Why AI chatbots lie to us | Science
https://www.science.org/doi/10.1126/science.aea3922
https://www.science.org/doi/10.1126/science.aea3922
Science
Why AI chatbots lie to us
A few weeks ago, a colleague of mine needed to collect and format some data from a website, and he asked the latest version of Anthropic’s generative AI system, Claude, for help. Claude cheerfully agreed to perform the task, generated a computer program ...
This playlist contains all keynotes from IC2S2'25 in Norrköping, Sweden.
https://youtube.com/playlist?list=PLrDB6riLfdJQaATZksFnXsWflA2cea9We&si=JfZHBovx27npBd5t
https://youtube.com/playlist?list=PLrDB6riLfdJQaATZksFnXsWflA2cea9We&si=JfZHBovx27npBd5t
YouTube
IC2S2'25 Norrköping
This playlist contains all keynotes from IC2S2'25 in Norrköping, Sweden.
Integrating explanation and prediction in computational social science
https://youtu.be/c7BB5Svd8aw?list=PLrDB6riLfdJQaATZksFnXsWflA2cea9We
Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions—the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes—and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.
https://www.nature.com/articles/s41586-021-03659-0
https://youtu.be/c7BB5Svd8aw?list=PLrDB6riLfdJQaATZksFnXsWflA2cea9We
Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions—the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes—and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.
https://www.nature.com/articles/s41586-021-03659-0
YouTube
Duncan Watts: Integrating explanation & prediction in CSS — IC2S2 2025 Keynote
Abstract:
Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyze them. It also represents a convergence of different fields with different ways of thinking about and…
Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyze them. It also represents a convergence of different fields with different ways of thinking about and…
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#PhD studentships in real-time infectious disease modelling
https://www.lshtm.ac.uk/study/fees-and-funding/funding-scholarships/research-degree-funding/phd-studentships-real-time-infectious-disease-modelling
https://www.lshtm.ac.uk/study/fees-and-funding/funding-scholarships/research-degree-funding/phd-studentships-real-time-infectious-disease-modelling
LSHTM
PhD studentships in real-time infectious disease modelling | LSHTM
The London School of Hygiene & Tropical Medicine (LSHTM), Imperial College London and the UK Health Security Agency (UKHSA) are pleased to invite applications for two PhD studentships in real-time
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Human Mobility in Epidemic Modeling
Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to capture the complex and heterogeneous nature of real-world human interactions. Recent advancements in data-driven methodologies and computational capabilities have unlocked the potential of integrating high-resolution human mobility data into epidemic modeling, significantly improving the accuracy, timeliness, and applicability of epidemic risk assessment, contact tracing, and intervention strategies. This review provides a comprehensive synthesis of the current landscape in human mobility-informed epidemic modeling. We explore diverse sources and representations of human mobility data, and then examine the behavioral and structural roles of mobility and contact in shaping disease transmission dynamics. Furthermore, the review spans a wide range of epidemic modeling approaches, ranging from classical compartmental models to network-based, agent-based, and machine learning models. And we also discuss how mobility integration enhances risk management and response strategies during epidemics. By synthesizing these insights, the review can serve as a foundational resource for researchers and practitioners, bridging the gap between epidemiological theory and the dynamic complexities of human interaction while charting clear directions for future research.
https://www.arxiv.org/abs/2507.22799
Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional models are often based on the assumption that all individuals have the same probability of infecting every other individual in the population, a so-called random homogeneous mixing, they struggle to capture the complex and heterogeneous nature of real-world human interactions. Recent advancements in data-driven methodologies and computational capabilities have unlocked the potential of integrating high-resolution human mobility data into epidemic modeling, significantly improving the accuracy, timeliness, and applicability of epidemic risk assessment, contact tracing, and intervention strategies. This review provides a comprehensive synthesis of the current landscape in human mobility-informed epidemic modeling. We explore diverse sources and representations of human mobility data, and then examine the behavioral and structural roles of mobility and contact in shaping disease transmission dynamics. Furthermore, the review spans a wide range of epidemic modeling approaches, ranging from classical compartmental models to network-based, agent-based, and machine learning models. And we also discuss how mobility integration enhances risk management and response strategies during epidemics. By synthesizing these insights, the review can serve as a foundational resource for researchers and practitioners, bridging the gap between epidemiological theory and the dynamic complexities of human interaction while charting clear directions for future research.
https://www.arxiv.org/abs/2507.22799
arXiv.org
Human Mobility in Epidemic Modeling
Human mobility forms the backbone of contact patterns through which infectious diseases propagate, fundamentally shaping the spatio-temporal dynamics of epidemics and pandemics. While traditional...
