As written before I never published any study that included a Mendelian randomization. The reasons are well known.
A new paper from Bristol discusses the recent explosion of low-quality two-sample Mendelian randomization studies and offers a cure.
We advise editors to simply reject papers that only report 2SMR findings, with no additional supporting evidence. For reviewers receiving such papers, we provide a template for rejection.
Apple recently introduced audio recording in Notes which is great. Hopefully the next version can finally compete with Notability.
Here is new video test file build with R.
I changed several things from the last version, basically switching to a new layout and going down from 100fps to 60fps as YT can handle this much better.
Just in case, somebody wants to modify it, here is the script.
vid <- function(nr){
nr2 = as.integer(nr*60) # total number 1/2s
nr3 = -600 + (nr*10) # current ms title
for (ii in 1:60 ){
fn = paste0(str_pad(nr*60+ii,5,pad = "0"), ".png")
png(file = fn, width = 1600, height = 900, units = 'px') # defaults to 300 ppi
par(mar=c(0,0,0,0),bg="black")
plot(c(0, 1), c(0, 1), ann = F, bty = 'n', type = 'n', xaxt = 'n', yaxt = 'n', asp=1)
color="red"
rect(xleft=0.5, xright=(nr3+500)/1000, ybottom=0.94, ytop=0.99, col= color)
color="lightgrey"
if( (nr<=58 & ii==30+nr/2) | (nr>=60 & ii==-30+nr/2) ) {
circlize::draw.sector(0, 360, center = c(0.02, 0.01), rou1 = 0.01, col = color, border = color)
}
circlize::draw.sector(90, 90-ii*6, center = c(0.5, 0.52), rou1 = 0.4, col = color, border = color)
if (ii<3 | ii>57) {
color="white"
circlize::draw.sector(0, 360, center = c(0.5, 0.52), rou1 = 0.4, col = color, border = color)
}
tx=paste(nr3,' ms')
text(x = 0.5, y = 0.85, tx, cex = 6, col = "white", family="Lato", adj=0.5)
tx=paste0(nr/2,':',str_pad( round(100*ii/60), 2, pad = "0"))
text(x = 0.5, y = 0.5, tx, cex = 12, col = "white", family="Lato", adj=0.5)
tx = "play until beep & flash in sync OR take image of source and processed video"
text(x = 0.5, y = 0.075, tx, cex = 3, col = "grey", family="Lato", adj=0.5)
par(bg="white")
dev.off()
}
}
for (i in seq(0,120,2) ) {
vid(i)
}
fn = paste0(list.files('*.png'))
av::av_encode_video(fn, framerate = 60, output = 'video.mp4')
Being a regular Scholar user, I am quite lost now with the many new scientific search engines. They don’ tell us which data they used for training, how they have been trained and how the results have been validated. The field is also highly dynamic when compared to the situation 2 years ago. Is it worth to test them?
Continue reading Too many AI powered scientific search engines
The common theme seems the low certainty about facts – a historical event that is wrongly memorized by a human or the Large Language Model that wrongly extrapolates from otherwise secure knowledge. But is there even more?
Yann Le Cun is being quoted at IEEE Spectrum
“Large language models have no idea of the underlying reality that language describes,” he said, adding that most human knowledge is nonlinguistic. “Those systems generate text that sounds fine, grammatically, semantically, but they don’t really have some sort of objective other than just satisfying statistical consistency with the prompt.”
Humans operate on a lot of knowledge that is never written down, such as customs, beliefs, or practices within a community that are acquired through observation or experience. And a skilled craftsperson may have tacit knowledge of their craft that is never written down.
I think “hallucination” is way too much an anthropomorphic concept – some LLM output is basically statistical nonsense (although I wouldn’t go as far as Michael Townsen Hicks…). Reasons for these kind of errors are manifold -reference divergence may be already in the data used for learning – data created by bots, conspiracy followers or even fraud science. The error may also originate from encoding or decoding routines.
I couldn’t find any further analogy with wrong human memory recall except the possibility that also human memory is influenced by probability as well. Otgar 2022 cites Calado 2020
The issue of whether repeated events can be implanted in memory has recently been addressed by Calado and colleagues (2020). In their experiment, they falsely told adult participants that they lost their cuddling toy several times while control participants were told that they only lost it once. Strikingly, they found that repeated false events were as easily inserted in memory as suggesting that the event happened once. So, this study not only showed that repeated events can be implanted, it raised doubts about the idea that repeated events might be harder to implant than single events
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While we are still waiting for the Nobel prize speech of Geoffrey Hinton in December, AI makes even more negative headlines.
