ALGOTRIX retweeted
Balaji just told you the charging problem is solved and nobody’s doing the infrastructure math.
BYD has 500 of these Megawatt chargers deployed in China. They’re targeting 4,000 by end of year.
Tesla has 75,000 Supercharger connectors. At 250-350kW each.
Here’s the constraint nobody’s pricing: each BYD Megawatt charger requires a direct connection to the medium-voltage grid. That’s the kind of power that runs small factories. You can’t just upgrade an existing site. You’re building new electrical infrastructure from scratch, often requiring grid connection timelines measured in years, not months.
Tesla spent 13 years building infrastructure that can serve 75,000 cars simultaneously at 250kW. BYD’s tech requires roughly 4x the grid capacity per charger. The bottleneck was never the battery chemistry. The bottleneck is getting 1MW to the parking lot.
And here’s what the demo doesn’t show: the InsideEVs test started at 13% and hit 60% in 5 minutes. That’s not a full tank. The Han L has 700km total range on China’s lenient test cycle. Real-world EPA equivalent is closer to 550km. Five minutes gets you about half a charge.
The tech is real. BYD’s battery holds 600kW at 90% state of charge, which is genuinely impressive. But the “EV flippening” requires BYD to build more grid infrastructure in 3 years than Tesla built in 13. And they have to do it in countries where grid connection timelines are measured in quarters, not weeks.
China can mandate the grid upgrades. The rest of the world has to negotiate with utilities.
ALGOTRIX retweeted
It’s unfortunate that a bug in OpenReview allowed reviewer and AC identities to be extracted.
That said, this is a chance to reflect on our practices: We should write our reviews as if our names were attached to them and visible to authors and the broader community, even when the recommendation is a strong reject!
ALGOTRIX retweeted
We are aware of low-quality and LLM-generated reviews and are currently deliberating on appropriate courses of action. For now, authors who receive very poor quality or LLM-generated reviews should flag them to their ACs. We appreciate the community's efforts in reporting these!
ALGOTRIX retweeted
I am an AC for ICLR 2026. One of the papers in my batch was just withdrawn. The authors wrote a brief response, explaining why the reviewers failed at their job. I agree with most of their comments. The authors gave up. They are fed up. Just like many of us. I understand. We pretend the emperor has clothes, but he is naked.
Here is the final part of their withdrawal notice. I took the liberty to make it public, to highlight that what we are doing with AI conference reviews these last few years is, basically, madness.
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Comment: We thank the reviewers for their time.
However, upon reading the reviews for our paper, it became immediately apparent that the four "reject" ratings are not based on good-faith academic disagreement, but on a critical failure to read the submitted paper.
The reviews are rife with demonstrably false claims that are directly contradicted by the text. The core justifications for rejection rely on asserting that key components are "missing" when they are explicitly detailed in the manuscript. Some specific examples are (and many are even fake claims).
Claim: Harder tasks like GSM8K are missing.
Fact: GSM8K results are in many tables, like Table 2 (Section 4.2) and Appendix G.
Claim: The method does not use per-layer ranks.
Fact: This is the entire point of our method. The reviewer clearly mistook our method for the baselines. (Section 2, Table 1).
Claim: The GP kernel is not specified.
Fact: It is specified in Appendix E (Table 6).
Claim: There is no ablation of the method's three stages.
Fact: Section 4.4 ("Ablation Study") and Appendix J are dedicated to this.
Reviewers have a fundamental responsibility to read and evaluate the work they are assigned. The nature of these errors is so fundamental, so systemic in overlooking explicit content, that it goes far beyond what "limited time" or "oversight" can explain. This work has gone through several rounds of revision over the last year. In earlier submissions, the paper usually received borderline or weak-accept scores.
Numerous signs strongly suggest that some reviewers are relying entirely on AI tools to automatically generate peer reviews, rather than fulfilling their fundamental responsibility of personally reading and evaluating manuscripts.
We strongly protest this.
This is a gross disrespect to the authors. It is a flagrant desecration of the reviewer's sacred duty. It fundamentally undermines the integrity of the entire peer-review process.
Given that the reviews are not based on the actual content of our paper, we have decided to withdraw the submission.
We leave this comment so that future readers of the OpenReview page are aware that the items described as "missing" are already present in the submitted manuscript. These negative reviews for this submission are factually unsound and do not reflect the content of the paper. We cannot and will not accept an assessment that is not based on the work we actually submitted.
ALGOTRIX retweeted
1. Fear of Unwinding Yen Carry Trade - if the US rates goes down and the Japanese bond yield goes up further. There is no much profit in yen carry trade. As and when it starts to unwind, more treasuries will be flooded into markets and also pushing Yen higher against Dollar
2. The Maturity wall of 9000 Billion bonds getting renewed over a period of 1 year may push the yeilds higher due to lower demand.
3. ON RRP is at a low point coming down to 20 billion from a high of 2400 billion. Pushing short term rates and SOFR higher.
