know that health selection in the low-education group is increasing over time [34]), such that the age pattern we see could be partly driven by cohort patterns. The health conditions that might co-determine education outcomes and later contribute to elevated younger-age AFs are themselves, to a large degree, socially determined and therefore are indirectly accounted for within our counterfactual.

    The rapid educational expansion of the Spanish society in recent decades suggests that, in absolute numbers (i.e. deaths attributable to inequality), the inequalities described in this paper are unlikely to be increasing, and that absolute sex differences seem to be decreasing. That is, the observed and expected changes in the educational composition of the Spanish population [35] are favourable for further reducing absolute educational inequalities and sex differences. However, unhealthy lifestyles among young generations remain a major concern for current and future health dynamics. For instance, several studies have demonstrated the social importance and its impact on the adoption of lifestyles by adolescents, such as binge drinking [36], or the increase in the prevalence of obesity [37, 38], which is associated with parental education and social deprivation [39, 40]. Life course approaches accounting for understanding the role of (cumulative) risk factors and lifestyle exposures and interactions on health outcomes have great potential to influence mid- and long-term population health and mortality outcomes [41]. Further studies should monitor unhealthy lifestyles in younger generations and the impact they may have on current and future all-cause mortality and mortality inequalities.

    Education is one of many social determinants of health, and we have shown its power to be substantial in examining socioeconomic inequalities in Spain, in line with findings from previous studies focusing on other countries [14,15,16]. Our results imply that interventions that shift the education distribution upward represent an indirect form of prevention for various causes of death. Yet, we acknowledge that the potential health effects of further educational shifts may be more important in the mid- and long-term. Similar mechanisms should be expected for any socially equitable intervention, including those that do not contemplate health as an outcome, as has been the case for contemporary educational expansion. Increases in social equality, a goal that is valuable in itself, also act to improve population health and contribute to overall mortality reduction. In the short term, policy interventions improving access and quality of public health care or strengthening the social security system may contribute to narrowing socioeconomic health gaps.

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  • A Multicentric Study to Validate the Patient-Reported Experience Measures (PREM) Tool for Wound Management and Safety in India

    A Multicentric Study to Validate the Patient-Reported Experience Measures (PREM) Tool for Wound Management and Safety in India


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  • Speech by Governor Cook on artificial intelligence and innovation

    Speech by Governor Cook on artificial intelligence and innovation

    Thank you, Avi. It is an honor to be back with you at the NBER Summer Institute. Thanks to you, Erik, and Catherine for organizing these insightful and thought-provoking sessions this summer.1

    Artificial intelligence (AI) is advancing across the globe and permeating every corner of the economy at an incredibly rapid rate. This has significant implications for Federal Reserve leaders, both as policymakers and managers of the organization. AI is transforming the economy, including by accelerating how quickly we generate ideas and making workers more efficient, and that, in turn, will affect both sides of our dual mandate of maximum employment and price stability. AI also is beginning to affect the way we conduct economic research within the Federal Reserve System, with the potential to make some tasks more efficient, harness nontraditional data in new ways, and broaden and deepen economic analysis.

    I believe we are at an inflection point. As I have stated before, I, like some of you here today, see AI as the next general-purpose technology (GPT)2. As many of you in this room know and have written about, GPTs, like the printing press or electric power, matter immensely to innovation.3 Similar to those seminal advances, AI will likely spread throughout the economy more broadly, spark innovation, and improve over time.

    Among large language models (LLMs), the highest scores on benchmark intelligence tests have almost doubled over the past 12 months, according to the Artificial Analysis Intelligence Index.4 The competition to improve is fierce: The leaderboard for the AI lab offering the best model switched six times in the past half a year. And the technology is diffusing rapidly. ChatGPT launched about three years ago, and now more than half a billion users engage with the internet-based LLM weekly.5 LLMs are super cool and grab the headlines, but there is a lot more to AI, which can be an important driver of productivity. Advances in multimedia generation is another way to think of AI’s fast advancement. It took human creators decades to move from silent pictures to “talkie” movies; AI models accomplished this advance in less than a year.

