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124
How much oil does the us use a day?
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DrHouseFan-315
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reacted to 4455henley's post with 🤯 6 days ago
How much oil does the us use a day? published a model 11 days ago
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reactedto 4455henley's post with 🤯 6 days ago
reactedto Nymbo's post with 👍 6 days ago
Post
6142
We should really have a release date range slider on the /models page. Tired of "trending/most downloaded" being the best way to sort and still seeing models from 2023 on the first page just because they're embedded in enterprise pipelines and get downloaded repeatedly. "Recently Created/Recently Updated" don't solve the discovery problem considering the amount of noise to sift through.
Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.
Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.
- 3 replies
reactedto marksverdhei's post with 😔 about 1 month ago
Post
4578
Poll: Will 2026 be the year of subquadratic attention?
The transformer architecture is cursed by its computational complexity.
It is why you run out of tokens and have to compact. But some would argue that this is a feature not a bug and that this is also why these models are so good. We've been doing a lot of research on trying to make equally good models that are computationally cheaper, But so far, none of the approaches have stood the test of time. Or so it seems.
Please vote, don't be shy. Remember that the Dunning-Kruger effect is very real, so the person who knows less about transformers than you is going to vote. We want everyone's opinion, no matter confidence.
👍 if you think at least one frontier model* will have no O(n^2) attention by the end of 2026
🔥 If you disagree
* Frontier models - models that match / outperform the flagship claude, gemini or chatgpt at the time on multiple popular benchmarks
The transformer architecture is cursed by its computational complexity.
It is why you run out of tokens and have to compact. But some would argue that this is a feature not a bug and that this is also why these models are so good. We've been doing a lot of research on trying to make equally good models that are computationally cheaper, But so far, none of the approaches have stood the test of time. Or so it seems.
Please vote, don't be shy. Remember that the Dunning-Kruger effect is very real, so the person who knows less about transformers than you is going to vote. We want everyone's opinion, no matter confidence.
👍 if you think at least one frontier model* will have no O(n^2) attention by the end of 2026
🔥 If you disagree
* Frontier models - models that match / outperform the flagship claude, gemini or chatgpt at the time on multiple popular benchmarks
- 4 replies
reactedto Alexander1337's post with 🔥🚀🧠👀 about 1 month ago
Post
1460
host website
reactedto kanaria007's post with 😔 2 months ago
Post
2225
✅ New Article: *Designing, Safeguarding, and Evaluating Learning Companions* (v0.1)
Title:
🛡️ Designing, Safeguarding, and Evaluating SI-Core Learning Companions
🔗 https://huggingface.co/blog/kanaria007/designing-safeguarding-and-evaluating
---
Summary:
Most “AI tutoring” talks about prompts, content, and engagement graphs.
But real learning companions—especially for children / ND learners—fail in quieter ways: *the system “works” while stress rises, agency drops, or fairness erodes.*
This article is a practical playbook for building SI-Core–wrapped learning companions that are *goal-aware (GCS surfaces), safety-bounded (ETH guardrails), and honestly evaluated (PoC → real-world studies)*—without collapsing everything into a single score.
> Mastery is important, but not the only axis.
> *Wellbeing, autonomy, and fairness must be first-class.*
---
Why It Matters:
• Replaces “one number” optimization with *goal surfaces* (and explicit anti-goals)
• Treats *child/ND safety* as a runtime policy problem, not a UX afterthought
• Makes oversight concrete: *safe-mode, human-in-the-loop, and “Why did it do X?” explanations*
• Shows how to evaluate impact without fooling yourself: *honest PoCs, heterogeneity, effect sizes, ethics of evaluation*
---
What’s Inside:
• A practical definition of a “learning companion” under SI-Core ([OBS]/[ID]/[ETH]/[MEM]/PLB loop)
• GCS decomposition + *age/context goal templates* (and “bad but attractive” optima)
• Safety playbook: threat model, *ETH policies*, ND/age extensions, safe-mode patterns
• Teacher/parent ops: onboarding, dashboards, contestation/override, downtime playbooks, comms
• Red-teaming & drills: scenario suites by age/context, *measuring safety over time*
• Evaluation design: “honest PoC”, day-to-day vs research metrics, ROI framing, analysis patterns
• Interpreting results: *effect size vs p-value*, “works for whom?”, go/no-go and scale-up stages
---
📖 Structured Intelligence Engineering Series
Title:
🛡️ Designing, Safeguarding, and Evaluating SI-Core Learning Companions
🔗 https://huggingface.co/blog/kanaria007/designing-safeguarding-and-evaluating
---
Summary:
Most “AI tutoring” talks about prompts, content, and engagement graphs.
