DeepSeek R1 on how to build conscious AGI
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Knut Jägersberg
KnutJaegersberg
AI & ML interests
NLP, opinion mining, narrative intelligence
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upvoted
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about 8 hours ago
SmolVLM 256M & 500M
commented on
their
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about 10 hours ago
DeepSeek R1 on how to build conscious AGI
posted
an
update
about 18 hours ago
Artificial Kuramoto Oscillatory Neurons
Artificial Kuramoto Oscillatory Neurons (AKOrN) differ from traditional artificial neurons by oscillating, rather than just turning on or off. Each neuron is represented by a rotating vector on a sphere, influenced by its connections to other neurons. This behavior is based on the Kuramoto model, which describes how oscillators (like neurons) tend to synchronize, similar to pendulums swinging in unison.
Key points:
Oscillating Neurons: Each AKOrN’s rotation is influenced by its connections, and they try to synchronize or oppose each other.
Synchronization: When neurons synchronize, they "bind," allowing the network to represent complex concepts (e.g., "a blue square toy") by compressing information.
Updating Mechanism: Neurons update their rotations based on connected neurons, input stimuli, and their natural frequency, using a Kuramoto update formula.
Network Structure: AKOrNs can be used in various network layers, with iterative blocks combining Kuramoto layers and feature extraction modules.
Reasoning: This model can perform reasoning tasks, like solving Sudoku puzzles, by adjusting neuron interactions.
Advantages: AKOrNs offer robust feature binding, reasoning capabilities, resistance to adversarial data, and well-calibrated uncertainty estimation.
In summary, AKOrN's oscillatory neurons and synchronization mechanisms enable the network to learn, reason, and handle complex tasks like image classification and object discovery with enhanced robustness and flexibility.
yt
https://www.youtube.com/watch?v=i3fRf6fb9ZM
paper
https://arxiv.org/html/2410.13821v1
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KnutJaegersberg's activity
posted
an
update
about 18 hours ago
Post
589
Artificial Kuramoto Oscillatory Neurons
Artificial Kuramoto Oscillatory Neurons (AKOrN) differ from traditional artificial neurons by oscillating, rather than just turning on or off. Each neuron is represented by a rotating vector on a sphere, influenced by its connections to other neurons. This behavior is based on the Kuramoto model, which describes how oscillators (like neurons) tend to synchronize, similar to pendulums swinging in unison.
Key points:
Oscillating Neurons: Each AKOrN’s rotation is influenced by its connections, and they try to synchronize or oppose each other.
Synchronization: When neurons synchronize, they "bind," allowing the network to represent complex concepts (e.g., "a blue square toy") by compressing information.
Updating Mechanism: Neurons update their rotations based on connected neurons, input stimuli, and their natural frequency, using a Kuramoto update formula.
Network Structure: AKOrNs can be used in various network layers, with iterative blocks combining Kuramoto layers and feature extraction modules.
Reasoning: This model can perform reasoning tasks, like solving Sudoku puzzles, by adjusting neuron interactions.
Advantages: AKOrNs offer robust feature binding, reasoning capabilities, resistance to adversarial data, and well-calibrated uncertainty estimation.
In summary, AKOrN's oscillatory neurons and synchronization mechanisms enable the network to learn, reason, and handle complex tasks like image classification and object discovery with enhanced robustness and flexibility.
yt
https://www.youtube.com/watch?v=i3fRf6fb9ZM
paper
https://arxiv.org/html/2410.13821v1
Artificial Kuramoto Oscillatory Neurons (AKOrN) differ from traditional artificial neurons by oscillating, rather than just turning on or off. Each neuron is represented by a rotating vector on a sphere, influenced by its connections to other neurons. This behavior is based on the Kuramoto model, which describes how oscillators (like neurons) tend to synchronize, similar to pendulums swinging in unison.
Key points:
Oscillating Neurons: Each AKOrN’s rotation is influenced by its connections, and they try to synchronize or oppose each other.
Synchronization: When neurons synchronize, they "bind," allowing the network to represent complex concepts (e.g., "a blue square toy") by compressing information.
Updating Mechanism: Neurons update their rotations based on connected neurons, input stimuli, and their natural frequency, using a Kuramoto update formula.
Network Structure: AKOrNs can be used in various network layers, with iterative blocks combining Kuramoto layers and feature extraction modules.
Reasoning: This model can perform reasoning tasks, like solving Sudoku puzzles, by adjusting neuron interactions.
Advantages: AKOrNs offer robust feature binding, reasoning capabilities, resistance to adversarial data, and well-calibrated uncertainty estimation.
In summary, AKOrN's oscillatory neurons and synchronization mechanisms enable the network to learn, reason, and handle complex tasks like image classification and object discovery with enhanced robustness and flexibility.
yt
https://www.youtube.com/watch?v=i3fRf6fb9ZM
paper
https://arxiv.org/html/2410.13821v1
- 2 replies
replied to
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1 day ago
meaning making is always work!
we can discriminate against (partially) AI generated content, to our disadvantage. That's freedom of choice.