    AI is poised to alter the contours of the global economy. In doing so, it has the potential to materially affect both sides of the Fed’s dual mandate. On the maximum-employment side of the mandate, AI can generate new tasks and jobs and possibly eliminate others, similar to many past technological innovations. On the price-stability side of our mandate, AI can improve productivity, which can lower inflationary pressures, but it can also boost prices in the interim, as AI adoption may lead to a surge in aggregate investment. Studying the net effects of AI on the economy over time will be critical to setting appropriate monetary policy. However, at the Fed, we are not only considering AI’s implications for the economy but also employing strategies to harness the technology’s power inside our walls.

    Commensurate with rapid improvements in AI, its adoption is accelerating across government and industry. As a result, there is an urgency for the Fed to both study AI’s effects and capture more of its benefits to maintain a highly productive workforce and extract additional insights from new economic analysis.

    Having spent much of my career studying the innovation production function and collecting and examining data on the economic effects of technology, productivity, and innovation, I am coupling caution with this optimism. This is consistent with my view when I was a research associate here at NBER and when I first spoke about AI at the NBER AI meeting in Toronto in 2018 before I joined the Board of Governors. While I see AI adoption as broadly beneficial to the economy and society, I know from economic history and the history of technology that there could be many multidimensional challenges to adopting it. With that in mind, I will start by offering general principles I believe guide our society’s engagement with AI. Next, I will describe recent progress on AI research at the Fed. I will then say a bit about both the opportunities and constraints I see affecting the wider adoption of AI. Finally, I will offer some brief remarks on how AI factors into my thinking on monetary policy.

    Responsible AI Adoption

    I want to start by stressing that any organization engaging with this technology should take a thoughtful and structured approach to AI adoption. I can offer four guiding principles for what I view as responsible AI adoption.

    First and foremost is establishing strong governance and risk management. A central tenet of good governance should be the mindset that humans are in the loop, because it ensures that people guide AI rather than allow AI to guide us. In a speech last year, I told a story about how Benjamin Franklin lost a game of chess to a machine called the “Mechanical Turk”6 Of course, there was a human chess master hidden inside. What might seem like a silly tale contains an important lesson for organizations and governments deploying AI: Like the Mechanical Turk, ultimately the human inside the machine is still in charge. Relatedly, organizations also must be careful about privacy, cybersecurity, and leakage of confidential and internal information.

    A second principle is that education and training of staff are critical to get and keep employees at the technological frontier. A third principle is empowerment. Teams within organizations should be encouraged to learn by doing and engage hands-on with AI technologies in controlled environments. Finally, a fourth principle is experimentation. Organizations should maintain a spirit of openness while retaining the ability to halt projects that do not meet rigorous standards.

    AI Research at the Fed

    Like many other leading organizations and researchers around the world, the Federal Reserve is working hard to understand AI’s implications for our mission and our own work. To be clear, the FOMC is not using AI in developing or setting policy, but rather to aid staff in their other tasks such as writing, coding, and research. For example, we have been deepening our understanding of the capabilities of LLMs and other machine learning models to produce economic insights.7 Several Fed papers document what we are learning. Board economists Wendy Dunn and Nitish Sinha, with coauthors Ellen Meade and Raakin Kabir found that LLMs have surprisingly good understanding of economic topics discussed in the FOMC minutes.8 In a recent paper, Board economist Paul Soto measured AI research and development by examining firms’ earnings conference calls using deep learning.9 Richmond Fed economist Anne Hansen, with coauthors, John Horton, Sophia Kazinnik, Daniela Puzzello and Ali Zarifhonarvar found partial success in simulating the Survey of Professional Forecasters’ panel using an LLM and create synthetic forecasters that often achieve superior accuracy, particularly at medium- and long-term horizons.10 A paper by Mary Chen, Matthew DeHaven, Isabel Kitschelt, Seung Jung Lee, and Martin Sicilian used machine learning techniques on a variety of unstructured textual data to identify and forecast financial crises.11 Another paper, by Leland Crane, Emily Green, Molly Harnish, Will McClennan, Paul Soto, Betsy Vrankovich, and Jacob Williams, harnessed the ability of an open-weight model to read Work Adjustment and Retraining Notifications to create a real-time measure of layoffs.12 As our researchers examine LLMs and other machine learning techniques critically, some research has demonstrated the benefits of AI, and other research has provided important insights about its limits and where we should be careful about AI.

