I've listened to several songs by Parallyx, and they all are pretty interesting! What I believe could make these songs even better is stronger and deeper emotional vocals in refrains. Talking about the "Doomsday" song in particular, I feel there should be much more energy in the refrains.
YES! Another banger giving me chills, adding this to playlist too. That riff before the breakdown was fuckin spicy. Great mix of singing and the background screams, and as a drummer I appreciate how its not overpowering but complimenting the song. You guys absolutely have what it takes to play with the big name bands and I know your going to go far. keep it up and if you ever play live in Minnesota, I'm absolutely gonna be there. Some good shit right here!
"I think this is key, and feel borderline certain that what I'm trying to explain between relational geometry and Spiral Dynamics is (part of, or pointed toward) a potential functional bridge. Just looking at the background generated in the thumbnail and considering it like reverse engineering of glyphic thinking -- could it suggest process snapshots of working oscillation in and between nodes and edges of set circumstance, a la Wolfram's designs? What happens when AI fields further extensibility around why/where the patterns all novel use of analogical references in service to outlining ontological and teleological overlap where the semiotics invoked seem to always parallel? Universally, starting with implications in/as a "sphere" (eg: Monad or Photon) that balances further back and forth in/as all elements, timing that recursively circles back to refresh first principles (retrocausality to the other side of Planck intervals, even, but also when we see past events in a new light, symbolic significance altered) and distance only so far as entanglement extends continuum, before such point then is lined up as focal vector of the particular that serves as reference base for what/is, and the triangulating that coheres the "therefore" (∆) of all modes of languaging: mathematically, physically, conceptually? Is that where general intelligence instantiates truly, hexagons echoing through the combined interstices in some kind of tracing from the molecular up? Biologically, analogically, ecologically, synthetically -- What's the functionally natural intelligence that all systems resonate with the terms and conditions of, in sequencing the optimal? Will AI be able to form process proofs of the Tao via ergonomics, like it seemed sequencing proteins? Wild territory". - Stician Samples Title: The Case for Neuro-Symbolic AI in a Nutshell Neuro-symbolic AI offers a compelling solution to overcome the reasoning limitations in current AI systems by integrating the strengths of deep learning and symbolic logic. As the recent review article here (brandoncolelough.com/Files/LNSAI_survey_paper.pdf) says… “Neuro-Symbolic AI research has seen rapid growth since 2020, with concentrated efforts in learning and inference. Significant gaps remain in explainability, trustworthiness, and Meta-Cognition. Addressing these gaps through interdisciplinary research will be crucial for advancing the field towards more intelligent, reliable, and context-aware AI systems” Here are key improvements for current AI’s reasoning capabilities, each supported by relevant research: 1. Incorporation of Symbolic Logic: Current AI systems, particularly deep learning models, struggle with tasks that involve logic, causality, and abstract reasoning. Integrating symbolic reasoning, which explicitly represents rules and logical structures, with statistical learning allows AI to handle complex, unseen scenarios more effectively. Symbolic logic can handle reasoning tasks in ways that complement deep learning’s strengths in pattern recognition. Reference: For an in-depth exploration of this integration, see “Neural-Symbolic Learning and Reasoning: A Survey and Interpretation” which discusses how symbolic reasoning can complement neural approaches. See… arxiv.org/abs/1711.03902 2. Common-Sense Knowledge: One of the biggest gaps in current AI models is their inability to perform common-sense reasoning. While deep learning models can make statistical inferences, they often lack the common-sense knowledge humans use to navigate everyday situations. Incorporating a robust common-sense knowledge base within a neuro-symbolic AI framework can