Estimated fraction of LLM-modified sentences across research paper venues over time.
https://www.nature.com/articles/s41562-025-02273-8
https://www.nature.com/articles/s41562-025-02273-8
Optimistic people are all alike: Shared neural representations supporting episodic future thinking among optimistic individuals
https://www.pnas.org/doi/10.1073/pnas.2511101122
Neural processing of cognitive function is similar among individuals with positive traits but more dissimilar among those with negative traits. Applying the cross-subject neural representational analytical approach, we found that optimistic individuals display similar neural processing when imagining the future, whereas less optimistic individuals show idiosyncratic differences. Additionally, we found that optimistic individuals imagined positive events as more distinct from negative events than less optimistic individuals.
Findings derived from a combination of IS-RSA and INDSCAL, suggest the existence of shared neurocognitive representations based on the emotional dimension among optimistic individuals, despite the fact that different individuals may perceive the same future event differently.
https://www.pnas.org/doi/10.1073/pnas.2511101122
Neural processing of cognitive function is similar among individuals with positive traits but more dissimilar among those with negative traits. Applying the cross-subject neural representational analytical approach, we found that optimistic individuals display similar neural processing when imagining the future, whereas less optimistic individuals show idiosyncratic differences. Additionally, we found that optimistic individuals imagined positive events as more distinct from negative events than less optimistic individuals.
Findings derived from a combination of IS-RSA and INDSCAL, suggest the existence of shared neurocognitive representations based on the emotional dimension among optimistic individuals, despite the fact that different individuals may perceive the same future event differently.
PNAS
Optimistic people are all alike: Shared neural representations supporting episodic future thinking among optimistic individuals
Optimism is a critical personality trait that influences future-oriented cognition by emphasizing positive future outcomes and deemphasizing negati...
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Elements of successful NIH grant applications
https://www.pnas.org/doi/10.1073/pnas.2315735121
5 postulates for Successful Applications 101:
1. The application is for the reviewer, not you, the applicant—remember that.
2. Learn from the Greek—communicate in stories.
3. Your Specific Aims story needs to be cohesive—leave no puzzling gaps.
4. Motivate the reviewer to keep reading—make your story resonate.
5. There is serendipity and noise in the peer-review system—accept that.
https://www.pnas.org/doi/10.1073/pnas.2315735121
5 postulates for Successful Applications 101:
1. The application is for the reviewer, not you, the applicant—remember that.
2. Learn from the Greek—communicate in stories.
3. Your Specific Aims story needs to be cohesive—leave no puzzling gaps.
4. Motivate the reviewer to keep reading—make your story resonate.
5. There is serendipity and noise in the peer-review system—accept that.
PNAS
Elements of successful NIH grant applications
Is there a formula for a competitive NIH grant application? The Serenity Prayer may provide one: "Grant me the serenity to accept the things I cann...
#Postdoc (Bioinformatics/Data Science) in data-driven protein-protein interaction research at Department of Drug Design and Pharmacology
https://jobportal.ku.dk/videnskabelige-stillinger/?show=164645
https://jobportal.ku.dk/videnskabelige-stillinger/?show=164645
jobportal.ku.dk
Videnskabelige stillinger
#Postdoc Research Assistant in Machine Learning
Statistics, 24-29 St Giles’, Oxford, OX1 3LB
We invite applications for a full-time Postdoctoral Research Associate to join the new Data-Driven Algorithms for Data Acquisition (DataAcq) project. This is a timely project developing new methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon Guarantee for an ERC Starting Grant awarded to Prof Tom Rainforth.
The post holder will undertake innovative research as part of the RainML Lab (https://www.rainml.uk/) towards the goals of the DataAcq project. In particular, the post holder will be expected to undertake research related to one or more of the three work packages of the project: a) scalable and robust Bayesian experimental design, b) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models).
The successful postholder will hold or be close to the completion of a PhD/DPhil in Machine Learning, Statistics, Computer Science or closely related discipline. They will demonstrate an ability to publish, including the ability to produce high-quality academic writing. They will have the ability to contribute ideas for new research projects and research income generation. Previous research experience in one or more areas relevant to the research programme. For example: probabilistic machine learning, deep learning, experimental design, active learning, generative modelling, computational statistics, reinforcement learning, or Bayesian optimisation. This must include the ability to develop and/or analyse new methodology. Proficiency in the use of PyTorch, Tensorflow, Jax, or an equivalent deep learning library is desirable.