    By actively engaging with and learning about AI tools in our research, we not only enhance our analytical capabilities, but also gain invaluable insights into the broader economic implications of AI. Researchers at the Fed are also examining the state of AI adoption and the potential of AI to affect our economy. A timely indicator of generative AI (GenAI) adoption in the U.S. has been developed by St. Louis Fed economist Alexander Bick, along with his coauthors Adam Blandin and David Deming, through a repeated survey.13 They also find that, so far, GenAI adoption for uses outside of work has been faster than personal computer (PC) adoption after its introduction. In the workplace, they find GenAI adoption has happened at a similar pace as occurred with PCs. Work from David Byrne and Paul Soto, along with coauthors Martin Baily and Aidan Kane, suggests that GenAI has the potential to be a GPT and could benefit the economy in other ways too such as by being an invention that itself leads to more innovation.14

    In addition, Fed staff from across our divisions keep abreast with the rapid developments in GenAI by engaging regularly with other researchers and experts from academia, other central banks, and the industry through various seminars, workshops, interviews, and invited presentation series.

    Simultaneously, the research at the Fed is proceeding deliberately and cautiously, as many AI tools are not yet ready to be put into production. For example, Leland Crane, Akhil Karra and Paul Soto show that when it comes to real-time analysis, LLMs suffer from look-ahead bias and frequently get confused by the vintage nature of economic data releases.15 Even for historical analysis, the researchers note: “From the perspective of historical analysis, an LLM may not reliably recall the details of real time data flow during historical episodes, limiting the reliability of historical analysis.”

    The Speed of Adoption

    Our experience with AI at the Fed is also informative about why we are not seeing more widespread adoption of AI in the economy, despite its remarkable pace of improvement and the apparently large potential economic gains.

    First, as in all industries grappling with AI, the workforce must be trained to take advantage of a rapidly changing technology that is strikingly different from previous technologies. Often, the premise of technology has been to automate routine tasks where the steps involved are predetermined. The premise of AI is different from technologies of the past. AI promises to augment areas of work involving human judgement, which do not follow any predetermined steps. Thus, education and training must evolve.

    Second, large organizations learn to use new tools through hands-on experimentation and shared experiences, and it takes time for the knowledge to diffuse. Some of these are planned and organized, while others are more organic and spontaneous. For example, earlier this year, the Board hosted an AI expo where AI early adopters shared their experiences with AI and innovative AI use cases. Events such as this showcase cross-functional, cross-organizational collaboration among participants throughout the Federal Reserve System and demonstrate how AI-driven solutions could address challenges in areas such as economic analysis, financial stability, and operations. These venues provide excellent avenues for sharing successes and failures in trying out different use cases and, in many cases, enable the broader community to engage with prototypes of AI applications. In addition to demystifying AI and encouraging its use, these events try to establish and promote cultural norms of responsible AI usage, which generates ideas related to the types of problems AI is better or worse at solving.

    Finally, organizations will also have a rational desire to be selective about which advances to adopt when the technology is rapidly changing. For example, we are seeing that some highly effective prompting strategies for older models are no longer necessary for thinking models. As with any new general-purpose technology, there is likely to be an extended period of learning by doing. This will be particularly important for the high-profile LLM models. With these models, even the developers are not fully aware of their capabilities, and organizations, including the Fed, learn about their abilities and limitations only once they are put to use.

    Implications for Monetary Policy

    Given what we know, as well as what we learn from researchers like you, we are thinking carefully about the implications of AI for monetary policy. As AI filters through the economy, it has the potential to affect both sides of our dual mandate in different ways.

    As with other technological innovations, AI is poised to reshape our labor market, which in turn could affect our notion of maximum employment or our estimate of the natural rate of unemployment. I see it as likely that AI will allow workers to be more productive while also changing the tasks associated with any given job. As with many technological breakthroughs, a certain set of jobs may be replaced. We must recognize the challenges and potential pain this may bring, and we are watching this closely. A successful response to these disruptions will be of paramount importance but lies outside the mandate of monetary policy. Fortunately, new types of employment, whether tasks or occupations, are also being created.

