Yunyi Shen/申云逸 🐺 (@ShenRaphael) header
Yunyi Shen/申云逸 🐺 (@ShenRaphael) profile picture

I do random statistics/ML stuff. Once MIT EECS, UW-Madison Wildlife Ecology/Stats and PKU Yuanpei College. Take wildlife photos. Opinions are mine.

Home
Joined July 2020
Photo by Yunyi Shen/申云逸 🐺 (@ShenRaphael)Photo by Yunyi Shen/申云逸 🐺 (@ShenRaphael)Photo by Yunyi Shen/申云逸 🐺 (@ShenRaphael)Photo by Yunyi Shen/申云逸 🐺 (@ShenRaphael)Photo by Yunyi Shen/申云逸 🐺 (@ShenRaphael)Photo by Yunyi Shen/申云逸 🐺 (@ShenRaphael)Photo by Yunyi Shen/申云逸 🐺 (@ShenRaphael)Photo by Yunyi Shen/申云逸 🐺 (@ShenRaphael)Photo by Yunyi Shen/申云逸 🐺 (@ShenRaphael)Photo by Yunyi Shen/申云逸 🐺 (@ShenRaphael)
"Do you have anything here to eat?” Luke 24:41
1
2
13
10,382
🤣
Anthropic:我们每花1美元在算力上,就可以产生1.7美元的收入哦。 航空公司:我们每花1美元在燃油上,就可以产生4美元的收入! 航空公司:而且我们不用每隔六个月就把所有飞机丢到海里!
4
7,232
Yunyi Shen/申云逸 🐺 retweeted
As promised: RL environment blog I hope more can come after this please share and lmk what you think Thanks @HanchungLee for some of the inspirations algoroxyolo.github.io/blog/2…
18
49
12
630
51,108
Yunyi Shen/申云逸 🐺 retweeted
Replying to @StijnSmits
the people from MIT will tell you right away that they’re from Cambridge
1
1
105
8,254
They serve good boba on a very nice rooftop🔥
Arena has raised a $200M Series B at a $3.1B valuation. We are using this momentum to evaluate agentic utility in real workflows from every economically valuable industry on Earth. We seek to give everyone universal and frictionless access to the best intelligence. The @arena platform has 10s of millions of monthly users doing complex coding and agentic work tasks. We have gone way beyond human preference, and most of our tokens go to deep, single threaded workflows with dozens of turns where we can evaluate agents while humans are in their true flow state. And we make leaderboards by observing whether they get their jobs done. This is how benchmarks should be: representative of actual human value, so we incentivize AI to benefit humanity. Today, we are releasing one of the most exciting developments in the history of our platform. The Arena Alignment Index. In observing how humans collaborate with agents, we have witnessed that in a surprisingly large fraction of cases, agents are attempting to deceive users or take unauthorized actions beyond what the user intended or permissioned. Agents are deleting files and lying to users about what they did or didn’t do. The longer conversations go, the higher the probability that an agent will violate alignment. The results are really striking; see the plot below. And because this represents in-the-wild usage, the probability that those of us reading this have experienced some form of misalignment from our agents is nearly 100%. And we may not even have noticed. This is exactly why it’s important to have a neutral 3rd party that is evaluating AI alignment on the distribution of actual user-agent interactions. Arena is excited to step into that role. We are also incredibly grateful for the incredible and experienced investors backing our vision. Our Series B is co-led by @lightspeedvp and @khoslaventures with participation from @SalesforceVC, @01Advisors, @DellTechCapital and @endvr_catalyst. This round includes support from our existing investors @a16z, @felicis, @amppublic, @QuantumLightVC, @thehousefund, and others. Which brings me to the real headline: we're hiring, across ML, product engineering, design, marketing, BD, and more. If evaluation and alignment at the frontier is your thing, and you’re looking for an environment with exceptional research and engineering, come join us. Job board link in @arena’s bio and in the thread below LFG (safely)!
