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Scientist, Entrepreneur, Investor: Digital Economy ¦ Applied AI ¦ Network Science ¦ Data…
Articles by Sinan
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The Economic Fallout from COVID-19
The Economic Fallout from COVID-19
Today the White House's top economist Robert Ludlow said the "fundamentals of the economy are strong." This idea…
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The COVID-19 Perception Problem
The COVID-19 Perception Problem
During the most recent COVID Task Force Briefing today, Dr. Anthony Fauci noted a key problem for our coronavirus…
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"Social Distancing" and Mental Health
"Social Distancing" and Mental Health
Over the last 72 hours, a large fraction of the world has isolated itself in an effort at "social distancing" to…
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A COVID-19 Behavior Change Primer
A COVID-19 Behavior Change Primer
As the incoming director of a large research center (the MIT IDE), the general chair of a large international…
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Sinan Aral reposted thisSinan Aral reposted thisPioneers of AI host Rana el Kaliouby, Ph.D. joined Sinan Aral at the MIT Media Lab Advancing Humans with AI Symposium to discuss the current state of AI and make the case for human-centric tech. She also shares her concerns about a male-dominated startup landscape, explains why AI needs EQ, and more. Watch the full conversation here: https://lnkd.in/e3cE46f4
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Sinan Aral shared thisSuper proud to have watched these students in action!Sinan Aral shared thisBPHC engaged with a team of students taking MIT IDE’s Analytics Lab (A-Lab) to explore how a wide range of non-traditional data such as weather metrics, unemployment statistics, and rental patterns can create a predictive model to forecast demand for public health services: https://lnkd.in/ebrrExrD (Evan Hoch, Jacob Lebovitz, Jérémie Taranto, Jiao Zhao, W.W. Sanouri Ursprung, Sinan Aral, Abdullah Almaatouq, Albert Scerbo, MIT Initiative on the Digital Economy, MIT Sloan School of Management Admissions)
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Sinan Aral reposted thisSinan Aral reposted thisGreat to chat with Sinan Aral last week about the future of AI and its implications for business and society. One of my favourite parts of the conversation was when we talked about how different AI personalities can have a big impact on how well different humans work with them. And it seems like the bigger picture trend across all of his work is that working with AI is very different for everyone, so we're going to have to be very deliberate about how we use it (and how we get other people to use it). Link to the fuller interview in the comments.
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Sinan Aral reposted thisSinan Aral reposted thisSon günlerde okuduğum akademik yayınlar net bir şekilde şunu söylüyor: Yapay zeka kısa vadede üretkenliğimizi artırırken, uzun vadede uzmanlığımızı yok ediyor... Son çalışma Michael Coasun ve Prof. Sinan Aral Hoca'nın kaleminden. Araştırma, yapay zekaya aşırı güvenmenin çalışanların uzmanlığını körelterek uzun vadede performansı düşürdüğünü ortaya koymaktadır. Bu performans düşüklüğünün sebep olduğu körlüğe ise "sezgi paslanması" (intuition rust) adını vermişler. Makalede, kanser uzmanları üzerinde yapılan bir yıllık araştırma ile de sonuçları ampirik olarak gözlemlemişler. Peki bilişsel yetkinliklerimizi kaybetmemek için ne yapmalı? Örneğin, bir hukuk dosyasını yapay zeka ile analiz öncesinde kendin analiz et ve daha sonra yapay zeka çıktıları ile karşılaştır. Aynı şekilde yazılımcılar, kod yazarken önce kendileri yazıp sonra yazdıkları kodu yapay zeka çıktısı ile karşılaştırmalılar. Bunu yapmazsak makalenin de başlığı olan "Zenginleştirme Tuzağı"na düşüp uzun vadede birkaç yıl önceki yetkinliklerimizi bile arar hale gelecegiz. Bu çalışmaya yorumdaki linkten ulaşabilirsiniz. #ai #llm #artificialintelligence #cognitive #yapayzeka
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Sinan Aral shared thisThoroughly enjoyed this wide ranging conversation with Jamie Condliffe for Aventine about the Future of AI and its implications for business and society. We covered a lot of ground and you can read it all through the link in the first comment.