We proudly hold a Race Equality Charter Bronze Award and a departmental Athena SWAN Silver Award, which guide our progress towards advancing racial and gender equality. Applicants will be selected for interview purely based on their ability to satisfy the selection criteria as outlined in full in the job description. You will be required to upload a statement setting out how you meet the selection criteria, a curriculum vitae, and the contact details of two referees as part of your online application. Please note that applicants are responsible for contacting their referees and making sure that their letters are sent to [email protected] directly by the closing date.
Please direct informal enquiries about the post to Professor Tom Rainforth [email protected], quoting vacancy reference 181060.
Only applications received before 12.00 noon UK time on 03 September 2025 can be considered. Interviews are anticipated to be held on 24 September 2025.
Link: https://www.jobs.ac.uk/job/DOC113/postdoctoral-research-assistant-in-machine-learning
Statistics, 24-29 St Giles’, Oxford, OX1 3LB
We invite applications for a full-time Postdoctoral Research Associate to join the new Data-Driven Algorithms for Data Acquisition (DataAcq) project. This is a timely project developing new methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon Guarantee for an ERC Starting Grant awarded to Prof Tom Rainforth.
The post holder will undertake innovative research as part of the RainML Lab (https://www.rainml.uk/) towards the goals of the DataAcq project. In particular, the post holder will be expected to undertake research related to one or more of the three work packages of the project: a) scalable and robust Bayesian experimental design, b) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models).
The successful postholder will hold or be close to the completion of a PhD/DPhil in Machine Learning, Statistics, Computer Science or closely related discipline. They will demonstrate an ability to publish, including the ability to produce high-quality academic writing. They will have the ability to contribute ideas for new research projects and research income generation. Previous research experience in one or more areas relevant to the research programme. For example: probabilistic machine learning, deep learning, experimental design, active learning, generative modelling, computational statistics, reinforcement learning, or Bayesian optimisation. This must include the ability to develop and/or analyse new methodology. Proficiency in the use of PyTorch, Tensorflow, Jax, or an equivalent deep learning library is desirable.
We proudly hold a Race Equality Charter Bronze Award and a departmental Athena SWAN Silver Award, which guide our progress towards advancing racial and gender equality. Applicants will be selected for interview purely based on their ability to satisfy the selection criteria as outlined in full in the job description. You will be required to upload a statement setting out how you meet the selection criteria, a curriculum vitae, and the contact details of two referees as part of your online application. Please note that applicants are responsible for contacting their referees and making sure that their letters are sent to [email protected] directly by the closing date.
Please direct informal enquiries about the post to Professor Tom Rainforth [email protected], quoting vacancy reference 181060.
Only applications received before 12.00 noon UK time on 03 September 2025 can be considered. Interviews are anticipated to be held on 24 September 2025.
Link: https://www.jobs.ac.uk/job/DOC113/postdoctoral-research-assistant-in-machine-learning
Jobs.ac.uk
Postdoctoral Research Assistant in Machine Learning at University of Oxford
Discover an exciting academic career path as a Postdoctoral Research Assistant in Machine Learning at jobs.ac.uk. Don't miss out on this job opportunity - apply today!
Forwarded from Sitpor.org سیتپـــــور
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منظور ما از پدیدارگی یا emergence در سیستمهای پیچیده چیه؟!
انگاره پیچیدگی عینک جدیدی برای مطالعه طبیعت به ما میدهد. سیستمهای پیچیده از تعداد زیادی اجزا تشکیل شدهاند که در مقیاس ریز، اجزایشان برهمکنشهای موضعی دارند و در مقیاس درشت، رفتارهای «پدیداره» از خود نشان میدهند که شبیه به رفتار اجزای آنها در مقیاس ریز نیست. پدیدارگی در مورد این جور پدیدههاست.
این ویدیو دعوتی است برای خواندن این مقاله مروری کوتاه:
What is emergence, after all?
🔗 https://arxiv.org/abs/2507.04951
🎞 https://youtu.be/fMyuRjgFu-I
🎧 Audio File
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@sitpor | sitpor.org
instagram.com/sitpor_media
انگاره پیچیدگی عینک جدیدی برای مطالعه طبیعت به ما میدهد. سیستمهای پیچیده از تعداد زیادی اجزا تشکیل شدهاند که در مقیاس ریز، اجزایشان برهمکنشهای موضعی دارند و در مقیاس درشت، رفتارهای «پدیداره» از خود نشان میدهند که شبیه به رفتار اجزای آنها در مقیاس ریز نیست. پدیدارگی در مورد این جور پدیدههاست.