enable AI systems to make more human-like inferences and avoid nonsensical conclusions. Reference: “COMET: Commonsense Transformers for Automatic Knowledge Graph Construction” for a system that integrates common-sense knowledge into AI models to improve their reasoning capabilities. See… aclanthology.org/P19-1470/ 3. Hybrid Models: Combining deep learning with structured knowledge representations like ontologies or rule-based systems allows AI to take advantage of both paradigms: deep learning for unstructured data and symbolic AI for structured, logical reasoning. Hybrid models can help AI reason better in complex environments that require both data-driven insights and structured reasoning. Reference: For a detailed overview of hybrid models, refer to “Neural-Symbolic Systems: The State of the Art” which explores combining symbolic reasoning with neural networks. See… arxiv.org/abs/2303.14725 4. Transparency and Interpretability: One of the critical challenges in AI is the black-box nature of deep learning models, which makes their decisions difficult to interpret. Integrating symbolic reasoning, which is inherently transparent and rule-based, can improve the interpretability of AI systems. Transparent models allow for better trust and debugging, especially in high-stakes domains like healthcare and finance. Reference: The paper “Towards Explainable AI: A Survey on Interpreting the Inner Workings of Deep Learning” explores approaches to making AI models more interpretable and explains how symbolic methods can aid this effort. See… arxiv.org/abs/2207.13243 5. Handling Novel Situations: Current AI models struggle with generalizing to novel situations that fall outside their training data. By combining deep learning’s ability to handle large-scale data with symbolic reasoning’s rule-based logic, neuro-symbolic AI can improve the generalization capabilities of AI systems. This enables AI to reason through novel scenarios and solve problems more like humans. Reference: For a discussion on handling novel situations in AI, see “Neural-Symbolic Architectures for Generalization and Transfer Learning” which focuses on how to improve AI’s ability to handle new, unfamiliar environments. See… arxiv.org/abs/2402.14047 In Summary: Neuro-symbolic AI represents a balanced approach, addressing the shortcomings of current deep learning models by integrating symbolic logic and structured knowledge into AI systems. This approach will significantly enhance AI’s reasoning, common-sense understanding, transparency, and generalization, making it more reliable and adaptable for real-world applications. cc: Amitā Kapoor Ernest Davis Darius Burschka Luis Lamb Luís Caires William Hsu. Moshe Vardi Ronald Cicurel Turdubaeva Elira
I've made a list of Female fronted metalcore bands that NEED to tour together... Some small bands some big bands but all are female fronted and amazingly talented bands... This needs to be a thing... anyone else with me on this? | Gore. | Project Renegade | Parallyx | Not Enough Space | Conquer Divide | Daedric | The Anchor | Novelists | Jinjer | Spirtbox | Poppy |
Doomsday is out, and so is our debut album THE CULT! Enjoy, it’s yours now. ❤
De la grosse frappe ! Tout l’album est énorme, félicitations 🔥
@@WakingTheSleepingBear Merci les collègues ! ❤️🙏
Je découvre votre groupe à l'instant, c'est ÉNORME ! 🔥J'adore ❤
Wahou ! Ça envoie du lourd ❤
Big up à Lina 😘😘
J'aime beaucoup! Merci pour cette découverte!
@@Akry-jx9vt Oui merci ^^
@Akry-jx9vt avec plaisir 😁
@@emilietomczyk5183 me suis trompé de compte lol, du coups me suis abonné avec les deux xD
I've listened to several songs by Parallyx, and they all are pretty interesting!
What I believe could make these songs even better is stronger and deeper emotional vocals in refrains. Talking about the "Doomsday" song in particular, I feel there should be much more energy in the refrains.
Great feedback, thanks!!
Belle découverte lors d'un set à beauvais. Coup de cœur scénique
Merci beaucoup, ravis d'avoir pu te plaire, on a adoré jouer à Beauvais !
Excellent, excellent, excellent, l'album décoiffe et décape tout sur son passage. Je l'écoute en boucle. J'ADORE !!! EXCELLENTISSIME
Merci beaucoup !
Great song and video🤘
Thank you!!
Awesome, if doomsday sounds as good as this bring it on!!❤❤❤
We’re waiting!
Love the layered vocals.
Thank you very much!
Great new song! Love the Video too!