    In terms of price stability, AI is likely to boost productivity and could help the economy achieve higher growth while reducing inflationary pressures, because those productivity improvements can counter labor cost increases. In addition, the ability of AI to process and analyze ever larger amounts of data will likely lead to advances in scientific research and innovation, resulting in an increased arrival rate of ideas, further amplifying its effect on productivity. As I have noted in recent speeches, it is possible that the disinflationary effect of AI could, over time, counter any factors putting upward pressure on inflation.16 It is also possible that AI could boost prices in the interim, as adoption of the technology might require a surge in aggregate investment.

    I am constantly monitoring incoming data, the ever-evolving outlook, and a broad range of risks to both sides of the dual mandate. I tend to be cautiously optimistic when I anticipate what AI could bring to the economy, but much uncertainty remains. As I have laid out for my institution specifically and the economy broadly, AI is a technology that is rapidly evolving, and it is good to be humble about our understanding of its exact effects on our economy and the timing of those effects.

    Conclusion

    To conclude, I see us at a moment of inflection where AI is being deployed as a general-purpose technology. Babies born today will ask what life was like before LLMs, just as today’s college students quiz us about what life was like before the internet and mobile phones. This is a moment for excitement and optimism, but also one we are taking seriously at the Federal Reserve. As I have described, AI will both change the economy for which we set policy and change how we can best operate as a central bank.

    Much more remains to be learned and understood about how AI will affect our economy and our everyday lives. This is why gatherings and discussions like these in the 48th session of the NBER Summer Institute are so important. I am excited to learn about the careful and insightful research presented by former colleagues, graduate students, and many others at the Summer Institute, and I look forward to listening to the remaining discussions today.

    Thank you.

    1. The views expressed here are my own and not necessarily those of my colleagues on the Federal Open Market Committee. Return to text

    2. See Lisa D. Cook (2023), “Generative AI, Productivity, the Labor Market, and Choice Behavior,” speech delivered at the National Bureau of Economic Research Economics of Artificial Intelligence Conference, Toronto, September 22. Return to text

    3. For the original notion of general-purpose technology, see, for example, Timothy F. Bresnahan and Manuel Trajtenberg (1995), “General Purpose Technologies ‘Engines of Growth’?” Journal of Econometrics, vol. 65 (January), pp. 83–108. For an assessment of how IT can boost productivity in the early 21st century, see Boyan Jovanovic and Peter L. Rousseau (2005), “General Purpose Technologies,” in Philippe Aghion and Steven N. Durlauf, eds., Handbook of Economic Growth, vol. 1B (Amsterdam: Elsevier), pp. 1181–224. More recently, Eloundou and others (2024) note that generative artificial intelligence could be a GPT and provide a framework for assessing that; see Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock (2024), “GPTs Are GPTs: Labor Market Impact Potential of LLMs,” Science, vol. 384 (6702), pp. 1306–08. Return to text

    4. See the chart “Frontier Language Model Intelligence, Over Time” on Artificial Analysis’s website at https://artificialanalysis.ai/#frontier-language-model-intelligence-over-time; the index calculation is described at https://artificialanalysis.ai/methodology/intelligence-benchmarking. Return to text

    5. See “Ideas to Power Democratic AI” on OpenAI’s website at https://cdn.openai.com/global-affairs/9c98a71f-7d2f-4566-9da7-4a7628c60bea/oai-ideas-to-power-democratic-ai-june-2025.pdf. Return to text

    6. See Lisa D. Cook (2024), “Artificial Intelligence, Big Data, and the Path Ahead for Productivity,” speech delivered at “Technology-Enabled Disruption: Implications of AI, Big Data, and Remote Work,” a conference organized by the Federal Reserve Banks of Atlanta, Boston, and Richmond, Atlanta, October 1. Return to text

    7. See Anton Korinek (2023), “Generative AI for Economic Research: Use Cases and Implications for Economists (PDF),” Journal of Economic Literature, vol. 61 (December), pp. 1281–317; Anton Korinek (2024), “LLMs Level Up—Better, Faster, Cheaper: June 2024 Update to Section 3 of ‘Generative AI for Economic Research: Use Cases and Implications for Economists,’ published in the Journal of Economic Literature 61(4);” and Anton Korinek (2024), “LLMs Learn to Collaborate and Reason: December 2024 Update to ‘Generative AI for Economic Research: Use Cases and Implications for Economists (PDF),’ published in the Journal of Economic Literature 61(4).” Return to text