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Sinan Aral shared thisAn excellent illustration of our findings with Harang Ju about the science of Human-AI Collaboration, which I'm calling Integrated Intelligence (I^2)... We have to increase the marginal productivity and performance of Human-AI teams above AI alone if humans are to survive the rise of Artificial Intelligence! Paper in the comments below!Sinan Aral shared thisWhen people's teammate at work is an AI agent (vs. another human), people: - Send 25% more task-oriented messages (so focus more on doing the work) - Send 18% less interpersonal messages (so less effort on being social) - Delegate 17% more work (so more comfy with telling others i.e., the ai agent, to do stuff) - Made 62% fewer direct text edits on work (so AI is good at writing) According to findings from a really cool research paper "Collaborating with AI Agents: A Field Experiment on Teamwork, Productivity, and Performance" by Harang Ju (John Hopkins Carey Business School) and Sinan Aral (MIT Sloan School of Management). Harang Ju Sinan Aral Visualisation is mine, using the findings from the paper.
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Sinan Aral shared thisWe're so excited to welcome you to MIT Annika! And we know our colleagues at HBS have prepared you well for the exciting journey ahead!Sinan Aral shared thisI’m so excited to share that I will be joining MIT Sloan School of Management this fall as a PhD student in the Information Technology group! I’m incredibly grateful for my time as a pre-doctoral researcher at Harvard Business School and for the many people who supported me along the way. A huge thank you to Iavor Bojinov and Edward McFowland III for their mentorship over the past two years and for the opportunity to be part of such exciting research. And to Ramona Pop for being the best lab director! I’ve learned so much from the amazing researchers I’ve had the chance to collaborate with. Thank you Matthew DosSantos DiSorbo, Shaolong Wu, Paul Hamilton, Luca Vendraminelli, Michael Menietti, and so many others for your wisdom and support! And to all the research associates I’ve had the chance to work with, it’s been such a joy to be part of this community :). Looking forward to what’s ahead!
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Sinan Aral reposted thisSinan Aral reposted thisIf you couldn’t make it to BIG AI@MIT (or want to revisit the conversations), we’ve shared the full sessions on the MIT Initiative on the Digital Economy YouTube channel. What stood out across these discussions wasn’t just where AI is headed, but how nuanced the path to getting there actually is: • What changes when AI moves from pilot to production • How leaders are thinking about managing—not just deploying—AI • The real tradeoffs between speed, cost, and quality • What breaks (and what doesn’t) when AI gets embedded into workflows • How teams are starting to rethink roles, ownership, and accountability It’s a mix of perspectives from researchers, operators, economists, and builders who are working through this in real time. 👉 Watch the full sessions here: https://lnkd.in/eCYJmPEj
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Sinan Aral reposted thisSinan Aral reposted thisThe companies winning at AI aren't always the ones with the best technology. I moderated a panel at the BIG.AI@MIT conference last week with H. James Wilson, Nicole Immorlica, Ernie Tedeschi, and Julia Neagu on how AI is reshaping work and organizations. And the message was clear. The real bottleneck is now not the model; it's everything around it. Three things that stood out to me. 1. Most companies try to automate their existing process. The successful adopters are redesigning the process entirely to do things that weren't possible before. 2. AI investment has a J-curve. Productivity dips first because the real work is organizational change: redesigning processes, rewriting job descriptions, curating data. That dip is not failure but an investment, and the companies that commit through the dip will pull ahead. 3. As AI gets more capable, the human complements become more valuable, not less. For every $1 on AI tech, expect $7-8 on human complements. Tacit knowledge. Context-switching. Judgment calls that don't exist in any dataset. Managing AI the way you'd manage a new hire. The competitive edge isn't the model. It's the people around it. Watch the full panel here: https://lnkd.in/eV2hk3UX We'll be back next year in 2027 with research presentations, fireside chats, and panels. Stay tuned! Thanks to the MIT Initiative on the Digital Economy and my co-organizers Sinan Aral, David Holtz! Albert Scerbo, Carrie Reynolds
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Sinan Aral liked thisSinan Aral liked this- Chips-on-shoulders founders outperform - Invest where capital is scarce - Efficient AI is one of the next big waves Endless Frontier Labs / Deepak — 🔥 fireside