[Hinton] “I worry that the overall consequences of this might be systems that are more intelligent than us that might eventually take control.” He also said he uses the AI chatbot ChatGPT4 for many things now but with the knowledge that it does not always get the answer right.
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The sheer power consumption of running AI models is frightening. Nature News asks if AI’s huge energy demands will spur a nuclear renaissance
Google announced that it will buy electricity made with reactors developed by Kairos Power, based in Alameda, California. Meanwhile, Amazon is investing approximately US$500 million in the X-Energy Reactor Company, based in Rockville, Maryland, and has agreed to buy power produced by X-energy-designed reactors due to be built in Washington State.
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A former OpenAI employee talks on his blog how AI is using copyrighted material eg stealing content.
While generative models rarely produce outputs that are substantially similar to any of their training inputs, the process of training a generative model involves making copies of copyrighted data. If these copies are unauthorized, this could potentially be considered copyright infringement, depending on whether or not the specific use of the model qualifies as “fair use”. Because fair use is determined on a case-by-case basis, no broad statement can be made about when generative AI qualifies for fair use. Instead, I’ll provide a specific analysis for ChatGPT’s use of its training data, but the same basic template will also apply for many other generative AI products.
Effects can be measured only indirectly for example by the visitor count at Stack Overflow where the traffic declined as many user (including me) don’t need Stack Overflow anymore.
Here is another phantastic discussion over at PP between Henry Leirvoll and 495yt on the very basic questions of copyright.
humans get inspired (parsing the external examples or experiences through their inner understanding and individual perspective) they start working to make something with their tools, skills, time and purpose. the result represents the author, their influences and their message.
a lot of this process is protected by copyright.
ai is not inspired. and it has no personal perspective or tools. no message to transmit.
any message put into prompts by an ai user is translated by it’s LLM layer into other, more complex prompts, which also get treated quasi-randomly by the weights and biases of the model, as well as rand seeds.
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And well, ChatGPT can produce malicious code even with all precautions: Researchers Bypass AI Safeguards Using Hexadecimal Encoding and Emojis
If a user instructs the chatbot to write an exploit for a specified CVE, they are informed that the request violates usage policies. However, if the request was encoded in hexadecimal format, the guardrails were bypassed and ChatGPT not only wrote the exploit, but also attempted to execute it “against itself”, according to Figueroa.
Geoffrey Hinton, the new physics Nobel prize winner, had also a big influence on the biology and omics field. This cannot be emphasized enough when reading his 2008 paper on “Visualizing Data using t-SNE” and the current implementation at t-SNE and UMAP projections in R.
Nach einigen früheren Projekten auf Basis des Raspberry Pi Zero kommt hier nun ein viertes mit dem Temperatur und Luftfeuchte im Keller und der Aussenluft bestimmt wird. Es funktioniert auf Basis des häufig verwendeten SHTC3 Sensors.
Da die physikalische I2C Adresse des SHTC3 nicht geändert werden kann, brauchen wir zusätzlich ein Mux Board, das mit einer Lötbrücke auf dem ADR0 Jumper die Adresse von 0x70 auf 0x71 ändert.
#!/usr/bin/env python3
import csv
import board
import busio
import adafruit_shtc3
from adafruit_tca9548a import TCA9548A
def main():
# Initialize the iic bus
i2c = busio.I2C(board.SCL, board.SDA)
# Initialize the TCA9548A multiplexer
mux = TCA9548A(i2c, address=0x71)
# Initialize the SHTC3 sensor on MUX channel 0
shtc3_channel_0 = adafruit_shtc3.SHTC3(mux[0])
# Initialize the SHTC3 sensor on MUX channel 1
shtc3_channel_1 = adafruit_shtc3.SHTC3(mux[1])
with open("/home/admin/www/taupunkt.log","a+") as out_file:
tsv_writer = csv.writer(out_file, delimiter='\t')
temperature0, relative_humidity0 = read_shtc3(shtc3_channel_0)
temperature1, relative_humidity1 = read_shtc3(shtc3_channel_1)
dt = time.strftime('%Y-%m-%d %H:%M:%S')
tsv_writer.writerow([dt,
temperature0,relative_humidity0,
temperature1,relative_humidity1])
if __name__ == '__main__':
main()
Damit kann nun nach Bedarf ein Lüfter angesteuert werden, mit unter 40€ Bauteilen statt mit 570€ Fertiglösung. Als smarte Steckdosen gibt es zB die Edimax Smartplug SP1101W auf der ein Webserver läuft der curl Befehle annimmt.