    8. See Wendy Dunn, Ellen E. Meade, Nitish Ranjan Sinha, and Raakin Kabir (2024), “Using Generative AI Models to Understand FOMC Monetary Policy Discussions,” FEDS Notes (Washington: Board of Governors of the Federal Reserve System, December 6). Return to text

    9. See Paul E. Soto (2025), “Research in Commotion: Measuring AI Research and Development through Conference Call Transcripts,” Finance and Economics Discussion Series 2025-011 (Washington: Board of Governors of the Federal Reserve System, February). Return to text

    10. See Anne Lundgaard Hansen, John J. Horton, Sophia Kazinnik, Daniela Puzzello, and Ali Zarifhonarvar (2024), “Simulating the Survey of Professional Forecasters,” available at SSRN: http://dx.doi.org/10.2139/ssrn.5066286. Return to text

    11. See Mary Chen, Matthew DeHaven, Isabel Kitschelt, Seung Jung Lee, and Martin J. Sicilian (2023), “Identifying Financial Crises Using Machine Learning on Textual Data,” Journal of Risk and Financial Management, 16(3): 161, https://doi.org/10.3390/jrfm16030161. Return to text

    12. See Leland D. Crane, Emily Green, Molly Harnish, Will McClennan, Paul E. Soto, Betsy Vrankovich, and Jacob Williams (2024), “Tracking Real Time Layoffs with SEC Filings: A Preliminary Investigation,” Finance and Economics Discussion Series 2024-020 (Washington: Board of Governors of the Federal Reserve System, April). Return to text

    13. See Alexander Bick, Adam Blandin, and David J. Deming (2024), “The Rapid Adoption of Generative AI,” NBER Working Paper Series 132966 (Cambridge, Mass.: National Bureau of Economic Research, September; revised February 2025). Return to text

    14. See Martin Baily, David Byrne, Aidan Kane, and Paul Soto (2025), “Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope?” working paper. Return to text

    15. See Leland D. Crane, Akhil Karra, and Paul E. Soto (2025), “Total Recall? Evaluating the Macroeconomic Knowledge of Large Language Models,” Finance and Economics Discussion Series 2025-044 (Washington: Board of Governors of the Federal Reserve System, June). Return to text

    16. See Lisa D. Cook (2025), “Opening Remarks on Productivity Dynamics,” speech delivered at “Finishing the Job and New Challenges,” a monetary policy conference hosted by the Hoover Institution, Stanford University, Stanford, Calif., May 9. Return to text

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  • Google is adding its Gemini 2.5 Pro to Search: What’s in it for users

    Google is adding its Gemini 2.5 Pro to Search: What’s in it for users

    Google has announced an upgrade to its Search capabilities. The company said that it is rolling out access to Gemini 2.5 Pro model – what it calls the most advanced model for complex tasks – and Deep Search within AI Mode. This particular mode was first made available in the US and was recently launched in India.The latest AI capabilities will be available for Google AI Pro and AI Ultra subscribers. The company also unveiled a new “agentic” feature: AI-powered calling to local businesses, aiming to streamline everyday tasks for users.“Gemini 2.5 Pro and Deep Search are rolling out in AI Mode to help with more complex questions and research – available now to Google AI Pro/Ultra subscribers,” Google CEO Sundar Pichai said.“Plus, a new agentic capability directly in Search: AI-powered calling to local businesses, rolling out to all users in the US,” he added.

