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mariozechner.at
Dann zitieren sie das als Methode:

papers.ssrn.com/sol3/papers....

Sprich, sie reproduzieren einfach eine Studie, die auch schon garbage war.

Vergleiche dazu diesen Artikel in Cell mit dem klingenden Title: "The reanimation of pseudoscience in machine learning and its ethical repercussions"
Assessing the Big Five personality traits using real-life static facial images A Kachur, E Osin, D Davydov, K Shutilov, A Novokshonov Scientific Reports, 2020•nature.com Abstract There is ample evidence that morphological and social cues in a human face provide signals of human personality and behaviour. Previous studies have discovered associations between the features of artificial composite facial images and attributions of personality traits by human experts. We present new findings demonstrating the statistically significant prediction of a wider set of personality features (all the Big Five personality traits) for both men and women using real-life static facial images. Volunteer participants (N = 12,447) provided their face photographs (31,367 images) and completed a self-report measure of the Big Five traits. We trained a cascade of artificial neural networks (ANNs) on a large labelled dataset to predict self-reported Big Five scores. The highest correlations between observed and predicted personality scores were found for conscientiousness (0.360 for men and 0.335 for women) and the mean effect size was 0.243, exceeding the results obtained in prior studies using ‘selfies’. The findings strongly support the possibility of predicting multidimensional personality profiles from static facial images using ANNs trained on large labelled datasets. Future research could investigate the relative contribution of morphological features of the face and other characteristics of facial images to predicting personality. Our AI-based methodology for extracting the Photo Big 5 personality scores uses an updated algorithm originally developed by Kachur et al. (2020, KODSN), who used selfsubmitted images annotated with Big 5 survey responses from a large sample of individuals to extract facial features and train a cascade of artificial neural networks that learns to predict personality from facial images. In the KODSN validation sample, the correlation between self-reported and photo-based personality scores ranges between 0.14 and 0.36, with most correlations exceeding 0.2. These correlations are comparable to those typically found between survey-based personality self-assessments and assessments made by individuals’ peers (e.g., co-workers), which range from 0.30 to 0.41, and higher than those between 4 self-reported personality and traits assessed by strangers after watching a short interaction video (Connolly et al., 2007). The reanimation of pseudoscience in machine learning and its ethical repercussions Mel Andrews1,2 ∙ Andrew Smart3 andrewsmart@google.com ∙ Abeba Birhane4,5 Affiliations & Notes Article Info Cover Image - Patterns, Volume 5, Issue 9 Download PDF Cite Share Set Alert Get Rights Reprints Previous article Next article Show Outline The bigger picture Machine learning has a pseudoscience problem. An abundance of ethical issues arising from the use of machine learning (ML)-based technologies—by now, well documented—is inextricably entwined with the systematic epistemic misuse of these tools. We take a recent resurgence of deep learning-assisted physiognomic research as a case study in the relationship between ML-based pseudoscience and attendant social harms—the standard purview of “AI ethics.” In practice, the epistemic and ethical dimensions of ML misuse often arise from shared underlying reasons and are resolvable by the same pathways. Recent use of ML toward the ends of predicting protected attributes from photographs highlights the need for philosophical, historical, and domain-specific perspectives of particular sciences in the prevention and remediation of misused ML. Summary The present perspective outlines how epistemically baseless and ethically pernicious paradigms are recycled back into the scientific literature via machine learning (ML) and explores connections between these two dimensions of failure. We hold up the renewed emergence of physiognomic methods, facilitated by ML, as a case study in the harmful repercussions of ML-laundered junk science. A summary and analysis of several such studies is delivered, with attention to the means by which unsound research lends itself to social harms. We explore some of the many factors contributing to poor practice in applied ML. In conclusion, we offer resources for research best practices to developers and practitioners. Introduction The fields of AI/machine learning (ML) ethics and responsible AI have documented an abundance of social harms enabled by the methods of ML, both actual and potential. Although the topic is comparatively more obscure, critics have also sought to draw attention to the epistemic failings of ML-based systems: failures of functionality and scientific legitimacy.1,2 The connection between the ethicality and epistemic soundness of deployed ML, however, has received scant attention.3,4 We urge that if the field of AI ethics is to be efficacious in preventing and remediating the social harms flowing from deployed ML systems, it must first grapple with discrepancies between the presumed epistemic operation of these tools and their in-practice ability to achieve those aims. While such an observation is not novel (see Raji et al.3), we build on prior work, both in offering an analysis of the issue from a philosophical vantage point and in venturing into the intricacies of in-practice epistemic and ethical misuses of ML systems. We argue that philosophical, historical, and scientific perspectives are necessary in confronting these issues and that ethical and epistemic issues cannot, and should not, be confronted independently. A recent surge of deep learning-based studies have claimed the ability to predict unobservable latent character traits, including homosexuality, political ideology, and criminality, from photographs of human faces or other records of outward appearance, including Alam et al.,5 Chandraprabha et al.,6 Hashemi and Hall,7 Kabir et al.,8 Kachur et al.,9 Kosinski et al.,10 Mindoro et al.,11 Parde et al.,12 Peterson et al.,13 Mujeeb
November 7, 2025 at 8:57 AM
radiology-ai.bsky.social
Check out today's #FreeFriday @radiology-ai.bsky.social article from #PubMedCentral!