4. Derivatives Melt Down : the 465 trillion dollar Interest Rate Swaps , A 1% rate swing = massive impact (~$4.6T?). Similar to the 2022 UK gilt crisis , the big 5 banks like JPM, Goldman Sachs, Citi , BofA , Wells Fargo, exposed big time! Fed cuts could spark global sell-off
ALGOTRIX retweeted
Almost a decade ago I was the Harvard economist that said that bitcoin was more likely to be worth $100 than 100k. What did I miss? I was far too optimistic about the US coming to its senses about sensible cryptocurrency regulation; why would policymakers want to facilitate tax evasion and illegal activities? Second, I did not appreciate how Bitcoin would compete with fiat currencies to serve as the transactions medium of choice in the twenty-trillion dollar global underground economy. This demand puts a floor on its price, as I discuss at length in my new book Our Dollar, Your Problem. Third, I did not anticipate a situation where regulators, and especially the regulator in chief, would be able to brazenly hold hundreds of millions (if not billions) of dollars in cryptocurrencies seemingly without consequence given the blatant conflict of interest.
cnbc.com/2018/03/05/bitcoin-…
ALGOTRIX retweeted
There is no chance that Amazon's drivers will be 100% robots by 2030.
ALGOTRIX retweeted
1. Medicine is global. Regulation is local. AI is progressing fast. So what you’re describing will simply never happen.
2. Doctors routinely order consults. They may not 100% follow the recommendations of the consultants. They know the patients the best. They may prioritize or edit. This is no different. Any recommendations always require thoughtfulness and looking at the patient in front of you.
ALGOTRIX retweeted
Today was my last day at xAI, the company that I helped start with Elon Musk in 2023. I still remember the day I first met Elon, we talked for hours about AI and what the future might hold. We both felt that a new AI company with a different kind of mission was needed.
Building AI that advances humanity has been my lifelong dream. My parents left the Russian Federation after the collapse of the USSR in search of a better life for their kids. Life wasn’t always easy as immigrants. Despite the hardships, my parents believed that human values were priceless: values like courage, compassion, curiosity for understanding the world. As a child, I admired scientists like Richard Feynman and Max Planck, who relentlessly pushed the frontiers of physics in order to understand the universe. As a particle physics PhD student at CERN I was excited to contribute to that mission. But the search for new physics was getting harder and harder, requiring bigger and bigger colliders, while new discoveries kept getting fewer. So I began to wonder if superintelligence, not larger colliders, could be the key to unlocking the mysteries of the universe. Could AI develop a consistent theory of quantum gravity? Could AI prove the Riemann hypothesis? In early 2023 I became convinced that we were getting close to a recipe for superintelligence. I saw the writing on the wall: very soon AI could reason beyond the level of humans. How could we ensure that this technology is used for good? Elon had warned of the dangers of powerful AI for years. Elon and I realized that we had a shared vision of AI used to benefit humanity, thus we recruited more like minded engineers and set off to build xAI.
The early days of xAI were not easy. Naysayers told us that we arrived too late to the game, so starting a top AI company from scratch would be impossible. But we believed we could do the impossible. Starting a company from zero required lots of hands-on work. In the beginning I built many of the foundational tools used at the company to launch and manage training jobs. I later oversaw much of the engineering at the company, including Infrastructure, Product and Applied AI projects. xAI’s people are deeply dedicated. Through blood sweat and tears, our team’s blistering velocity built the Memphis supercluster, and shipped frontier models faster than any company in history. I learned 2 priceless lessons from Elon: #1 be fearless in rolling up your sleeves to personally dig into technical problems, #2 have a maniacal sense of urgency.
xAI executes at ludicrous speed. Industry veterans told us that building the Memphis supercluster in 120 days would be impossible. But we believed we could do the impossible. Our goal was to get our training setup running at scale on the Memphis cluster ASAP. Towards the end of our 120 day deadline, we were riddled with mysterious issues with communicating over RDMA between the machines. Elon decided to fly to the datacenter, and we followed. Our infra team landed in Memphis in the middle of the night and got straight to work. After pouring through tens of thousands of lines of lspci output we finally identified a wrong BIOS setting, the root of the problem. Elon was there with us until late into the night. When the training run finally worked, Elon posted our triumph at “4:20am” causing us to laugh out loud. I will never forget the rush of adrenaline that night, and the emotional bonds that we were all in this together. We went to bed feeling like we were living through the most exhilarating time of our lives.
I have enormous love for the whole family at xAI. Our team is truly special - you’re the most dedicated people I’ve ever worked with. Catching up to the frontier this quickly hasn’t been easy. It was made possible by everyone’s diehard grit and team spirit. Thank you to every single person who joined me on this adventure. I want to honor your contributions, your time, your sacrifices, which are never easy. I will always remember working together far into the nights and burning the midnight oil. I will never forget the sacrifices and contributions you’ve made. As I drive away today, I feel like a proud parent, driving away after sending their kid away to college. My heart is brimming with tears of joy, rooting for the company as it grows and matures.