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Sinan Aral liked thisSinan Aral liked thisWrapped up two days of “Leveraging Generative AI for Business” at Carlson Executive Education yesterday with Ravi Bapna and a terrific cohort of senior leaders from Cargill, Ameriprise, Boston Scientific, Adobe, Comcast, and more. The real question in the room was “where are we on the ladder, and how do we climb it fast?” We use a simple framework: Gen AI sophistication runs from basic generation and task completion all the way up to analyze/predict, RAG, fine-tuning, and full agentic workflows. Most companies are stuck in the middle — and don’t realize Gen AI can now do what used to require a dedicated data science team to pull off. But knowing the ladder isn’t enough. Moving up it responsibly means designing robust evals and guardrails before you scale — something most organizations underinvest in until something goes wrong. And ultimately, the real strategic question isn’t just what AI can do — it’s how you redesign work, roles, and processes around it. The harder work is getting internal buy-in, aligning leadership, and turning that vision into a concrete project roadmap your organization can actually execute. That’s where Ravi and I spend a lot of our time — sitting at the intersection of AI research, strategy, and organizational transformation. Above all, while the organizational challenges are significant, we often forget about human challenges. When thinking about Future of Work, human limitations (Metaknowledge) and transformation (Borg effect) need to be kept in mind. Figuring out who and when should humans work with AI is, in itself, a significant challenge and we dealt with issues such as understanding, measuring and deploying organization of work with AI. If your organization is considering any of these issues, we customize this program for individual companies. Contact Carol Roecklein
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Sinan Aral liked thisSinan Aral liked thisI am celebrating 5 years since my book, HOW TO CHANGE, was first published! Translated into over 20 languages, named a best book of the year for healthy living by the The New York Times, featured on Mel Robbins, Freakonomics, Hidden Brain, The School of Greatness, NPR, and on hundreds of other podcasts, radio programs and TV shows... and you made it not only a national but an *international* bestseller. The experience of releasing HOW TO CHANGE into the world was dizzying, but by FAR the best part was hearing from readers -- getting emails from people who told me my book had changed their life or transformed their organization for the better. I've saved every last message and they remind me of why I do the work I do. Huge thanks to my editor Niki P. and Portfolio | Penguin Random House for bringing my book to life and to Rafe Sagalyn for being an amazing agent. If you're reading this post, you've probably already read my book, but just in case you haven't, you can always find a copy here: https://lnkd.in/e_hs95x
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Sinan Aral liked thisSinan Aral liked thisMany thanks for Millenium Capital Management for inviting me to talk about AI and my book, Thinking With Machines (https://lnkd.in/ed_p9QHx), during their "AI Week." And a special thanks to Head of AI for Equities Mike Purewal for being a wonderful interlocutor and to Chelsea Lee and Melita Loncar for organizing the talk. To everyone attended, thank you all, I really enjoyed the session and the Q&A. NYU Stern School of Business Millenium Capital Management
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Sinan Aral liked thisSinan Aral liked thisPioneers of AI host Rana el Kaliouby, Ph.D. joined Sinan Aral at the MIT Media Lab Advancing Humans with AI Symposium to discuss the current state of AI and make the case for human-centric tech. She also shares her concerns about a male-dominated startup landscape, explains why AI needs EQ, and more. Watch the full conversation here: https://lnkd.in/e3cE46f4
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Sinan Aral liked thisSinan Aral liked thisBPHC engaged with a team of students taking MIT IDE’s Analytics Lab (A-Lab) to explore how a wide range of non-traditional data such as weather metrics, unemployment statistics, and rental patterns can create a predictive model to forecast demand for public health services: https://lnkd.in/ebrrExrD (Evan Hoch, Jacob Lebovitz, Jérémie Taranto, Jiao Zhao, W.W. Sanouri Ursprung, Sinan Aral, Abdullah Almaatouq, Albert Scerbo, MIT Initiative on the Digital Economy, MIT Sloan School of Management Admissions)
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Sinan Aral liked thisCenter for Digital Health and Artificial Intelligence at Johns Hopkins (CDHAI)
Center for Digital Health and Artificial Intelligence at Johns Hopkins (CDHAI)