News article and paper showing
bigger AI chatbots more inclined to spew nonsense — and people don’t always realize.
and some solutions
various emerging techniques should help to create chatbots that bullshit less, or that can, at least, be prodded to disclose when they are not confident in their answers. But some hallucinatory behaviours might get worse before they get better.
I used to parse PDFs using the Allenai method and the layoutparser.
This worked in many instances but is no longer maintained.
I still have Nougat on my to do list while a new paper now points to AceParse
AceParse includes various types of structured text, such as formulas, tables, algorithms, lists, and sentences embedded with mathematical expressions, among others. We provide examples of several dataset samples to give you a better understanding of our dataset.
From the creator of wordfreq
Generative AI has polluted the data
I don’t think anyone has reliable information about post-2021 language usage by humans.
The open Web (via OSCAR) was one of wordfreq’s data sources. Now the Web at large is full of slop generated by large language models, written by no one to communicate nothing. Including this slop in the data skews the word frequencies.
I wanted to allow a PDF download on my blog. Unfortunately this turned out to be complicated for multiple problems including WP and browser caching, CSS failure and mPDF misdirection. Here are the solutions.
Issue 1: If you are editing the print.css but the browser shows the old version: Comment out or delete the reference to parent in child css
/*
Theme Name: Twenty Twenty Child 1
Theme URI: http://wordpress.org/themes/twentytwenty
@import url('../twentytwenty/style.css');
*/
and include versioning in header.php
function my_theme_enqueue_styles() {
wp_enqueue_style( 'twentyfourteen', get_template_directory_uri() . '/style.css' );
wp_enqueue_style( 'twentyfourteen-child1', get_stylesheet_directory_uri() . '/style.css', array(), filemtime(get_template_directory() . '/style.css'), false );
}
add_action( 'wp_enqueue_scripts', 'my_theme_enqueue_styles' );
Issue 2: css editing is applied but printing is still not correctly formatted, possible due to higher specificity of formatting instructions in the parent theme.
Solution: This is a bit tricky as it needs try and error to find the offending element. Chrome can show the print version in the browser as shown on this SO thread
Then add the important flag to the specific css rule..
@media print {
.myelement1 { display: none !important; }
}
Issue 3: mPDF pulls the screen not the print version. Solution: Create an own mpdf media tag.
@media mpdf {
#sidebar, #header {
display:none !important;
}
blockquote, table, pre {
page-break-inside:avoid !important;
font-size: 0.7em !important;
}
}
Wir brauchen offensichtlich auch so ein “Kill Switch” Gesetz wie in Kalifornien das einen Notausschalter vorschreibt , wenn die Filter nicht mehr mitkommen undunethische Entscheidungen getroffen werden.
As we’ve previously explored in depth, SB-1047 asks AI model creators to implement a “kill switch” that can be activated if that model starts introducing “novel threats to public safety and security,” especially if it’s acting “with limited human oversight, intervention, or supervision.”
Nur – wann wird der Kill Switch aktiviert? Bilder wie die von Elon Musk’s X-Grok könnten wahlentscheidend sein.
Oh my god. Grok has absolutely no filters for its image generation. This is one of the most reckless and irresponsible AI implementations I’ve ever seen. pic.twitter.com/oiyRhW5jpF — Alejandra Caraballo (@Esqueer_) August 14, 2024
After changing the battery it needs to be calibrated
So the question was how to drain it most effectively? Maybe playing video would work but running this short line in the terminal as may times as there are CPUs was much more efficient
yes > /dev/null &
It seems that the initial high loading is most important
Researchers at SLAC-Stanford Battery Center report in a study published today in Joule that giving batteries their first charge “at unusually high currents increased their average lifespan by 50% while decreasing the initial charging time from 10 hours to just 20 minutes.”
A recent stackexcange response confirms this
Apple Mail can make its Core Spotlight index available to other apps which include an app specific Mail Plugin. This is controlled (from the Apple Mail perspective) in the general preferences under Manage Plug-ins.
Unfortunately however the Houdah plugin stopped now working. So the only working solution seems InfoClick that can build its own index but takes 17 hours for 340.00 emails… 