Paul Hünermund reposted
Who's Who of AI
Paul Hünermund
Why they matter
Researcher with public evidence across AI research, AI business.
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Professor of Empirical Economics & Data Science at TU Munich | Heilbronn Data Science Center (HDSC) | Co-founder of causalscience.org
What they're sharing
We Must Act Now wemustactnow.ai
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What Investment Data Implies about the AI Transition nber.org
eytanadar.medium.com
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Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale arxiv.org
Refine partners with the American Economic Association and the Econometric Society refine.ink
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economist.com
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cuda.ml 0.4.0: GPU-accelerated machine learning from R opensource.posit.co
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Articles & links
Paul Hünermund reposted
I was one of the economists who signed this statement on "AI’s Transformation of the Economy” - whatever your view about AI, we should try to understand the economic impacts and figure out how to make sure it benefits society. wemustactnow.ai
According to a new NBER paper, to justify the AI investment surge we're seeing, the implied productivity gains need to be enormous. The authors calibrate a model where AI-sector productivity rises by roughly 2.7x. Link: www.nber.org/papers/w35290
Paul Hünermund reposted
Nice post from @eytan.adar.prof on the cargo cult science of "AI-native" PhD students, who come in super productive because they perform all major parts of research with genAI. No one needs all these quick papers, and they can really hurt students down the road. eytanadar.medi…
With large numbers of p's you don't need randomization. Actually makes more sense than the Rabois version, but be aware: arxiv.org/abs/2108.11294
Paul Hünermund reposted
Pretty big break happening in academia between the “AI is banned for reviews” journals and the “AI is mandatory for reviews” journals. www.refine.ink/blog/economi...
Paul Hünermund reposted
A gift link if someone is interested: www.economist.com/finance-and-...
Paul Hünermund reposted
Announcing cuda.ml 0.4.0: GPU-accelerated machine learning in #RStats! Bring #NVIDIA compute into tidymodels & parsnip workflows with 2 commands. • Accelerated XGBoost & LightGBM inference via nvForest • Portable model persistence Read more: opensource.posit.co/blog/2026-08...
Their own posts
Recent commentary
New college reality: Students have to write in-person exams that their instructors have created using AI
Interested in spending up to a year in Heilbronn and experiencing our emerging AI ecosystem first-hand? Our new AI Fellowship is now open (link below)! If you have a strong PhD from a leading university and work closely on causal AI, get in touch. I’d be happy to explore hosting you at TUM.
Enjoying this brief window where generative AI has made me dramatically more productive, but performance expectations haven't caught up yet. 😇
The current AI buildout only makes sense if it delivers massive productivity gains. The biggest US tech firms are on track to spend ~$755B on AI capex in 2026, up from $155B in 2022. At this point, AI is not just a tech story. It's a macro bet.
🧵1/4 📢 Call for submissions: Causal Data Science Meeting 2026 Join researchers and practitioners from academia and industry for a virtual meeting on Nov 4–5, 2026, exploring the role of causality in machine learning and AI. #CDSM2026
📢 The Causal Data Science Meeting 2026 is back! Join us virtually on Nov 4–5 for two days of discussion on #causality in ML & AI, with Teppo Felin as our first keynote speaker. No fees, no proceedings—just research, feedback & exchange. 📅 Submit by Sept 30 ✉️ [email protected]
I tried Positron AI in RStudio today and it was amazing
Man, due to LLMs referee reports are becoming longer and longer 😫
It's funny how, just a few years ago, a random cold email could easily cost you 10 minutes of your day. How did you all deal with that before LLMs?
Letting the AI work and working out in the meantime
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