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Metin Seven 🎨

2025 summed up in one headline…

𝙎𝙥𝙤𝙩𝙞𝙛𝙮 𝙋𝙪𝙗𝙡𝙞𝙨𝙝𝙚𝙨 𝘼𝙄-𝙂𝙚𝙣𝙚𝙧𝙖𝙩𝙚𝙙 𝙎𝙤𝙣𝙜𝙨 𝙁𝙧𝙤𝙢 𝘿𝙚𝙖𝙙 𝘼𝙧𝙩𝙞𝙨𝙩𝙨 𝙒𝙞𝙩𝙝𝙤𝙪𝙩 𝙋𝙚𝙧𝙢𝙞𝙨𝙨𝙞𝙤𝙣

404media.co/spotify-publishes-

404 Media · Spotify Publishes AI-Generated Songs From Dead Artists Without Permission"They could fix this problem. One of their talented software engineers could stop this fraudulent practice in its tracks, if they had the will to do so."
#AI#Spotify#music
Ruth Mottram

On reflection, I think the big mistake is the conflation of #AI with #LLM and #MachineLearning.
There are genuine exciting advances in ML with applications all over the place, in science, (not least in my own research group looking at high resolution regional climate downscaling), health diagnostics, defence etc. But these are not the AIs that journalists are talking about, nor that are really related the LLMs.
They're still good uses of GPUs and will probably produce economic benefits, but probably not the multi- trillion ones the pundits seem to be expecting

fediscience.org/@Ruth_Mottram/
Ruth_Mottram - My main problem with @edzitron.com 's piece on the #AIbubble is that I agree with so much of it.
I'm now wondering if I've missed something about #LLMs? The numbers and implications for stock markets are terrifyingly huge!

wheresyoured.at/the-haters-gui

FediScience.orgRuth Mottram (@Ruth_Mottram@fediscience.org)My main problem with @edzitron.com 's piece on the #AIbubble is that I agree with so much of it. I'm now wondering if I've missed something about #LLMs? The numbers and implications for stock markets are terrifyingly huge! https://www.wheresyoured.at/the-haters-gui/
US

europesays.com/us/77484/ An end-to-end multifunctional AI platform for intraoperative diagnosis #ai #ArtificialIntelligence #Biomedicine #biotechnology #diagnosis #general #MachineLearning #Medicine/PublicHealth #Technology #UnitedStates #UnitedStates #US

An end-to-end multifunctional AI platform for intraoperative diagnosis
United States · An end-to-end multifunctional AI platform for intraoperative diagnosis - United StatesIntraoperative frozen section diagnosis provides essential, real-time histological insights to guide surgical decisions. However, the quality of these time-sensitive sections is often suboptimal, posing significant diagnostic challenges for pathologists. To address these limitations, we utilized over 6700 whole slide images to develop GAS, a comprehensive platform comprising three modules: Generation, Assessment, and Support modules. The Generation module, based on a GAN-driven multimodal network guided by FFPE-style text descriptions, demonstrated effective enhancement of frozen section quality across various organs. The Assessment module, which fine-tuned quality control models using pathological foundation models, showed substantial improvements in microstructural quality for the generated images. Validated through a prospective study (ChiCTR2300076555) on the human–AI collaboration software, the Support module demonstrated that GAS significantly boosted diagnostic confidence for pathologists. In summary, this study highlights the clinical utility of the GAS platform in intraoperative diagnosis and establishes a new paradigm for integrating end-to-end AI solutions into clinical workflows.
Tanguy Fardet

I just discovered the ARC-AGI initiative and the associated test to estimate how close "AI" models are from #AGI

arcprize.org/arc-agi

While I found the initiative interesting, I'm not sure I understand what in this test really guarantees that the model is capable of some form of generalization and problem-solving.
Wouldn't it be possible for specialized pattern-matching/discovering algorithms to solve such problems?
I imagine some computer scientists, mathematicians or computational neuroscientists have already had a look at this, so would anyone knows of some articles/blogs on the topic?

Maybe @wim_v12e? Is this something you already looked at?

ARC PrizeARC Prize - What is ARC-AGI?The only AI benchmark that measures AGI progress.
#AI#machineLearning#neuroscience
*
Institute for AI

Is complex query answering really complex? A paper at the International Conference on Machine Learning (#ICML2025) presented by Cosimo Gregucci, PhD student at @UniStuttgartAI @Uni_Stuttgart, discussed this question.

In this paper, Cosimo Gregucci, Bo Xiong, Daniel Hernández (@daniel), Lorenzo Loconte, Pasquale Minervini (@pminervini), Steffen Staab, and Antonio Vergari (@nolovedeeplearning) reveal that the “good” performance of SoTA approaches predominantly comes from answers that can be boiled down to single link prediction. Current neural and hybrid solvers can exploit (different) forms of triple memorization to make complex queries much easier. The authors confirm this by reporting the performance of these methods in a stratified analysis and by proposing a hybrid solver, CQD-Hybrid, which, while being a simple extension of an old method like CQD, can be very competitive against other SoTA models.

The paper proposed a way to make query answering benchmarks more challenging in order to advance science.

arxiv.org/abs/2410.12537

#KnowledgeGraphs#QueryAnswering#ArtificialIntelligence
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