The video can be found at the Lindau Mediathek.
Here is my annotated list of excuses numbered as SUEDHOF1, SUEDHOF2, …,. SUEDHOF15 in chronological order.
Is this really “an unprecedented quality initiative” as F.A.Z. Joachim Müller-Jung wrote?
IMHO this looks more like a larmoyant defense but form your own opinion now. Continue reading The Südhof Nomenclature
One of the best essays that I have read about current science.
Ricky Lanusse. How Peer Review Became Science’s Most Dangerous Illusion. https://medium.com/the-quantastic-journal/how-peer-review-became-sciences-most-dangerous-illusion-54cf13da517c
Peer review is far from a firewall. In most cases, it’s just a paper trail that may have even encouraged bad research. The system we’ve trusted to verify scientific truth is fundamentally unreliable — a lie detector that’s been lying to us all along.
Let’s be bold for a minute: If peer review worked, scientists would act like it mattered. They don’t. When a paper gets rejected, most researchers don’t tear it apart, revise it, rethink it. They just repackage and resubmit — often word-for-word — to another journal. Same lottery ticket in a different draw mindset. Peer review is science roulette.
Once the papers are in, the reviews disappear. Some journals publish them. Most shred them. No one knows what the reviewer said. No one cares. If peer review were actually a quality check, we’d treat those comments like gospel. That’s what I value about RealClimate [PubPeer, my addition]: it provides insights we don’t get to see in formal reviews. Their blog posts and discussions — none of which have been published behind paywalls in journals — often carry more weight than peer-reviewed science.
I asked ChatGPT-4 for more references around the 2024 paper “Unraveling the Enigma of Organismal Death: Insights, Implications, and Unexplored Frontieres” as Tukdam continues to be a hot topic. Here is the updated reading list
1. Organismal Superposition & the Brain‑Death Paradox
Piotr Grzegorz Nowak (2024) argues that defining death as the “termination of the organism” leads to an organismal superposition problem. He suggests that under certain physiological conditions—like brain death—the patient can be argued to be both alive and dead, much like Schrödinger’s cat, creating ethical confusion especially around organ harvesting. https://philpapers.org/rec/NOWOSP
2. Life After Organismal “Death”
Melissa Moschella (2017, revisiting Brain‑Death debates) highlights that even after “organismal death,” significant biological activity persists—cells, tissues, and networks (immune, stress responses) can remain active days postmortem. https://philpapers.org/rec/MOSCOD-2
3. Metaphysical & Ontological Critiques
The Humanum Review and similar critiques challenge the metaphysical basis of the paper’s unity‑based definition of death. They stress that considering a person’s “unity” as automatically tied to brain-function is metaphysically dubious. They also quote John Paul II, arguing death is fundamentally a metaphysical event that science can only confirm empirically. https://philpapers.org/rec/MOSCOD-2
4. Biological Categorization Limits
Additional criticism comes from theoretical biology circles, pointing out that living vs. dead is an inherently fuzzy, non-binary distinction. Any attempt to define death (like in the paper) confronts conceptual limits due to the complexity of life forms and continuous transitions. https://humanumreview.com/articles/revising-the-concept-of-death-again
5. Continuation of Scientific Research
Frontiers in Microbiology (2023) supports the broader approach but emphasizes that transcriptomic and microbiome dynamics postmortem should be more deeply explored, suggesting the paper’s overview was incomplete without enough data-driven follow-up https://pmc.ncbi.nlm.nih.gov/articles/PMC6880069/
Beispiel eines Gödelsatzes „Diese Aussage ist nicht beweisbar“
Wenn der Satz beweisbar wäre, dann wäre die Aussage falsch , also Widerspruch! Wenn der Satz nicht beweisbar ist, dann ist die Aussage wahr, aber nicht beweisbar.
After giving up Nextcloud – which is now overkill for me with 30.000 files of basic setup – I am syncing now using iCloud. As I am working only on the desktop, it would make sense to sync the desktop in regular intervals but unfortunately this can be done only together with the Documents folder (something I don’t want). SE has also no good solution, so here is mine
# make another Desktop in iCloud folder
mkdir -p ~/Library/Mobile\ Documents/com~apple~CloudDocs/iCloudDesktop
# sync local Desktop
rsync -av --delete ~/Desktop/ ~/Library/Mobile\ Documents/com~apple~CloudDocs/iCloudDesktop/
# and run it every hour or so
# launchctl load ~/Library/LaunchAgents/launched.com.desktop.rsync.plist
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>KeepAlive</key>
<dict>
<key>Crashed</key>
<true/>
</dict>
<key>Label</key>
<string>launched.com.desktop.rsync</string>
<key>ProgramArguments</key>
<array>
<string>/usr/bin/rsync</string>
<string>-av</string>
<string>--delete</string>
<string>/Users/xxx/Desktop/</string>
<string>/Users/xxx/Library/Mobile Documents/com~apple~CloudDocs/iCloudDesktop/</string>
</array>
<key>RunAtLoad</key>
<true/>
<key>StartCalendarInterval</key>
<array>
<dict>
<key>Minute</key>
<integer>0</integer>
</dict>
</array>
<key>StandardOutPath</key>
<string>/tmp/rsync.out</string>
<key>StandardErrorPath</key>
<string>/tmp/rsync.err</string>
</dict>
</plist>
Hier zur Erinnerung ein Video mit Otto Hahn (1879-1968), Lise Meitner (1878-1968) und Fritz Straßmann (1902-1980) im Gespräch, eine historische Aufnahme aus dem Januar 1960.
