1/ The Fairness of High-Skilled Immigrants Act, 2019, or #HR1044/#S386, which would've removed country caps on green cards in the US for Indian and Chinese nationals, particularly bringing the wait time for Indians from 150yrs to ~10...
2/ ... was blocked in the Senate by Sen. Dave Perdue after bipartisan support in the House. If you were Indian and moved to the US for an undergraduate degree in 2001, you'd be 36, have spent half your life in the country and not have a green card.
3/ You might be married with kids but if you lose your job, you might have to leave your family after paying for a college degree and 14yrs worth of usually fairly high taxes. Isn't that absurd?
4/ Despite being Indian, and a beneficiary of this bill, there are problems with this bill. One, most Indians in the backlog are not high skilled tech workers, but cheap outsourced labour from IT consultancies like Wipro and Infosys.
5/ Two, without a smoother cap removal transition plan, this would essentially flood the green card quota with Indians for the next ~10yrs, throttling competent candidates of other nationalities.
6/ If those two issues are fully addressed, I this bill will be unanimously favored and @sendavidperdue will let it pass and hopefully Trump will sign it!
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HUGE Immigration News! The birthright citizenship executive order Trump signed today means children of non-citizens or non-permanent residents will not be US citizens.
Children of Indians who are in the 100+yr green card wait WILL NOT be US citizens.
I think the claim that I am somehow happy about this is preposterous. This is a terrible injustice to millions of legal immigrants who have gainfully contributed to the US for decades, held back solely because of their country of birth.
HUGE: DeepSeek R1 from China reaches o1 level performance! Most important paper of 2025.
They show that using pure RL using GRPO can teach models to reason better and better with more tokens: the first at this scale.
Here are the 4 top research learnings from the paper:
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1. Distilling more powerful models like R1 on smaller base models (Qwen / Llama) outperforms large scale RL training on those base models themselves (like QwQ-32B).
2/5
2. The upper bound of model performance requires even more powerful base models and larger scale RL to break out of the plateau.
3/5
— It's been 2yrs since the priority date has moved past Feb '22
— Indian EB-1 petitions stayed flat this year, at ~12k
— The backlog moved forward by ~200/mo.
The EB-1 backlog is basically stuck.
Let's break it down..
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What is the EB-1?
Indians under the standard EB-2/3 green card category have a 100+yr long wait. This is because of the 7% country cap by category.
This was the initial misconstrued old remark by Sriram that was taken out of context and set off this whole H-1B drama.
2/12
What is the EB-1?
The EB-1 is one of the few faster options. 3 categories: extraordinary ability, researchers and multinational managers.
Those in the EB-2/3 line can transfer their priority date while "upgrading" to an EB-1. This CAN stagnate the line from moving forward.
The Superhuman AI for Poker is a timeless research paper.
Poker, unlike Go/Chess, is an imperfect info game. Pluribus, made by now OpenAI engineer, beat 5 pros decisively in 10,000 poker hands, winning 48 milli big blinds/game.
Made a mini demo on rock-paper-scissors too:
1/5
Like humans, Pluribus confirms that limping, calling the “big blind” rather than folding or raising, is suboptimal.
However it disagrees with the wisdom that “donk betting” (starting a round by betting when one ended the previous betting round with a call) is a mistake.
2/5
Pluribus uses Monte Carlo counterfactual regret minimization (MCCFR) which samples actions and adjusts their probabilities, unlike MCTS for Go/ Chess.
It uses <128GB memory on 3 Intel Haswell CPUs and takes 20s/hand, half the time of a human.