Sinan Aral liked thisJoin us next week for the 16th Annual Conference on Health IT and Analytics (CHITA) 2026 at Johns Hopkins! We’re looking forward to bringing together researchers, clinicians, policymakers, and industry leaders to explore how we can build an AI-ready healthcare ecosystem—from cutting-edge research to real-world implementation. 📍 Washington, DC 📅 May 8–9, 2026 This year’s program features an incredible lineup of keynote speakers, panels, and research presentations. A special thank you to our keynote speakers, including Jesse Isaacman-Beck, Ph.D., Anupam B. Jena, as well as our industry voices Ed Lee, MD, MPH and Tej Shah, M.D.. We’re also grateful for the leadership of our program chairs, Atiye Cansu Erol, Weiguang Wang and our conference co-chairs Ritu Agarwal, Guodong (Gordon) Gao, Jeffrey McCullough, Indranil Bardhan and the many contributors who made this year’s conference possible. If you haven’t registered yet, there’s still time—we hope to see you there! https://lnkd.in/eYsmACSB #CHITA2026 #HealthIT #AIinHealthcare #DigitalHealth #ResponsibleAI #HealthcareInnovation
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Ryan Wright
Yael Grushka-Cockayne leading an interesting panel on Building AI Infrastructure in Virginia at VirginiaAI Summit hosted by Virginia Academy of Science, Engineering and Medicine. Panelist: Lingjia Liu, Milos Manic, Nathan Mellis, and Manish Parashar. Key takeaways • Virginia is emerging as a national leader in AI infrastructure. • Community and environmental impacts must be designed in from the start. • Cybersecurity is driving real-world AI use cases—and the infrastructure required to support them. #AI #AIInfrastructure #Cybersecurity #Sustainability #Virginia #VirginiaAI
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Roland Rust
Special Issue of Production and Operations Management on "Generative AI (GenAI) and Agentic AI at the Marketing–Operations Interface." Guest edited by Roland Rust, Ming-Hui Huang, Shane Wang, Praveen Kopalle. Commitment to Timeliness Given the rapid pace of research and practice in this domain, the editorial team is committed to quick turnaround and timely decisions. A paper submitted to the special issue will be processed right away. Authors are encouraged to submit as soon as they are ready. Our goal is to ensure that accepted papers are published swiftly, with the special issue scheduled for print publication in early 2027 to maximize its relevance and impact. First-round submission deadline: June 30, 2026 First-round decisions: August 31, 2026 Revised submission deadline: November 15, 2026 Final decisions: December 31, 2026
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Liang Cheng
Seeking a comprehensive, integrated view on Human-Machine Cooperation? Our new paper, recently published in ACM Transactions on Autonomous and Adaptive Systems (TAAS), provides just that. Paper: "A Review of AI in Human-Machine Cooperation: Machine Perspective" We offer a critical analysis of how AI-driven machine agents assess and interact with human partners. Read the full review and let us know where you believe future research in this field should be directed. https://lnkd.in/g9XTyKwi #AI #MachineLearning #HumanMachineCooperation #Research
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Erik Mudrinich
Stanford just published the 2026 Emerging Technology Review, and the space chapter alone is worth your time. Herbert Lin and the team — co-chaired by Condoleezza Rice, Jennifer Widom, and Amy Zegart — lay out the hard questions: OST norms fraying under geopolitical pressure, the U.S. leaning heavily on a small number of commercial launch providers, a new Moon race driven more by strategic advantage than prestige, and a debris problem that keeps getting worse. What struck me most is how clearly the chapter connects the technology trends to the policy gaps we keep talking about but haven't closed. I've been using this in my National Security Space Law course at Nebraska this Spring. If space law, security, or policy is your world, give it a read!
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Frauke Kreuter
Day 3 of the spring break course with a fascinating discussion with David Broska about this paper on The #Mixed #Subjects #Design: Treating Large Language Models as Potentially Informative Observations We discussed - when predicted and observed behavior are and are not interchangeable, - when LLMs could obtain valid estimates of causal effects - when LLM predictions can be treated as informative observations - and how to get to more precise estimates at a lower - and how the prediction-powered inference (PPI) lit mirros so much of survey statistics .... Thank you and all the students. I am very curious to see existing and forthcoming examples using this method.
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