Tootfinder

Frankly, Trump may have a greater impact on the English language than any president in the history of the United States, believe me. 🤣
https://www.nytimes.com/2025/08/17/opinion/trump-speaking-style.html?smid=nytcor…

Opinion | The Insidious Creep of Trump’s Speaking Style
The fact that we’re talking like Donald Trump could mean that we’re starting to think like him as well.

The Insidious Creep of Trump's Speaking Style (Adam Aleksic/New York Times)
https://www.nytimes.com/2025/08/17/opinion/trump-speaking-style.html?unlocked_article_code=1.e08.xShm.8RpYjZSTm0Nn&smid=url-share
http://www.memeorandum.com/250817/p61#a250817p61

🇺🇦 Auf radioeins läuft...
Sophia Kennedy:
🎵 Imaginary Friend (Radio Edit)
#NowPlaying #SophiaKennedy
https://sophiakennedy.bandcamp.com/track/imaginary-friend
https://open.spotify.com/track/7CbVZ2Yzl0TtcCERaJDZSR

Es ist wieder einmal #SchiffsSamstag, heute mit der GOETHEBORG, aufgenommen bei der #Sail 2015 in #Bremerhaven. Sie ist ein Nachbau des 1738 gebauten Ostindienfahrers der Schwedischen Ostindien-Kompa…

Enhancing Symbolic Machine Learning by Subsymbolic Representations
Stephen Roth, Lennart Baur, Derian Boer, Stefan Kramer
https://arxiv.org/abs/2506.14569 …

Enhancing Symbolic Machine Learning by Subsymbolic Representations
The goal of neuro-symbolic AI is to integrate symbolic and subsymbolic AI approaches, to overcome the limitations of either. Prominent systems include Logic Tensor Networks (LTN) or DeepProbLog, which offer neural predicates and end-to-end learning. The versatility of systems like LTNs and DeepProbLog, however, makes them less efficient in simpler settings, for instance, for discriminative machine learning, in particular in domains with many constants. Therefore, we follow a different approach:…

Loosely Speaking
Great Australian Pods Podcast Directory: #GreatAusPods

UniSLU: Unified Spoken Language Understanding from Heterogeneous Cross-Task Datasets
Zhichao Sheng, Shilin Zhou, Chen Gong, Zhenghua Li
https://arxiv.org/abs/2507.12951

UniSLU: Unified Spoken Language Understanding from Heterogeneous Cross-Task Datasets
Spoken Language Understanding (SLU) plays a crucial role in speech-centric multimedia applications, enabling machines to comprehend spoken language in scenarios such as meetings, interviews, and customer service interactions. SLU encompasses multiple tasks, including Automatic Speech Recognition (ASR), spoken Named Entity Recognition (NER), and spoken Sentiment Analysis (SA). However, existing methods often rely on separate model architectures for individual tasks such as spoken NER and SA, whi…

🇺🇦 Auf #radioeins läuft...
Sophia Kennedy:
🎵 Hot Match
#NowPlaying #SophiaKennedy
https://sophiakennedy.bandcamp.com/track/hot-match
https://open.spotify.com/track/1QBwezIfN3BP7CF1vN7wCk

The Trump regime is arresting protesters now. This was the plan all along. (Will Bunch/The Philadelphia Inquirer)
https://www.inquirer.com/opinion/ice-protesters-arrested-spokane-20250717.html
http://www.memeorandum.com/250717/p131#a250717p131

🇺🇦 Auf radioeins läuft...
Sufjan Stevens:
🎵 Video game
#NowPlaying #SufjanStevens
https://music.sufjan.com/track/video-game
https://open.spotify.com/track/3Q8B1nnUBlJz0VTajyk1IS