But real learning companions—especially for children / ND learners—fail in quieter ways: *the system “works” while stress rises, agency drops, or fairness erodes.*
This article is a practical playbook for building SI-Core–wrapped learning companions that are *goal-aware (GCS surfaces), safety-bounded (ETH guardrails), and honestly evaluated (PoC → real-world studies)*—without collapsing everything into a single score.
> Mastery is important, but not the only axis.
> *Wellbeing, autonomy, and fairness must be first-class.*
---
Why It Matters:
• Replaces “one number” optimization with *goal surfaces* (and explicit anti-goals)
• Treats *child/ND safety* as a runtime policy problem, not a UX afterthought
• Makes oversight concrete: *safe-mode, human-in-the-loop, and “Why did it do X?” explanations*
• Shows how to evaluate impact without fooling yourself: *honest PoCs, heterogeneity, effect sizes, ethics of evaluation*
---
What’s Inside:
• A practical definition of a “learning companion” under SI-Core ([OBS]/[ID]/[ETH]/[MEM]/PLB loop)
• GCS decomposition + *age/context goal templates* (and “bad but attractive” optima)
• Safety playbook: threat model, *ETH policies*, ND/age extensions, safe-mode patterns
• Teacher/parent ops: onboarding, dashboards, contestation/override, downtime playbooks, comms
• Red-teaming & drills: scenario suites by age/context, *measuring safety over time*
• Evaluation design: “honest PoC”, day-to-day vs research metrics, ROI framing, analysis patterns
• Interpreting results: *effect size vs p-value*, “works for whom?”, go/no-go and scale-up stages
---
📖 Structured Intelligence Engineering Series
reactedto aposadasn's post with 🚀 6 months ago
Post
2583
My team at 
arclabmit created a robotic teleoperation and learning software for controlling robots, recording datasets, and training physical AI models, which is compatible with 
lerobot . This work was part of a paper we published to ICCR Kyoto 2025. Check out or code here: https://github.com/ARCLab-MIT/beavr-bot/tree/main
Our work aims to solve two key problems in the world of robotic manipulation:
1. The lack of a well-developed, open-source, accessible teleoperation system that can work out of the box.
2. No performant end-to-end control, recording, and learning platform for robots that is completely hardware agnostic.
If you are curious to learn more or have any questions please feel free to reach out!
Paper: BEAVR: Bimanual, multi-Embodiment, Accessible, Virtual Reality Teleoperation System for Robots (2508.09606)
Our work aims to solve two key problems in the world of robotic manipulation:
1. The lack of a well-developed, open-source, accessible teleoperation system that can work out of the box.
2. No performant end-to-end control, recording, and learning platform for robots that is completely hardware agnostic.
If you are curious to learn more or have any questions please feel free to reach out!
Paper: BEAVR: Bimanual, multi-Embodiment, Accessible, Virtual Reality Teleoperation System for Robots (2508.09606)
- 1 reply
reactedto yjernite's post with 😔 6 months ago
Post
2854
Tremendous quality of life upgrade on the Hugging Face Hub - we now have auto-complete emojis 🤗 🥳 👏 🙌 🎉
Get ready for lots more very serious analysis on a whole range of topics from yours truly now that we have unlocked this full range of expression 😄 🤔 🗣 🙊
Get ready for lots more very serious analysis on a whole range of topics from yours truly now that we have unlocked this full range of expression 😄 🤔 🗣 🙊
reactedto TuringsSolutions's post with 👀 7 months ago
Post
930
Maybe that post I showed the other day with my Hyperbolic Embeddings getting to perfect loss with RAdam was a one-time fluke, bad test dataset, etc.? Anotha' one! I gave it a test set a PhD student would struggle with. This model is a bit more souped up. Major callouts of the model: High Dimensional Encoding (HDC), Hyperbolic Embeddings, Entropix. Link to the Colab Notebook: https://colab.research.google.com/drive/1mS-uxhufx-h7eZXL0ZwPMAAXHqSeGZxX?usp=sharing
- 26 replies
reactedto hba123's post with 🔥 8 months ago
Post
2355
I am happy to announce that Ark now supports the following robots:
1. Franka Panda
2. Kuka LWR
3. UFactory XArm
4. Husky Robot
Everything is done in Python. You can even control your robot from a Jupiter notebook.