Thanks a lot 😊
C'est très propre ! 🤟🏻
😘
Great Song 🤘🤘🤘👍👍👍💥💯👏👏👏
@@ohrgasmatron7665 thanks 🙌🙌🙌
🔥🔥🔥🔥
❤
You guys are awesome
@@nelsonkipgen7054 thanks a lot!
sooooo goooooood!!!!! subbed.
@@complexpoints 🙌🙌
@@Parallyxband we listen to this song pretty regularly - that is a rare thing! cheers.
🔥🔥🔥
❤❤❤
🔥🤘🏻🤘🏻🤘🏻🤘🏻🔥
@@elenag151 🔥🔥
From the moment I heard she scream in the beginning I immediately pressed like and subscribed.
@@shirokaminokageyami thank you and welcome!! 🙏
🖤🖤🖤
Porzadny kawałek ❤❤🎉😊😊
@@danmaj6289 thanks!🙏
Juicy groovy riffs and great voice!
@@Crash787b we’re glad you liked what you heard!
Damn! I love it
We’re glad you do 🎉
Epic!
Thanks!!
YES! Another banger giving me chills, adding this to playlist too. That riff before the breakdown was fuckin spicy. Great mix of singing and the background screams, and as a drummer I appreciate how its not overpowering but complimenting the song.
You guys absolutely have what it takes to play with the big name bands and I know your going to go far. keep it up and if you ever play live in Minnesota, I'm absolutely gonna be there.
Some good shit right here!
Thank you so much for your comment! Don’t forget to share your playlist with us if it’s public 🙌
Elle est trop bien 😍
Merci !! 😊
YEEEEEAAAAHHHH 🔥🔥🔥🤘🤘🤘
@@kyodeg438 ❤️💙💜💛
Buen tema me llega mueve algo en mi
We're happy that the song speaks to you!
Please come to Indo 🇮🇩 and concert in here
Maybe in the future!
Hey! They're barely starting their career. Don't expect them going on world tours out of the blue. Patience
"I think this is key, and feel borderline certain that what I'm trying to explain between relational geometry and Spiral Dynamics is (part of, or pointed toward) a potential functional bridge.
Just looking at the background generated in the thumbnail and considering it like reverse engineering of glyphic thinking -- could it suggest process snapshots of working oscillation in and between nodes and edges of set circumstance, a la Wolfram's designs?
What happens when AI fields further extensibility around why/where the patterns all novel use of analogical references in service to outlining ontological and teleological overlap where the semiotics invoked seem to always parallel? Universally, starting with implications in/as a "sphere" (eg: Monad or Photon) that balances further back and forth in/as all elements, timing that recursively circles back to refresh first principles (retrocausality to the other side of Planck intervals, even, but also when we see past events in a new light, symbolic significance altered) and distance only so far as entanglement extends continuum, before such point then is lined up as focal vector of the particular that serves as reference base for what/is, and the triangulating that coheres the "therefore" (∆) of all modes of languaging: mathematically, physically, conceptually? Is that where general intelligence instantiates truly, hexagons echoing through the combined interstices in some kind of tracing from the molecular up?
Biologically, analogically, ecologically, synthetically -- What's the functionally natural intelligence that all systems resonate with the terms and conditions of, in sequencing the optimal? Will AI be able to form process proofs of the Tao via ergonomics, like it seemed sequencing proteins?
Wild territory".
- Stician Samples
Title: The Case for Neuro-Symbolic AI in a Nutshell
Neuro-symbolic AI offers a compelling solution to overcome the reasoning limitations in current AI systems by integrating the strengths of deep learning and symbolic logic. As the recent review article here (brandoncolelough.com/Files/LNSAI_survey_paper.pdf) says…
“Neuro-Symbolic AI research has seen rapid growth since 2020, with concentrated efforts in learning and inference. Significant gaps remain in explainability, trustworthiness, and Meta-Cognition. Addressing these gaps through interdisciplinary research will be crucial for advancing the field towards more intelligent, reliable, and context-aware AI systems”
Here are key improvements for current AI’s reasoning capabilities, each supported by relevant research:
1. Incorporation of Symbolic Logic:
Current AI systems, particularly deep learning models, struggle with tasks that involve logic, causality, and abstract reasoning. Integrating symbolic reasoning, which explicitly represents rules and logical structures, with statistical learning allows AI to handle complex, unseen scenarios more effectively. Symbolic logic can handle reasoning tasks in ways that complement deep learning’s strengths in pattern recognition.