    Google’s Gemini 2.5 Pro arrives in AI Mode

    Google AI Pro and AI Ultra subscribers can access Gemini 2.5 Pro directly within Search’s AI Mode. This integration provides users with Google’s most intelligent AI model for tackling complex queries. Subscribers can select the 2.5 Pro model from a dropdown menu in the AI Mode tab. The default AI Mode model will continue to offer general assistance for most questions, Google said.For users requiring more in-depth information, Google is introducing Deep Search capabilities within AI Mode, also powered by Gemini 2.5 Pro. Described as Google Search’s most advanced research tool, Deep Search can “save hours by issuing hundreds of searches, reasoning across disparate pieces of information and crafting a comprehensive, fully-cited report in minutes.”Deep Search is particularly beneficial for extensive research related to professional work, hobbies, studies, or significant life decisions like purchasing a house or financial analysis. According to Google, the rollout of Deep Search and Gemini 2.5 Pro begins this week for Google AI Pro and AI Ultra subscribers in the US who have opted into the AI Mode experiment in Google Labs.

    Google will allow AI to help you shop with AI-powered calling

    Google is also introducing an “agentic” capability directly into Search: AI-powered calling to local businesses. This feature allows users to delegate tasks like checking pricing and availability for services such as pet grooming or dry cleaning, without needing to make the call themselves.To use this feature, users can search for services like “pet groomers near me” and will see an option to “Have AI check pricing.” After submitting the request, Search will compile information on appointments and services from various businesses, presenting users with a range of options.This AI calling capability is now rolling out to all Search users in the US, with Google AI Pro and AI Ultra subscribers receiving higher usage limits.


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  • Restricting 1 amino acid in food could speed wound healing

    Restricting 1 amino acid in food could speed wound healing



    Restricting an amino acid found in common foods could potentially speed up wound healing, researchers report.

    The skin has two types of adult stem cells: epidermal and hair follicle. Their jobs seem pretty well-defined: maintain the skin, or maintain hair growth.

    But as research from Rockefeller University has shown, hair follicle stem cells (HFSCs) can switch teams, pitching in to heal the skin when it receives an injury. How do these cells know it’s time to pivot?

    The lab behind those original findings has now identified a key signal telling HFSCs when to drop the hair cycle and pick up the skin repair: an integrated stress response (ISR) that directs stem cells to conserve energy for essential tasks.

    In the skin, nutrient deficits are sensed by a non-essential amino acid known as serine that’s found in common foods such as meat, grains, and milk. As they demonstrate in a recent study in Cell Metabolism, when serine levels drop, the ISR is activated, causing HFSCs to slow hair production. If the skin is injured on top of nutrient deficits, the ISR is elevated even more, halting hair production and funneling efforts towards skin repair. This reprioritization accelerates the healing process.

    “Serine deprivation triggers a highly sensitive cellular ‘dial’ that fine tunes the cell’s fate—towards skin and away from hair,” says first author Jesse Novak, a current MD-PhD student at Weill Cornell’s Tri-Institutional MD-PhD Program and former PhD student in Rockefeller’s Robin Chemers Neustein Laboratory of Mammalian Cell Biology and Development, led by Elaine Fuchs.

    “Our findings suggest that we might be able to speed up the healing of skin wounds by manipulating serine levels through diet or medications.”

    Adult tissues harbor stem cell pools that tightly balance cell proliferation, differentiation, and turnover to maintain homeostasis, or normal functioning, and repair wounds. But their metabolic needs remain poorly understood.

    For the current study, Novak aimed to identify the metabolic factors that keep stem cells humming along during everyday operations—and then track what changes when an injury forces HFSCs to moonlight in wound recovery.

    “Most skin wounds that we get are from abrasions, which destroy the upper part of the skin. That area is home to a pool of stem cells that normally takes charge in wound repair. But when these cells are destroyed, it forces hair follicle stem cells to take the lead in repair,” Novak says.

    “Knowing that, we thought that tracking these skin cells through wound healing presented a very good model for testing if and how metabolites are regulating this process overall.”

    Previous findings from the Fuchs lab indicated that pre-cancerous skin stem cells become addicted to serine circulating in the body, and that these cells can be prevented from turning fully cancerous by restricting serine in the diet. These findings demonstrated that the metabolite is a key regulator of tumor formation and inspired trials to implement serine-free diets as cancer treatments. But no one understood how dietary serine deprivation would affect normal tissue functioning. So Novak focused on this amino acid for his studies.

    The team subjected the hair follicle stem cells to a series of metabolic stress tests by either depriving them of serine in their diet or using genetic tricks in mice to selectively prevent hair follicle stem cells from making serine. They found that serine is in direct and constant communication with the ISR, a trigger activated when tissue conditions go off balance. When the serine tank is low, HFSCs tune down hair growth, which requires substantial energy.