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update
2 days ago
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1263
DeepSeek R1 on how to build conscious AGI
https://huggingface.co./blog/KnutJaegersberg/deepseek-r1-on-conscious-agi
https://huggingface.co./blog/KnutJaegersberg/deepseek-r1-on-conscious-agi
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- 3 replies
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an
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5 days ago
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447
Yet another blog post about general intelligence
https://huggingface.co./blog/KnutJaegersberg/general-intelligence
https://huggingface.co./blog/KnutJaegersberg/general-intelligence
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AtAndDev's
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6 days ago
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1539
R1 is out! And with a lot of other R1 releated models...
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an
update
6 days ago
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1733
Understanding and Benchmarking Artificial Intelligence: OpenAI's o3 Is Not AGI
It's an interesting paper that argues "new approaches are required that can reliably solve a wide variety of problems without existing skills."
"It is therefore hoped that the benchmark outlined in this article contributes to further exploration of this direction of research and incentivises the development of new AGI approaches that focus on intelligence rather than skills."
https://arxiv.org/abs/2501.07458
It's an interesting paper that argues "new approaches are required that can reliably solve a wide variety of problems without existing skills."
"It is therefore hoped that the benchmark outlined in this article contributes to further exploration of this direction of research and incentivises the development of new AGI approaches that focus on intelligence rather than skills."
https://arxiv.org/abs/2501.07458
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prithivMLmods's
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12 days ago
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5885
Reasoning SmolLM2 🚀
🎯Fine-tuning SmolLM2 on a lightweight synthetic reasoning dataset for reasoning-specific tasks. Future updates will focus on lightweight, blazing-fast reasoning models. Until then, check out the blog for fine-tuning details.
🔥Blog : https://huggingface.co./blog/prithivMLmods/smollm2-ft
🔼 Models :
+ SmolLM2-CoT-360M : prithivMLmods/SmolLM2-CoT-360M
+ Reasoning-SmolLM2-135M : prithivMLmods/Reasoning-SmolLM2-135M
+ SmolLM2-CoT-360M-GGUF : prithivMLmods/SmolLM2-CoT-360M-GGUF
🤠 Other Details :
+ Demo : prithivMLmods/SmolLM2-CoT-360M
+ Fine-tune nB : prithivMLmods/SmolLM2-CoT-360M
🎯Fine-tuning SmolLM2 on a lightweight synthetic reasoning dataset for reasoning-specific tasks. Future updates will focus on lightweight, blazing-fast reasoning models. Until then, check out the blog for fine-tuning details.
🔥Blog : https://huggingface.co./blog/prithivMLmods/smollm2-ft
🔼 Models :
+ SmolLM2-CoT-360M : prithivMLmods/SmolLM2-CoT-360M
+ Reasoning-SmolLM2-135M : prithivMLmods/Reasoning-SmolLM2-135M
+ SmolLM2-CoT-360M-GGUF : prithivMLmods/SmolLM2-CoT-360M-GGUF
🤠 Other Details :
+ Demo : prithivMLmods/SmolLM2-CoT-360M
+ Fine-tune nB : prithivMLmods/SmolLM2-CoT-360M
reacted to
davanstrien's
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12 days ago
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3003
Introducing scandi-fine-web-cleaner
davanstrien/scandi-fine-web-cleaner, the first model trained on FineWeb-C community annotations!
FineWeb2 is a massive multilingual dataset for pre-training language models. Like any web-scale dataset, it contains low-quality content. How can we improve it?
Over the past months, an amazing community of 400+ annotators has been labelling content quality (using Argilla) across 23 languages through the FineWeb-C initiative.
Today, I'm happy to share the first classifier trained on this data.
🔍 What we've built:
- A lightweight classifier that efficiently removes low-quality content
- 90%+ precision demonstrated on Danish & Swedish
- Can process the 43M+ documents in Danish FineWeb2 with minimal compute
🌍 Why this matters: The approach can be reproduced for any of the 23 languages in FineWeb-C ( data-is-better-together/fineweb-c). We can improve training data quality at scale without massive compute resources by starting with community annotations and training small, efficient classifiers.
Want to build a classifier for your language? Check out the full blog post with code examples and implementation details: https://danielvanstrien.xyz/posts/2025/FineWeb-c/scandinavian-content-filtering-fineweb.html
FineWeb2 is a massive multilingual dataset for pre-training language models. Like any web-scale dataset, it contains low-quality content. How can we improve it?
Over the past months, an amazing community of 400+ annotators has been labelling content quality (using Argilla) across 23 languages through the FineWeb-C initiative.
Today, I'm happy to share the first classifier trained on this data.
🔍 What we've built:
- A lightweight classifier that efficiently removes low-quality content
- 90%+ precision demonstrated on Danish & Swedish
- Can process the 43M+ documents in Danish FineWeb2 with minimal compute
🌍 Why this matters: The approach can be reproduced for any of the 23 languages in FineWeb-C ( data-is-better-together/fineweb-c). We can improve training data quality at scale without massive compute resources by starting with community annotations and training small, efficient classifiers.