Nur Otto Hahn bekam einen Nobelpreis, obwohl unstrittig alle an der Entdeckung der Kernspaltung beteiligt waren.
Es fällt auf, wie zuvorkommend in der Diskussion immer auch die Leistung von anderen anerkannt wird, die eigentlich demütige Grundeinstellung aller, in der nicht die Selbstdarstellung sondern der Erkenntnisgewinn die treibende Kraft war.
There is a funny paper at arXiv, that is now published in Neurology. It claims to have found a
neural conundrum behind the slowness of human behavior. The information throughput of a human being is about 10 bits/s. In comparison, our sensory systems gather data at ~10^9 bits/s. The stark contrast between these numbers remains unexplained and touches on fundamental aspects of brain function: What neural substrate sets this speed limit on the pace of our existence? Why does the brain need billions of neurons to process 10 bits/s? Why can we only think about one thing at a time?
If there are really two brains, an “outer” brain with fast high-dimensional sensory and motor signals and an “inner” brain that does are the processing? My inner brain says this is a huge speculation.
I needed this urgently for indexing PDFs as Spotlight on the Mac is highly erratic after all this years.
Anything LLM seemed the most promising approach with an easy to use GUI and being well documented. But indexing failed after several hours, so I went on with LM Studio. Also this installation turned out to be more complicated than expected due to library “dependency hell” and version mismatch spiralling…
30.000 PDFs result in a 4G index while the system is unfortunately not very responsive (yet)
Continue reading How to run LLaMA on your local PDFs
Herrmann von Helmholtz hat ja selbst über seine Krankheit geschrieben.
Und von Heisenberg wissen wir auch, daß er Heuschnupfen hatte.
Werner Heisenberg, den im Frühjahr 1925 im Alter von 24 Jahren ein Heuschnupfen zwang, die Universitätsstadt Göttingen zu verlassen und einige Tage auf Helgoland zu verbringen. Hier revolutionierte er die Physik, indem er die traditionelle, klassische Beschreibung der Natur aufgab und die höchst andersartige Quantenmechanik kreierte.
Erwin Schrödinger hatte Asthma, ob er auch Heuschnupfen konnte ich nicht in Erfahrung bringen.
vs
Here is our newest paper – a nice collaboration with Andrea Taloni et al. along with a nice commentary – to recognize surgisphere-like fraud
Recently, it was proved that the large language model Generative Pre-trained Transformer 4 (GPT-4; OpenAI) can fabricate synthetic medical datasets designed to support false scientific evidence. To uncover statistical patterns that may suggest fabrication in datasets produced by large language models and to improve these synthetic datasets by attempting to remove detectable marks of nonauthenticity, investigating the limits of generative artificial intelligence.
[…] synthetic datasets were produced for 3 fictional clinical studies designed to compare the outcomes of 2 alternative treatments for specific ocular diseases. Synthetic datasets were produced using the default GPT-4o model and a custom GPT. Data fabrication was conducted in November 2024. Prompts were submitted to GPT-4o to produce 12 “unrefined” datasets, which underwent forensic examination. Based on the outcomes of this analysis, the custom GPT Synthetic Data Creator was built with detailed instructions to generate 12 “refined” datasets designed to evade authenticity checks. Then, forensic analysis was repeated on these enhanced datasets. […]
Sufficiently sophisticated custom GPTs can perform complex statistical tasks and may be abused to fabricate synthetic datasets that can pass forensic analysis as authentic.
Science’s Holden Thorp nailed it again
Scientists take it for granted that the consensus they refer to is not the result of opinion polls of their colleagues or a negotiated agreement reached at a research conclave. Rather, it is a phrase that describes a process in which evidence from independent lines of inquiry leads collectively toward the same conclusion. This process transcends the individual scientists who carry out the research.
Unfortunately parallel lines only intersect at infinity.
Lensrental has some great advice
Quantity Based: One of the continual problems the AI art generation faces is in quantity, though it is continually improving. For instance, in the past, AI art would struggle with getting the correct number of fingers correct, or perhaps the correct placement of knuckles and joints in the fingers.
General Softness & Low Resolution: AI art takes immense computing power to generate, and it still hasn’t streamlined this problem. So often, AI art is limited in resolution and detail.
Repetition: To further expand on the tip above, AI art often uses repetition to help speed up the generation process. So you may see something copied several times over the same image.
Asymmetry: Asymmetry exists in all facets of life, [… if you] photograph the building so that it looks symmetrical across the plane. AI doesn’t understand these rules and often creates subtle symmetry shifts in its images.
TBC
Just some context for non-German readers …
The last science ministry here Bettina Stark-Watzinger was preparing a list whether they could strip funding from academics who had supported a pro-Palestinian protest camp in Berlin. Here are the links
Unfortunately, even after premature termination of her position, 82% of German scientists still have only short-term contracts.
The incoming new minister Dorothee Bär had doubts on man-made climate change and believes in technology neutrality as we have “nuclear fusion and alike”.