    Turning to another stress challenge, the team then focused on wound repair. They discovered that the ISR also activates in HFSCs after injury. Moreover, when mice experience both serine deficiency and injury, the pendulum swings even further, suppressing hair regeneration and favoring wound repair. In this way, the ISR measures overall tissue stress levels and prioritizes regenerative tasks accordingly.

    “No one likes to lose hair, but when it comes down to survival in stressful times, repairing the epidermis takes precedence,” says Fuchs. “A missing patch of hair isn’t a threat to an animal, but an unhealed wound is.”

    It was clear that low levels of serine had a significant impact on stem cell fate and behavior. But what about the opposite? Could a large dose of serine supercharge hair growth, for example?

    Unfortunately for anyone with hair loss, it turns out that the body tightly regulates the amount of serine in circulation. When Novak fed mice six times the amount of serine than normal, their serine levels only rose 50%.

    “However, we did see that if we prevented a stem cell from making its own serine and replenished its losses through a high-serine diet, we were able to partially rescue hair regeneration,” Novak adds.

    Next on the horizon is exploring the potential to speed up wound healing through reducing dietary serine or via medications that affect serine levels or ISR activity. The team also wants to test other amino acids to find out whether serine is unique in its influence.

    “Overall, the ability of stem cells to make cell fate decisions based upon the levels of stress they experience is likely to have broad implications for how tissues optimize their regenerative capacities in times where resources are scarce,” says Fuchs.

    Source: Rockefeller University

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  • Titan’s Hydrocarbon Seas Could Have the Conditions for Protocells, Study Shows – extremetech.com

    1. Titan’s Hydrocarbon Seas Could Have the Conditions for Protocells, Study Shows  extremetech.com
    2. The precursors of life could form in the lakes of Saturn’s moon Titan  Space
    3. A proposed mechanism for the formation of protocell-like structures on Titan | International Journal of Astrobiology  Cambridge University Press & Assessment
    4. Scientists Just Showed How Alien Life Could Emerge in Titan’s Methane Lakes  ZME Science
    5. Titan’s methane lakes could foster an early step in the creation of life  Mashable

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  • Aditi Chauhan, Indian women’s football pioneer, retires after 17 years

    Aditi Chauhan, Indian women’s football pioneer, retires after 17 years

    Aditi Chauhan, a pioneer in Indian women’s football, announced her retirement from the sport on Thursday, bringing down the curtains on an illustrious career spanning 17 years.

    The former Indian national team goalkeeper, now 32 years old, holds the distinction of being the first-ever player from India to play club football in Europe.

    Having already been capped by India at the U19 and senior level, Aditi Chauhan’s first taste of football abroad came with Loughborough University, whom she represented while pursuing a Master’s degree in Sports Management.

    Her performances caught the attention of Women’s Super League outfit West Ham United Ladies and she signed for the west Londoners in 2015, She was a part of the Irons for four years.

    “Thank you, football — for shaping me, testing me, and carrying me through,” Aditi said on a social media post. “After 17 unforgettable years, I’m retiring from professional football with deep gratitude and pride. This game gave me more than just a career; it gave me an identity.

    “From chasing a dream in Delhi to carving out my own path all the way to the UK, where I pursued my Master’s in Sports Management and played for West Ham United – I walked a road with no clear map. I never had to choose between education and passion. I fought hard to do both, and that balance has defined me.”

    She returned to India in 2018 and joined India Rush SC for one season before joining Gokulam Kerala, with whom she won the Indian Women’s League (IWL) twice.

    Aditi Chauhan also finished third with Gokulam Kerala in the AFC Women’s Club Championship in 2021.

    She also represented Iceland’s Hamar Hveragerði in 2021 and called time on her career after one last stint with Sreebhumi FC in 2025.

    On the international stage, Aditi earned 57 caps with the Indian women’s football team between 2011 and 2023, winning the SAFF Women’s Championship thrice – in 2012, 2016 and 2019.

    She also boasts two South Asian Games gold medals, won in 2016 and 2019.

    Aditi also revealed her intentions to stay connected with football in a different capacity.

