sabaimran
Joined 4 years ago
Karma
107
Submissions
48
Member since
Nov 1
https://twitter.com/sabaimran_go
https://www.upbeatmoon.com/
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Comment · 9 months ago
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.
Comment · 1 year ago
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.
(1): https://epoch.ai/gradient-updates/how-much-energy-does-chatg...
(2): https://santannaenergyservices.com/how-many-watts-does-a-tv-...
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And then consider the amount of energy traditionally required by one human to do the same research tasks. Also quite significant.
I think we should be focused on making the more efficient, for sure! But I don't buy that the arguments based on energy consumption are very strong.
Comment · 1 year ago
Related, link to the GH repo: https://github.com/openai/harmony
Comment · 1 year ago
Super excited to see these released!
Major points of interest for me:
- 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.
Model cards with some of my annotations: https://openpaper.ai/paper/share/7137e6a8-b6ff-4293-a3ce-68b...
Comment · 1 year ago
Do subagents run in parallel?
Comment · 1 year ago
> 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.
Whoa, that's really cool.
You can see the paper along with figures & regional breakdowns here: https://openpaper.ai/paper/share/1d0c6956-4820-4ee2-ac1e-12c...
Comment · 1 year ago
> 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.
Comment · 1 year ago
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.
Comment · 1 year ago
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.
See: https://arxiv.org/pdf/2305.04388
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 :)
Comment · 1 year ago
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.