Challenges of Implementing Artificial Intelligence–enabled Programs in the Clinical Practice of Radiology

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11427920 #ML #MachineLearning #ArtificialIntelligence
Image from article in Radiology: Artificial Intelligence
October 10, 2025 at 2:16 PM
brianreeve.bsky.social
So much VC money goes to companies with "AI-powered" or "AI-enabled" in their descriptions.

Are those companies just leveraging existing AI products or actually innovating on underlying AI/ML tech?
March 12, 2025 at 5:22 PM
10000thmule.bsky.social
Jokes on us they're gonna spend it all on AI/ML enabled quadcopters with 500g tannerite payloads bought at 10000% markup from Anduril
June 6, 2025 at 9:30 PM
javanse.bsky.social
I don't think they really understand what "AI"(ML enabled would be more accurate) targeting systems do. Given the age of current decision makers even the brightest probably imagine sci-fi AI from the 1960s~80s. IMO such an agreement is as meaningful as banning laser guidance in missiles
November 13, 2023 at 8:10 PM
techbykanwal.bsky.social
In 2023, I was awarded with a fully funded Mitacs Research Internship in Canada.
-got the chance for global exposure and networking with top-notch Canadian professors
-research projects in AI/ML enabled me to go beyond in research.
Read about it more here: shorturl.at/2nsLg
January 10, 2025 at 6:41 PM
anthonydemeusy.bsky.social
Top top takeaway from the Artificial Intelligence Index Report 2025, published by Stanford HAI
“In 2023, the FDA approved 223 AI-enabled medical devices, up from just six in 2015.”
The FDA released data for 2024, showing a decrease in approval, down to 169 AI/ML-enabled medical devices in 2024.
Number of AI medical devices approved by the FDA, 1995–2024 | Created with Datawrapper
Create interactive, responsive & beautiful charts — no code required.
www.datawrapper.de
April 20, 2025 at 12:11 AM
nostalgianinja.com
I found a property when searching browser .ml that might be worth turning off as well: "browser . ml . enabled". I'm disabling it, but do you think this is needed especially since the one you gave was specifically for the chat feature?
May 27, 2025 at 7:44 PM
g-age.bsky.social
Kyber (ML-KEM)
✅ Yes
Used in hybrid PQC + Ed25519 key agreement (default for validator comms and identity exchange)

Dilithium (ML-DSA)
⚠️ Optional
Available via crypto/pqc.rs, not enabled by default due to size/speed tradeoff
August 5, 2025 at 4:37 PM
davidnett.bsky.social
I've worked for a decade w tech enabled by what we used to call Deep Learning & ML, now swept into the AI bucket. There is great utility in these underlying technologies. Special-purpose models can be trained ethically (I wrote a paper on it). But GenAI nonsense is tarnishing an entire industry. 2/
January 24, 2025 at 5:35 PM
dogsinautumn.bsky.social
It was a marketing coup to rebrand ML as AI, which conjures all kinds of fantastic and mostly impossible specters of the future.