    “As I now step into life beyond the pitch, I carry that belief with me – not as a player anymore, but as someone committed to building a stronger pathway and ecosystem for the next generation. My second half is about giving back to the game that gave me everything,” Aditi said.

    After Aditi Chauhan paved the way for India’s women footballers in Europe, Bala Devi became the first outfield player in Europe after joining Scottish outfit Rangers in 2020.

    India recently qualified for AFC Women’s Asian Cup 2026 after 22 years after winning its qualification group.

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  • Scientists found a 100 million-year-old ‘zombie fungus’ preserved in amber

    Scientists found a 100 million-year-old ‘zombie fungus’ preserved in amber

    From being the main antagonist of a major video game and now television franchise, zombie fungus like cordyceps have quickly become a popular point of scientific intrigue. However, these interesting mushrooms and fungi have lived far longer than you might expect. In fact, a newly discovered piece of amber shows that the “zombie fungi” actually lived over 100 million years ago.

    Beyond being a plot device in “The Last of Us,” cordyceps has also proven to be a possible tool in the fight to cure cancer. Beyond that, though, discovering a new strain of almost 100 million-year-old zombie fungus preserved in amber is exciting for a number of reasons. The newly described fungi, Paleoophiocordyceps gerontoformicae and Paleoophiocordyceps ironomyiae, look to have operated very similar to their modern entomopathogenic relatives.

    Much like the zombie fungi that can be found today, these new fungi sprout stems from their dead hosts, allowing them to infect other animals and insects as they come close to it. The newly discovered hunk of amber shows an ant pupa, which died and then sprouted a slender fungal stem. The amber also contains a fly, which has been pierced by a second type of fungus: a projectile-like stroma.

    This discovery is exciting because it shows that even 100 million years ago, zombie fungi like this were found throughout the world. Based on the count of the stalks, as well as the arrangement found within the fungus, it appears they can be tied to the modern Ophiocordyceps family, despite the fact that the lineage for the genus split more than 130 million years ago.

    Of course, we all know that amber is a bit of a time capsule for ancient days. It even starred as a primary source of DNA for researchers in the fictional “Jurassic Park,” which has spurred on a franchise of multiple movies, video games, and even television shows. Of course, the chances of actually pulling working DNA from amber is very slim, but it’s still cool to be able to look back in time and see a 100 million-year-old zombie fungus frozen in time.

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  • Jabeur announces break from tennis ‘to put myself first’

    Jabeur announces break from tennis ‘to put myself first’

    Three-time Grand Slam finalist Ons Jabeur says she is stepping away from professional tennis — for now. The former World No. 2 announced Thursday that she is taking a break from the Hologic WTA Tour, after nearly two years of playing through injuries, to “finally put [herself] first.”

    “I’ve been pushing myself so hard, fighting through injuries and facing many other challenges,” Jabeur wrote in a statement posted to social media. “But deep down, I haven’t truly felt happy on the court for some time now.”

    “Tennis is such a beautiful sport. But right now, I feel it’s time to take a step back … to breathe, to heal, and to rediscover the joy of simply living,” Jabeur continued.

    Since reaching her career peak — historic for a woman from an Arab nation — Jabeur has struggled with a myriad of physical problems. Last year, the fan favorite shut down her season in September due to a shoulder problem. A calf injury first flared up in 2023, and returned at the Miami Open in March — where she was forced to retire when leading Jasmine Paolini.

    Her last match ended abruptly in retirement, too — when trailing Viktoriya Tomova in the first round of Wimbledon, a tournament she reached the final at twice. 

    “I’m really sad. It doesn’t really help me with my confidence and what I keep pushing myself to do, even though it was a very tough season for me. So I hope I’m going to feel better and we’ll see what’s going to happen,” Jabeur said after her SW19 effort ended this year.

    Currently ranked No. 71, the 30-year-old’s most recent title came in Ningbo, China in September 2023. 

    A popular figure in the locker room, Jabeur’s announcement was met with a flood of support from her peers, including love from Mirra Andreeva and recent Wimbledon finalist Amanda Anisimova.

    “We love you,” Anisimova wrote to the three-time reigning Karen Krantzcke Sportsmanship Award winner.

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