Want to build a classifier for your language? Check out the full blog post with code examples and implementation details: https://danielvanstrien.xyz/posts/2025/FineWeb-c/scandinavian-content-filtering-fineweb.html
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merve's
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12 days ago
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there's a new multimodal retrieval model in town 🤠
LlamaIndex released vdr-2b-multi-v1
> uses 70% less image tokens, yet outperforming other dse-qwen2 based models
> 3x faster inference with less VRAM 💨
> shrinkable with matryoshka 🪆
> can do cross-lingual retrieval!
Collection: llamaindex/visual-document-retrieval-678151d19d2758f78ce910e1 (with models and datasets)
Demo: llamaindex/multimodal_vdr_demo
Learn more from their blog post here https://huggingface.co./blog/vdr-2b-multilingual 📖
LlamaIndex released vdr-2b-multi-v1
> uses 70% less image tokens, yet outperforming other dse-qwen2 based models
> 3x faster inference with less VRAM 💨
> shrinkable with matryoshka 🪆
> can do cross-lingual retrieval!
Collection: llamaindex/visual-document-retrieval-678151d19d2758f78ce910e1 (with models and datasets)
Demo: llamaindex/multimodal_vdr_demo
Learn more from their blog post here https://huggingface.co./blog/vdr-2b-multilingual 📖
reacted to
s3nh's
post with ❤️
about 1 month ago
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1808
Welcome back,
Small Language Models Enthusiasts and GPU Poor oss enjoyers lets connect.
Just created an organization which main target is to have fun with smaller models tuneable on consumer range GPUs, feel free to join and lets have some fun, much love ;3
https://huggingface.co./SmolTuners
Small Language Models Enthusiasts and GPU Poor oss enjoyers lets connect.
Just created an organization which main target is to have fun with smaller models tuneable on consumer range GPUs, feel free to join and lets have some fun, much love ;3
https://huggingface.co./SmolTuners
- 3 replies
posted
an
update
about 1 month ago
Post
1364
Intelligence Potentiation: An Evolutionary Perspective on AI Agent Designs
I found it useful to think of AI agent design as progressing up a ladder, through evolutionary selection.
https://huggingface.co./blog/KnutJaegersberg/intelligence-potentiation
I found it useful to think of AI agent design as progressing up a ladder, through evolutionary selection.
https://huggingface.co./blog/KnutJaegersberg/intelligence-potentiation
reacted to
sayakpaul's
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about 2 months ago
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2099
Introducing a high-quality open-preference dataset to further this line of research for image generation.
Despite being such an inseparable component for modern image generation, open preference datasets are a rarity!
So, we decided to work on one with the community!
Check it out here:
https://huggingface.co./blog/image-preferences
Despite being such an inseparable component for modern image generation, open preference datasets are a rarity!
So, we decided to work on one with the community!
Check it out here:
https://huggingface.co./blog/image-preferences
- 7 replies
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ariG23498's
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about 2 months ago
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1405
We are blessed with another iteration of Pali Gemma. Google launches PaliGemma 2.
google/paligemma-2-release-67500e1e1dbfdd4dee27ba48
merve/paligemma2-vqav2
google/paligemma-2-release-67500e1e1dbfdd4dee27ba48
merve/paligemma2-vqav2
posted
an
update
about 2 months ago
Post
1268
Practical Consciousness Theory for AI System Design
Wrote a blog post about practical consciousness theory
https://huggingface.co./blog/KnutJaegersberg/practical-consciousness-theory
Wrote a blog post about practical consciousness theory
https://huggingface.co./blog/KnutJaegersberg/practical-consciousness-theory
posted
an
update
about 2 months ago
Post
1835
DrNicefellow/Qwen-QwQ-32B-Preview-4.25bpw-exl2
Rumor has it this is currently the best model for 24 GB VRAM local usage.
DrNicefellow/Qwen-QwQ-32B-Preview-4.25bpw-exl2
Rumor has it this is currently the best model for 24 GB VRAM local usage.
DrNicefellow/Qwen-QwQ-32B-Preview-4.25bpw-exl2
posted
an
update
2 months ago
Post
1111
openGPT-X/Teuken-7B-instruct-research-v0.4
New European LLM
openGPT-X/Teuken-7B-instruct-research-v0.4
New European LLM
openGPT-X/Teuken-7B-instruct-research-v0.4
posted
an
update
5 months ago
Post
1176
appvoid/arco
arco consistently outperforms every sota model below 600m parameters on average
appvoid/arco
arco consistently outperforms every sota model below 600m parameters on average
appvoid/arco
posted
an
update
5 months ago
Post
2240
Wrote a blog post with some ideas about prompt engineering
https://huggingface.co./blog/KnutJaegersberg/first-principles-prompt-engineering
https://huggingface.co./blog/KnutJaegersberg/first-principles-prompt-engineering
posted
an
update
6 months ago
Post
2315
mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq
99% of the performance across various benchmarks!
mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq
99% of the performance across various benchmarks!
mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq