The death of privacy is definitely a more fundamental issue here, outside of politics. In an ideal world, private citizens, even if they have notorious views, have a right to privacy.
I don't personally think there's that much value to this argument. Compare, for instance the consumption of 1 hour of tv vs. 1 hour of GPT usage:
A single AI chat message can consume 0.34 watt-hours of energy (1). So, let's say a hundred messages in an hour (quite an aggressive session) would be 34 watt-hours of energy.
An LCD TV running for an hour consumes about 100 watt-hours of energy, depending on size, LED, vs. OLED etc. (2).
I think AI does help people do better research faster, which is a significant uplift to humanity, while I do not see anyone specifically curbing their TV usage. We should probably focus our effrots on helping people use AI better and meanwhile build more nuclear energy plants, imo.
- In the "Main capabilities evaluations" section, the 120b outperform o3-mini and approaches o4 on most evals. 20b model is also decent, passing o3-mini on one of the tasks.
- AIME 2025 is nearly saturated with large CoT
- CBRN threat levels kind of on par with other SOTA open source models. Plus, demonstrated good refusals even after adversarial fine tuning.
- Interesting to me how a lot of the safety benchmarking runs on trust, since methodology can't be published too openly due to counterparty risk.
> The central finding is that a 15% increase in solar generation across the U.S. is associated with an annual reduction of 8.54 million metric tons (MMT) of CO2, a significant step toward national climate goals.
> Despite being sparse, NSA surpasses Full Attention baseline on average across general benchmarks, long-context tasks, and reasoning evaluation.
Isn't it very notable that the latency improvement didn't have a performance loss? I'm not super familiar with all the technical aspects, but that seems like it should be one of the main focuses of the paper.
In things that I am comparatively good at (e.g., coding), I can see that it helps 'raise the ceiling' as a result of allowing me to complete more of the low level tasks more effectively. But it is true as well that it hasn't raised my personal bar in capability, as far as I can measure.
When it comes to things I am not good at at, it has given me the illusion of getting 'up to speed' faster. Perhaps that's a personal ceiling raise?
I think a lot of these upskilling utilities will come down to delivery format. If you use a chat that gives you answers, don't expect to get better at that topic. If you use a tool that forces you to come up with answers yourself and get personalized validation, you might find yourself leveling up.
This is interesting, but I wonder how reliable this type of monitoring is really going to be in the long run. There are fairly strong indications that CoT adherence can be trained out of models, and there's already research showing that they won't always reveal their thought process in certain topics.
On a related note, if anyone here is also reading a lot of papers to keep up with AI safety, what tools have been helpful for you? I'm building https://openpaper.ai to help me read papers more effectively without losing accuracy, and looking for more feature tuning. It's also open source :)
We need to distinctly think about what tasks are actually suitable for LLMs. Used poorly, they'll gut our abilities to think thoughtfully. The push, IMO, should be for using them for verification and clarification, but not for replacements in understanding and creativity.
Example: Do the problem sets yourself. If you're getting questions wrong, dig deeper with an AI assistant to find gaps in your knowledge. Do NOT let the AI do the problem sets first.
I think it was similar to how we used calculators in school in the 2010s at least. We learned the principles behind the formulae and how to do them manually, before introducing the calculators to abstract the usage of the tools.
I've let that core principle shape some of how we're designing our paper-reading assistant, but still thinking through the UX patterns -- https://openpaper.ai/blog/manifesto.
Semantic Reader looks really cool! Just gave it a spin. Unless I'm mistaken, it seems like their AI assistive features are more designed for efficient skimming and inline-citation lookup in the PDF, rather than open-ended question/answering. Really love their design.
My approach is for user-driven highlights with AI help. As in, you can upload your paper and directly ask questions ("Why did they only include the HotPotQA dataset?", "What scores did they achieve with the fine-tuned model?", etc). When the AI responds, it provides citations inline to reference text. You can then click on the reference text to take you there in the doc.
Ah, good question! I'd say not quite. Managing a knowledge bank of your papers is a core feature, but I don't think I'd want to get _as_ extensive as Zotero (being your entire knowledge storage space/citations generation).
Yes! We can definitely help with this. Khoj lets you chat with your documents, indexing your private knowledge base for local RAG with any open source (or foundation) model.
You can make it as 'fancy' as you want, and use speech-to-text, image generation, web scraping, custom agents.
Let me know if you run into any issues? I'd love to get this setup for senior citizens! You can reach me at saba at khoj.dev.
My absolute favorite for hand-drawn-esque elements is Excalidraw (https://excalidraw.com/). Immediately became enamored by the UX and the ability to get done what I was looking for - architecture diagrams and low fidelity mock ups. It stores everything locally on your browser, which makes it easy to work offline as well.
Khoj will allow you to plug in your Obsidian vault or any plaintext files on your machine or Notion workspace. After you share the relevant data, it creates embeddings and uses it for RAG, so you get appropriately contextual responses with your LLM.
I responded in the issue, but I'll paste here as well for those also curious:
Khoj does not collect any search or chat queries. As mentioned in the docs, you can see our telemetry server[1]. If you see anything amiss, point it out to me and I'll hotfix it right away. You can see all the telemetry metadata right here[2].
Configuration with the `docker-compose` setup is a little bit particular, see the issue^ for details.
Thanks for the reference points for GPU integration! Just to clarify, we do use GPU optimization for indexing, but not for local chat with Llama. We're looking into getting that working.