این ویدیو دعوتی است برای خواندن این مقاله مروری کوتاه:
What is emergence, after all?
🔗 https://arxiv.org/abs/2507.04951
🎞 https://youtu.be/fMyuRjgFu-I
🎧 Audio File
----------------------------------------------
@sitpor | sitpor.org
instagram.com/sitpor_media
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The Era of Experience & The Age of Design: Richard S. Sutton, Upper Bound 2025
https://youtu.be/FLOL2f4iHKA
Welcome to the era of experience
https://storage.googleapis.com/deepmind-media/Era-of-Experience%20/The%20Era%20of%20Experience%20Paper.pdf
https://youtu.be/FLOL2f4iHKA
Welcome to the era of experience
https://storage.googleapis.com/deepmind-media/Era-of-Experience%20/The%20Era%20of%20Experience%20Paper.pdf
YouTube
The Era of Experience & The Age of Design: Richard S. Sutton, Upper Bound 2025
In his first large-scale public presentation after receiving the Turing Award, Dr. Richard S. Sutton presents, "The Era of Experience & The Age of Design," recorded live at Upper Bound.
In this presentation, Dr. Sutton lays the groundwork and shares a few…
In this presentation, Dr. Sutton lays the groundwork and shares a few…
We are hiring multiple #PhD and #postdoc researchers for two newly funded projects related to the interaction of mental health and political polarization. The positions are in the Department of Computer Science at Aalto University in Finland. You will be joining a larger group of researchers working on similar topics. The department has a strong community of researchers working on related topics, including digital health and welbeing, network science, computational social science, and many topics in machine learning.
https://www.aalto.fi/en/open-positions/open-postdoctoral-and-doctoral-positions-to-work-on-polarization-and-mental-health
https://www.aalto.fi/en/open-positions/open-postdoctoral-and-doctoral-positions-to-work-on-polarization-and-mental-health
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CSS, recognize the genocide in Gaza!
This is a call to all members of the Complex Systems Society.
We ask the CSS to join other academic organisations in publicly condemning the ongoing genocide in Gaza.
We ask you to sign the letter below in support of the initiative.
https://docs.google.com/forms/d/e/1FAIpQLSc9yqD3JufuaNBJQkg7q0KsYqnUSpYnRVprbb8E-V7rV7To2A/viewform
This is a call to all members of the Complex Systems Society.
We ask the CSS to join other academic organisations in publicly condemning the ongoing genocide in Gaza.
We ask you to sign the letter below in support of the initiative.
https://docs.google.com/forms/d/e/1FAIpQLSc9yqD3JufuaNBJQkg7q0KsYqnUSpYnRVprbb8E-V7rV7To2A/viewform
Google Docs
CSS, recognize the genocide in Gaza!
This is a call to all members of the Complex Systems Society.
We ask the CSS to join other academic organisations in publicly condemning the ongoing genocide in Gaza.
We ask you to sign the letter below in support of the initiative.
29th August 2025
Dear…
We ask the CSS to join other academic organisations in publicly condemning the ongoing genocide in Gaza.
We ask you to sign the letter below in support of the initiative.
29th August 2025
Dear…
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#Postdoc Positions in Mathematics at the Department of Mathematical Sciences KU
https://candidate.hr-manager.net/ApplicationInit.aspx/?cid=1307&departmentId=18973&ProjectId=164797&MediaId=5&SkipAdvertisement=false
https://candidate.hr-manager.net/ApplicationInit.aspx/?cid=1307&departmentId=18973&ProjectId=164797&MediaId=5&SkipAdvertisement=false
Talentech
Postdoctoral Positions in Mathematics at the Department of Mathematical Sciences
The Department of Mathematical Sciences at the University of Copenhagen is seeking top early-career researchers for a number of attractive one- to three-year po
Some thoughts on Al and infectious diseases as we prepare for the next pandemic
https://sciencenews.dk/en/artificial-intelligence-could-be-our-greatest-ally-in-the-next-pandemic
https://sciencenews.dk/en/artificial-intelligence-could-be-our-greatest-ally-in-the-next-pandemic
Science News
Artificial intelligence could be our greatest ally in the next...
Artificial intelligence (AI) will undoubtedly have a key role in how pandemics are managed in the future. Now, researchers have created a concrete plan for how the technology should be developed...