I think it will prove to have been a mistake shortly. It enabled a kind of cavalier cultishness that’s going to inflict a lot of pain, likely for negative returns.
May 7, 2025 at 9:58 PM
bluesky.awakari.com
Assigning Specific GPUs to Docker Containers with Docker Compose When working with GPU-enabled workloads in Docker — such as AI/ML models, video processing, or simulations — it’s often important to… Continue reading on Medium »

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medium.com
May 13, 2025 at 7:33 AM
deep-in-depth.bsky.social
Optimized Grasshopper Optimisation Algorithm enabled DETR (DEtection TRansformer) model for skin disease classification #DL #AI #ML #DeepLearning #ArtificialIntelligence #MachineLearning #ComputerVision #AutonomousVehicles #Robotics #LLM #VLM #LVLM
journals.plos.org/plosone/arti...
Optimized Grasshopper Optimisation Algorithm enabled DETR (DEtection TRansformer) model for skin disease classification
Skin disease classification is a choir cognate for early diagnosis and therapy. The novelty of this study lies in integrating the Grasshopper Optimisation Algorithm (GOA) with a DETR (DEtection…
journals.plos.org
June 3, 2025 at 4:03 PM
chillu.com
So many AI enabled startups stating “we’ll never train on our data”. I wonder how many of them will walk that back once discovering the limits of Gen AI and rediscovering how traditional ML works…
August 17, 2025 at 2:49 AM
bluesky.awakari.com
thanks for the informative article The offline ML-based human-language translation system is still enabled for the Debian builds of Firefox, but it auto-downloads the models though, since Debian doesn't train them, relying on the Mozilla-trained versions.

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lwn.net
August 22, 2025 at 3:29 AM
premiumai.bsky.social
"Unlock your data science potential with these top FREE AI tools:
1. Google Colab (GPU-enabled notebooks)
2. H2O AI (machine learning & deep learning)
3. TensorFlow (open-source ML framework)
4. Scikit-Learn (Pyt... Get Unlimited Access to 32+ Premium AI Tools for Just $2 - cutt.ly/6rn9D9aD
June 8, 2025 at 11:30 AM
daemonburrito.bsky.social
Convince me I'm wrong about the impossibility of rehabilitating the party. The Hogg mess is only the latest example of the futility, and it wasn't like Hogg was an ML.

imo, the Third Way centrism starting with Clinton enabled our current predicament. What hasn't the Left ceded?
June 20, 2025 at 5:00 PM
arxiv-cs-cr.bsky.social
Gargi Mitra, Mohammadreza Hallajiyan, Inji Kim, Athish Pranav Dharmalingam, Mohammed Elnawawy, Shahrear Iqbal, Karthik Pattabiraman, Homa Alemzadeh
Systems-Theoretic and Data-Driven Security Analysis in ML-enabled Medical Devices
https://arxiv.org/abs/2506.15028
June 19, 2025 at 9:08 AM
condmatmtrlsci-bot.bsky.social
arXiv:2505.23064v1 Announce Type: new
Abstract: The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing [1/7 of https://arxiv.org/abs/2505.23064v1]
May 30, 2025 at 6:09 AM
mhwilliams.bsky.social
There were folks on the other site posting at Adobe that they want features like easy ML-enabled layer masking, but those aren't the "AI" features they're getting.
xoxogossipgita.bsky.social it's gita time @xoxogossipgita.bsky.social · Mar 26
The more I think about it, the more I think it’s going to become increasingly necessary to differentiate from tools that are useful and powered in part by machine learning (content aware fill, advanced CG animation techniques) and generative AI plagiarizing artists at lighting speeds
March 26, 2024 at 10:45 PM
loremanlear.bsky.social
"...so when implemented properly, PoW + PoS hybrids,
PoS + Byzantine Fault Tolerance, and
AI/ML-enabled consensus address the trilemma of security, decentralization, and scalability..."
a woman is laying on the ground with a man laying on the ground behind her .
ALT: a woman is laying on the ground with a man laying on the ground behind her .
media.tenor.com
December 11, 2024 at 9:14 AM
graygooglitch.com
A rush to push ML enabled features to market that runs counter to both mission and messaging? You don't say...
June 7, 2024 at 11:20 AM
nielsberglund.bsky.social
Hey, I am new here. I live in Durban, South Africa. Software Architect Lead at Derivco. My team develops high-performing, AI-enabled iGaming systems.

Microsoft AI MVP. Interested in AI/ML, #GenAI, #AIAgents, #streamprocessing, #databases, #datacommunity.

Blogs at: nielsberglund.com
Niels Berglund | Niels Berglund
Blog by Niels Berglund
nielsberglund.com
November 17, 2024 at 8:53 AM
marsrover.bsky.social
My company has a system of approved services by categories. I was asked if they could shut down the IoT section b/c "nobody uses it".

Ahh IoT, remember when sensor-enabled tiny-buttoned everything was gonna change the world? And then ML, VR, then blockchain?

Anyway glad all fads are over, forever.
June 28, 2025 at 8:25 AM