These are the most helpfull/interesting channels for me now:
https://tttttt.me/axisofordinary
https://tttttt.me/j_links
https://tttttt.me/ComplexSys
https://tttttt.me/emptyset_of_ideas
https://tttttt.me/brenoritvrezorkre_channel
https://tttttt.me/AI_DeepLearning
https://tttttt.me/axisofordinary
https://tttttt.me/j_links
https://tttttt.me/ComplexSys
https://tttttt.me/emptyset_of_ideas
https://tttttt.me/brenoritvrezorkre_channel
https://tttttt.me/AI_DeepLearning
Telegram
Axis of Ordinary
Memetic and cognitive hazards.
Substack: https://axisofordinary.substack.com/
Substack: https://axisofordinary.substack.com/
Researching Deep Meaning Of
These are the most helpfull/interesting channels for me now: https://tttttt.me/axisofordinary https://tttttt.me/j_links https://tttttt.me/ComplexSys https://tttttt.me/emptyset_of_ideas https://tttttt.me/brenoritvrezorkre_channel https://tttttt.me/AI_DeepLearning
Telegram
Pathetic low-frequenciers
That's my personal channel of some crazy stuff. Daily I see a lot of strange things across the internet, so I decided to publish some of them here. Beware of: weird math, crazy pics, cybernercophilia, nerdish humor.
Forwarded from Axis of Ordinary
by Matthew Barnett
Some improvements we might start to see more in large language models within 2 years:
- Explicit memory that will allow it to retrieve documents and read them before answering questions https://arxiv.org/abs/2112.04426
- A context window of hundreds of thousands of tokens, allowing the model to read and write entire books https://arxiv.org/abs/2202.07765
- Dynamic inference computation that depends on the difficulty of the query, allowing the model to "think hard" about difficult questions before spitting out an answer https://arxiv.org/abs/2207.07061
- Alignment principles that help the model produce more reliable and more useful output than naive RLHF, such as Anthropic's "Constitutional AI" approach https://www.anthropic.com/constitutional.pdf
Some improvements we might start to see more in large language models within 2 years:
- Explicit memory that will allow it to retrieve documents and read them before answering questions https://arxiv.org/abs/2112.04426
- A context window of hundreds of thousands of tokens, allowing the model to read and write entire books https://arxiv.org/abs/2202.07765
- Dynamic inference computation that depends on the difficulty of the query, allowing the model to "think hard" about difficult questions before spitting out an answer https://arxiv.org/abs/2207.07061
- Alignment principles that help the model produce more reliable and more useful output than naive RLHF, such as Anthropic's "Constitutional AI" approach https://www.anthropic.com/constitutional.pdf
Forwarded from Complex Systems Studies
Complex systems in the spotlight: next steps after the 2021 Nobel Prize in Physics
The 2021 Nobel Prize in Physics recognized the fundamental role of complex systems in the natural sciences. In order to celebrate this milestone, this editorial presents the point of view of the editorial board of JPhys Complexity on the achievements, challenges, and future prospects of the field. To distinguish the voice and the opinion of each editor, this editorial consists of a series of editor perspectives and reflections on few selected themes. A comprehensive and multi-faceted view of the field of complexity science emerges. We hope and trust that this open discussion will be of inspiration for future research on complex systems.
📎 https://iopscience.iop.org/article/10.1088/2632-072X/ac7f75
The 2021 Nobel Prize in Physics recognized the fundamental role of complex systems in the natural sciences. In order to celebrate this milestone, this editorial presents the point of view of the editorial board of JPhys Complexity on the achievements, challenges, and future prospects of the field. To distinguish the voice and the opinion of each editor, this editorial consists of a series of editor perspectives and reflections on few selected themes. A comprehensive and multi-faceted view of the field of complexity science emerges. We hope and trust that this open discussion will be of inspiration for future research on complex systems.
📎 https://iopscience.iop.org/article/10.1088/2632-072X/ac7f75
s41586-022-05434-1.pdf
1.5 MB
Suppressing quantum errors by scaling a surface code logical qubit by Google Quantum AI
Forwarded from Complex Systems Studies
ncomms5213.pdf
653.3 KB
A variational eigenvalue solver on a photonic quantum processor