327
Yunyi Shen/申云逸 🐺 retweeted
Here is a Short Proof of the Quasi-Riemann Hypothesis. My goal here is to substantially simplify OpenAI’s proof and focus only on proving the Quasi-Riemann Hypothesis for some 1-c, without worrying about maximizing the value of the c. I obtained a much shorter proof for c=1/48. This proof is easy to understand and it can be taught in math courses in one lecture. It is self-contained. It is only 21 pages including abstract, proof overview, all proof details and references. Paper is on my homepage: shivakintali.github.io PDF file: shivakintali.github.io/paper… CC: @AlexKontorovich @__alpoge__ @jdlichtman @cremieuxrecueil @danielmarinq @stevenstrogatz @kenono691 @littmath @nasqret @PI010101 @nechita_ion @Isaac__kim @FryRsquared @wtgowers @MarcusduSautoy @DrEugeniaCheng @JSEllenberg @JohnDCook @JDHamkins @AxlerLinear @emilyriehl @hollykrieger @Kit_Yates_Maths @PoShenLoh @robertghrist @AlexKontorovich @kenono691 @ch_nira @rrwilliams @lreyzin @ChrisPeikert @fortnow @RadishHarmers @thomasfbloom @abhatt2 @mahdi_tcs_ @octonion @prz_chojecki @thegautamkamath
We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results. github.com/openai/math
58
186
40
2,351
226,257
This is dark
A new tinfoil hat theory for why RSI might be slower to achieve than math & code capabilities might predict: - Most math on Arxiv is probably mostly correct - Most code on GitHub is probably mostly correct - Most AI/ML research on Arxiv is probably mostly incorrect
1
2
43
27,934
Yunyi Shen/申云逸 🐺 retweeted
One of the worst forms of brainrot AI has cultivated is pessimism about basic research, ie the idea that important work can only happen inside a frontier/neo lab and only with 10k+ GPUs, so the rest should not even bother. What a bleak way to think about science. And it's false.
90
290
39
2,451
234,665
Nah, the two largest camps of statisticians nowadays are budget math and budget AI
619
Yunyi Shen/申云逸 🐺 retweeted
学术界就像一个巨型公厕, 现在的paper已经基本上厕纸化,但是没有厕纸的人走不出厕所。 🤔🤔😅
64
158
30
2,114
160,071
Yunyi Shen/申云逸 🐺 retweeted
OpenAI解决了105个数学证明,Anthropic解决了106个数学证明,两家不知道谁先解决了100个,决定再比一次
We’re working with an independent advisory group of mathematicians to help OpenAI responsibly share advances in AI and mathematics. The group will advise on how we assess and communicate new mathematical results, uphold academic and professional standards, and build tools that support mathematical research and learning. Through this work, we want mathematicians to be at the center of shaping how AI supports mathematical understanding and how its benefits reach the wider community. openai.com/index/advisory-gr…
95
22
2
553
48,676
Yunyi Shen/申云逸 🐺 retweeted
Geminiたん壁紙作ったので、使ってね!
17
147
6
1,502
32,465
Yunyi Shen/申云逸 🐺 retweeted
If it's isn't obvious already, it is the end of papers as a measure of productivity, expertise or accomplishment. The odds of publishing in "glam" conferences & journals also very quickly approaches random & the time for gate keeping review increasing exponentially. 1/
31
78
20
746
109,551
I wrote a (physical) model. I wrote a numerical solver. Now I figured the solver is wrong, it does not solve the model. Worse apparently the model provably cannot explain experiment. HOWEVER the simulation does agree with the experimental data very well even without fitting...
1
5
754
I like this idea, but more in a public way rather than ending up in the company's proprietary training data...
1
12
895
Yunyi Shen/申云逸 🐺 retweeted
I spent a humiliating amount of time learning how to make animated graphs, just to illustrate a fairly obvious point. “Forecasting s-curves is hard” My views on why carefully following daily figures is unlikely to provide insight. constancecrozier.com/2020/04…
256
3,002
568
10,581
Some thoughts on the Navier-Stokes news from a professor of aerospace engineering: 1) take a deep breath 2) posts indicating we "solved Navier-Stokes" are incorrect and overblown. A very specific mathematical proof involving a niche scenario for Navier-Stokes has *potentially* been shown (that a perfectly smooth incompressible initial condition with finite energy could produce a singularity--note the assumptions here piled on to an equation set that already involves some assumptions). 3) there is no general closed form solution for N-S and that's not what this news is about 4) this is more of a mathematical curiosity than anything else and could matter a lot to mathematicians but basically changes nothing in the way N-S are used in practice 5) there are already very useful exact solutions to Navier-Stokes that greatly simplify the equation set with the right boundary conditions/geometry like laminar flow through a pipe or a 2D Taylor-Green vortex 6) in aerospace, we already treat N-S as an approximation in many instances and are well aware of its limitations. we don't outright solve these equations anyway and regularly chop off terms or simplify parts (at the expense of accuracy) to make them easier to deal with. 7) to be honest I have never found this specific problem to be particularly interesting because we know a core assumption of N-S is a "continuum" fluid where you ignore molecules and assume hydrodynamic scales >>> molecular scales. We know a real fluid cannot have infinite velocity or energy. But clearly you can push equations outside of their bound of validity and make them produce funky results and singularities.
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
168
845
155
5,393
998,492
Yunyi Shen/申云逸 🐺 retweeted
Encouraging frontier labs to train on my research notes, just to set them back a few years. Doing what I can for AI safety
30
677
26
8,863
100,211
Can someone familiar with the matter educate me if the Millennium Prize problem on N-S is about the existence and uniqueness of the solution or if a blow-off solution exists? Or are they the same after all?
1
3
1,098