Jeremy Henrichs

Just wrapped up Rainer Mühlhoff‘s “Künstliche Intelligenz und der neue Faschismus“ during my flight to Riyadh - and I’m convinced more than ever that this is a must-read for anyone designing, regulating, or depending on AI systems. Rainer Mühlhoff is a Professor at the University of Osnabrück (Universität Osnabrück), Germany, holding the chair in Ethics of Artificial Intelligence, one of the first in Germany explicitly dedicated to this interdisciplinary field. In his new book he argues that AI has become a fertile terrain for authoritarian and fascistic tendencies. Mühlhoff does not use “fascism” in the sense of 20th-century historical regimes, but rather as a concept adapted to our present and future: a political form characterized by anti-democratic behavior, readiness to use force, and the deployment of technology as a power instrument. A central theme of the book is how “Tech Ideologies”, especially those originating in Silicon Valley, portray AI as a kind of salvation: an all-powerful tool that can emulate or even surpass human capacities. This narrative is deeply misleading and dangerous, especially when it merges with right-wing political agendas. Mühlhoff also highlights that the myth of neutrality around AI is inflated. People provide enormous amounts of data through everyday digital usage, enabling companies and political actors to amass power. Meanwhile, the promises made around AI - efficiency, miracles, objectivity - often obscure its ideological framing and possible misuse. He warns that AI is already embedded in many everyday systems: surveillance, scoring, administrative decisions, employment decisions, etc. This widespread use makes regulation not just advisable, but urgent. To guard against the misuse of AI, Mühlhoff calls for democratic design of technology not letting tech elites alone define what AI should do. He pushes for legal and political frameworks that ensure transparency, accountability, and fairness In my role as Chief Medical Officer at GE HealthCare, I see firsthand how AI is transforming diagnostics, imaging, workflow, and decision support promising tremendous improvement, but also with serious risks if deployed without careful design. Finetuning models, verifying fairness, enabling explainable decision pathways, and integrating human oversight are more than just best practice, they are responsibilities. This book reinforces that AI governance isn’t something you add after deployment. It must be thought through from the outset: in design, in data, in testing, in regulation, in monitoring. What we build in healthcare has downstream effects that are deeply human. If you’re working in AI (especially in regulated industries like healthcare), this book gives you language and frameworks to challenge claims of “just more efficiency”, ask who is served / who is marginalized by predictive systems and push for ethical guardrails now. #AI #Ethics #Healthcare #digitalhealth

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🚨 New “foundation model” for reasoning on text and time series by Stanford Biodesign Digital Health, Google and ETH Zürich. Spoiler alert: OpenTSLM lacks evidence to support its claims, does not show evidence of working beyond a narrow problem set, and is not more accurate than classical models. 👉 OpenTSLM is marketed as a breakthrough multimodal foundation model for temporal reasoning with order-of-magnitude gains in medical reasoning and beyond. ‼️But... no evidence to support those claims is to be found. The paper overstates results, omits key literature, and cherry-picks baselines, falling short of minimal standards for methodological rigor and transparency. Here are 🔟 points that I find problematic and share in the spirit of academic exchange. ⚠️ Core concerns: 1️⃣ Misleading framing: Tasks are mostly templated classification, not real temporal reasoning. No forecasting, imputation, or anomaly detection. 2️⃣ False claims: “order-of-magnitude gains in temporal reasoning” is simply false and misleading. 3️⃣ Missing literature: Complete omission of related work. E.g. Chronos by Amazon , TimesFM by Google, Moirai by Salesforce, Toto by Datadog, TabPFN by Prior Labs, TiRex by NXAI, Moment by Carnegie Mellon University... 4️⃣ Weak baselines (cherry-picking): self constructed datasets and omission of standard baselines. (E.g., Sleep-EDF results are far below models from the last decade) 5️⃣ Template dependence — Models were trained to solve one very particular task and don't show any signs of generalization. 6️⃣ Compute inefficiency — Enormous complexity and compute for subpar results. 7️⃣ No forecasting, classification or anomaly detection: Using ChatGPT-style captioning is not forecasting. The authors do include code to handle the Monash Dataset and The UCR Archive but completely omit any reference in the paper. 8️⃣ Missing results, logs, checkpoints, and relevant ablations — No breakdown of curriculum, rationale, or encoder impact. 9️⃣ Code irregularities — >122 files deleted before publishing; including evaluation notebooks and dataset generation script. Also the code history shows that some standard benchmarks (e.g. PAMAP2) were introduced, presumably evaluated and then replaced. 🔟 Suboptimal quality — The paper is signed, endorsed, and shared by researchers from from top institutions. Such an amazing set of authors can and has to produce better work. 🔍 Transparency, quality, and rigor matter — especially in fields like healthcare. 🎖️ If we want trustworthy foundation models we need to raise the bar, not lower. Patrick Langer, Thomas Kaar, Max Rosenblattl, Paul Schmiedmayer, Filipe Barata Welcome to the time series field, and #happyforecasting. 💬 Thoughts, evidence, or corrections welcome. If I’ve missed something, please point it out — I’ll happily adjust or retract. #AI #MachineLearning #TimeSeries #FoundationModels #Transparency #MLResearch #AcademicIntegrity

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Sartiq, a Swiss Vertical AI company formerly known as Shootify closed a CHF 2.1 pre-seed funding round. The funding round was led by Founderful and included participation from Venture Kick, alongside a follow-on investment from original backer Magic Mind, the vertical AI investment vehicle of Zest and Compagnia di Sanpaolo. This investment will allow Sartiq to develop a content engine allowing fashion brands to generate entire visual campaigns, from early-stage lookbooks to final e-commerce assets, with unprecedented speed and creative control. Sartiq has already given tremendous results, from luxury fashion houses and high-volume digital retailers in Italy and Switzerland and is currently in pilot with leading European fashion houses including Guess, OVS Group, Missoni, MooseKnuckles and more. Luca Ambrosini, Andrea Ravano, Felipe Cardoso Sartiq Alex Stöckl Founderful Gabriele Ronchini #sahion #retail #GenAI More about the company from Ticino in our article: https://lnkd.in/ed7iCyzt

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