Check out the tutorials: https://arkrobotics.notion.site/ARK-Home-22be053d9c6f8096bcdbefd6276aba61
Check out the code: https://github.com/orgs/Robotics-Ark/repositories
Check out the documentation: https://robotics-ark.github.io/ark_robotics.github.io/docs/html/index.html
Check out the paper: https://robotics-ark.github.io/ark_robotics.github.io/static/images/ark_framework_2025.pdf
Hope you find it useful. Let us know if you want a specific feature! We would love to support you 😄
1. Franka Panda
2. Kuka LWR
3. UFactory XArm
4. Husky Robot
Everything is done in Python. You can even control your robot from a Jupiter notebook.
Check out the tutorials: https://arkrobotics.notion.site/ARK-Home-22be053d9c6f8096bcdbefd6276aba61
Check out the code: https://github.com/orgs/Robotics-Ark/repositories
Check out the documentation: https://robotics-ark.github.io/ark_robotics.github.io/docs/html/index.html
Check out the paper: https://robotics-ark.github.io/ark_robotics.github.io/static/images/ark_framework_2025.pdf
Hope you find it useful. Let us know if you want a specific feature! We would love to support you 😄
- 6 replies
reactedto ghostai1's post with 😔 9 months ago
Post
1508
# The future trends of Explainable AI in 2024
The world of artificial intelligence (AI) is constantly evolving, with new advancements and applications emerging every day. One trend that has captured the attention of many is Explainable AI. As the name suggests, this revolutionary technology aims to provide a clear, understandable explanation for the decisions and actions taken by AI systems.
In the future, Explainable AI is expected to become even more sophisticated, with advanced algorithms and techniques being developed to better interpret and analyze the vast amounts of data generated by AI systems. This will not only make AI systems more reliable and trustworthy, but it will also help to demystify the world of AI, making it more accessible to a wider audience.
As the demand for AI solutions grows, the need for Explainable AI will become increasingly important. Businesses, governments, and individuals will require clear, concise explanations for the AI systems they are using, ensuring that every decision made is transparent and easily understood.
The advancements in Explainable AI will also pave the way for new applications of AI technology, opening up a world of possibilities in fields such as healthcare, education, and transportation. From diagnosing medical conditions to improving traffic flow, Explainable AI is poised to revolutionize the way we live and work, providing us with the tools we need to tackle the complex challenges of the modern world.
So, as we step into the future of AI, let's embrace the power of Explainable AI, and ensure that our AI systems are not only powerful and efficient, but also transparent and easy to understand.
The world of artificial intelligence (AI) is constantly evolving, with new advancements and applications emerging every day. One trend that has captured the attention of many is Explainable AI. As the name suggests, this revolutionary technology aims to provide a clear, understandable explanation for the decisions and actions taken by AI systems.
In the future, Explainable AI is expected to become even more sophisticated, with advanced algorithms and techniques being developed to better interpret and analyze the vast amounts of data generated by AI systems. This will not only make AI systems more reliable and trustworthy, but it will also help to demystify the world of AI, making it more accessible to a wider audience.
As the demand for AI solutions grows, the need for Explainable AI will become increasingly important. Businesses, governments, and individuals will require clear, concise explanations for the AI systems they are using, ensuring that every decision made is transparent and easily understood.
The advancements in Explainable AI will also pave the way for new applications of AI technology, opening up a world of possibilities in fields such as healthcare, education, and transportation. From diagnosing medical conditions to improving traffic flow, Explainable AI is poised to revolutionize the way we live and work, providing us with the tools we need to tackle the complex challenges of the modern world.
So, as we step into the future of AI, let's embrace the power of Explainable AI, and ensure that our AI systems are not only powerful and efficient, but also transparent and easy to understand.
- 1 reply
reactedto BFFree's post with 😔😔 9 months ago
Post
3070
Working on some chess set concepts. I went towards minimal sculpted shapes then returned to some traditionalism.
- 1 reply
reactedto nicolay-r's post with 👀 about 1 year ago
Post
1684
The Concept behind xLSTM has recently turn into the xLSTM-7B model that showcase the performance in the category of the similar-scale Gemma 7B, LLama2 7B, FlaconMamba 7B but with higher performing Inference Kernel
Model: NX-AI/xLSTM-7b
Paper: https://arxiv.org/abs/2503.13427
Model: NX-AI/xLSTM-7b
Paper: https://arxiv.org/abs/2503.13427
- 1 reply
repliedto their post about 1 year ago
you act like you think you've met me before thouh
repliedto their post about 1 year ago
just for clarification, who do you think I am?
repliedto their post about 1 year ago
why did you make this metaphorical? i was asking about the llm model