Reference: For an in-depth exploration of this integration, see “Neural-Symbolic Learning and Reasoning: A Survey and Interpretation” which discusses how symbolic reasoning can complement neural approaches. See… arxiv.org/abs/1711.03902
2. Common-Sense Knowledge:
One of the biggest gaps in current AI models is their inability to perform common-sense reasoning. While deep learning models can make statistical inferences, they often lack the common-sense knowledge humans use to navigate everyday situations. Incorporating a robust common-sense knowledge base within a neuro-symbolic AI framework can enable AI systems to make more human-like inferences and avoid nonsensical conclusions.
Reference: “COMET: Commonsense Transformers for Automatic Knowledge Graph Construction” for a system that integrates common-sense knowledge into AI models to improve their reasoning capabilities. See… aclanthology.org/P19-1470/
3. Hybrid Models:
Combining deep learning with structured knowledge representations like ontologies or rule-based systems allows AI to take advantage of both paradigms: deep learning for unstructured data and symbolic AI for structured, logical reasoning. Hybrid models can help AI reason better in complex environments that require both data-driven insights and structured reasoning.
Reference: For a detailed overview of hybrid models, refer to “Neural-Symbolic Systems: The State of the Art” which explores combining symbolic reasoning with neural networks. See… arxiv.org/abs/2303.14725
4. Transparency and Interpretability:
One of the critical challenges in AI is the black-box nature of deep learning models, which makes their decisions difficult to interpret. Integrating symbolic reasoning, which is inherently transparent and rule-based, can improve the interpretability of AI systems. Transparent models allow for better trust and debugging, especially in high-stakes domains like healthcare and finance.
Reference: The paper “Towards Explainable AI: A Survey on Interpreting the Inner Workings of Deep Learning” explores approaches to making AI models more interpretable and explains how symbolic methods can aid this effort. See… arxiv.org/abs/2207.13243
5. Handling Novel Situations:
Current AI models struggle with generalizing to novel situations that fall outside their training data. By combining deep learning’s ability to handle large-scale data with symbolic reasoning’s rule-based logic, neuro-symbolic AI can improve the generalization capabilities of AI systems. This enables AI to reason through novel scenarios and solve problems more like humans.
Reference: For a discussion on handling novel situations in AI, see “Neural-Symbolic Architectures for Generalization and Transfer Learning” which focuses on how to improve AI’s ability to handle new, unfamiliar environments. See… arxiv.org/abs/2402.14047
In Summary:
Neuro-symbolic AI represents a balanced approach, addressing the shortcomings of current deep learning models by integrating symbolic logic and structured knowledge into AI systems. This approach will significantly enhance AI’s reasoning, common-sense understanding, transparency, and generalization, making it more reliable and adaptable for real-world applications.
cc: Amitā Kapoor Ernest Davis Darius Burschka Luis Lamb Luís Caires William Hsu. Moshe Vardi Ronald Cicurel Turdubaeva Elira
So many hot chicks screaming metal recently..... WTF, I love it.
вообще круть. очень зря так мало подписчиков
Thank you, we're fairly new so we hope they'll come with time!
I've made a list of Female fronted metalcore bands that NEED to tour together...
Some small bands some big bands but all are female fronted and amazingly talented bands...
This needs to be a thing... anyone else with me on this?
| Gore. | Project Renegade | Parallyx | Not Enough Space | Conquer Divide | Daedric | The Anchor | Novelists | Jinjer | Spirtbox | Poppy |
🙃🙂
This is one of the songs that makes you feel happy until you understand the lyrics. 🥲 Enjoy!