As I'm heading towards my next chapter, I’m inspired by how my parents immigrated to seek a better world for their children. Recently I had dinner with Max Tegmark, founder of the Future of Life Institute. He showed me a photo of his young sons, and asked me “how can we build AI safely to ensure that our children can flourish?” I was deeply moved by his question. Earlier in my career, I was a technical lead for DeepMind's Alphastar StarCraft agent, and I got to see how powerful reinforcement learning is when scaled up. As frontier models become more agentic over longer horizons and a wider range of tasks, they will take on more and more powerful capabilities, which will make it critical to study and advance AI safety. I want to continue on my mission to bring about AI that’s safe and beneficial to humanity. I’m announcing the launch of Babuschkin Ventures, which supports AI safety research and backs startups in AI and agentic systems that advance humanity and unlock the mysteries of our universe. Please reach out at [email protected] if you want to chat. The singularity is near, but humanity’s future is bright!
ALGOTRIX retweeted
NEWS: AGI labs have started poaching talent from quant firms offering competitive pay and benefits
> Entry-level quants are eligible for base salaries of as much as $300,000
> Wall Street is attempting to hit back against the poaching attempts
LMAO
ITS HAPPENING
ALGOTRIX retweeted
I haven’t read the paper yet. I’m curious to see how it works empirically, there are a lot of different kinds of graphs. I’m guessing it’s got good (and neutral) performance relative to old algos on certain kinds of graphs at certain scales.
ALGOTRIX retweeted
$NDX participation is getting narrower.
Prior notable divergences led to an index-level pullback.
Chart @McClellanOsc
ALGOTRIX retweeted
Neural networks are grown, not programmed. What does that growth process look like? Like this!
This is a small language model (3M) across training, visualised with a new interpretability technique: susceptibilities. We call this handsome critter the rainbow serpent.
ALGOTRIX retweeted
Who invented convolutional neural networks (CNNs)?
1969: Fukushima had CNN-relevant ReLUs [2].
1979: Fukushima had the basic CNN architecture with convolution layers and downsampling layers [1]. Compute was 100 x more costly than in 1989, and a billion x more costly than today.
1987: Waibel applied Linnainmaa's 1970 backpropagation [3] to weight-sharing TDNNs with 1-dimensional convolutions [4].
1988: Wei Zhang et al. applied "modern" backprop-trained 2-dimensional CNNs to character recognition [5].
All of the above was published in Japan 1979-1988.
1989: LeCun et al. applied CNNs again to character recognition (zip codes) [6,10].
1990-93: Fukushima’s downsampling based on spatial averaging [1] was replaced by max-pooling for 1-D TDNNs (Yamaguchi et al.) [7] and 2-D CNNs (Weng et al.) [8].
2011: Much later, my team with Dan Ciresan made max-pooling CNNs really fast on NVIDIA GPUs. In 2011, DanNet achieved the first superhuman pattern recognition result [9]. For a while, it enjoyed a monopoly: from May 2011 to Sept 2012, DanNet won every image recognition challenge it entered, 4 of them in a row. Admittedly, however, this was mostly about engineering & scaling up the basic insights from the previous millennium, profiting from much faster hardware.
Some "AI experts" claim that "making CNNs work" (e.g., [5,6,9]) was as important as inventing them. But "making them work" largely depended on whether your lab was rich enough to buy the latest computers required to scale up the original work. It's the same as today. Basic research vs engineering/development - the R vs the D in R&D.
REFERENCES
[1] K. Fukushima (1979). Neural network model for a mechanism of pattern recognition unaffected by shift in position — Neocognitron. Trans. IECE, vol. J62-A, no. 10, pp. 658-665, 1979.
[2] K. Fukushima (1969). Visual feature extraction by a multilayered network of analog threshold elements. IEEE Transactions on Systems Science and Cybernetics. 5 (4): 322-333. This work introduced rectified linear units (ReLUs), now used in many CNNs.
[3] S. Linnainmaa (1970). Master's Thesis, Univ. Helsinki, 1970. The first publication on "modern" backpropagation, also known as the reverse mode of automatic differentiation. (See Schmidhuber's well-known backpropagation overview: "Who Invented Backpropagation?")
[4] A. Waibel. Phoneme Recognition Using Time-Delay Neural Networks. Meeting of IEICE, Tokyo, Japan, 1987. Backpropagation for a weight-sharing TDNN with 1-dimensional convolutions.
[5] W. Zhang, J. Tanida, K. Itoh, Y. Ichioka. Shift-invariant pattern recognition neural network and its optical architecture. Proc. Annual Conference of the Japan Society of Applied Physics, 1988. First backpropagation-trained 2-dimensional CNN, with applications to English character recognition.
[6] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel: Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, 1(4):541-551, 1989. See also Sec. 3 of [10].
[7] K. Yamaguchi, K. Sakamoto, A. Kenji, T. Akabane, Y. Fujimoto. A Neural Network for Speaker-Independent Isolated Word Recognition. First International Conference on Spoken Language Processing (ICSLP 90), Kobe, Japan, Nov 1990. A 1-dimensional convolutional TDNN using Max-Pooling instead of Fukushima's Spatial Averaging [1].
[8] Weng, J., Ahuja, N., and Huang, T. S. (1993). Learning recognition and segmentation of 3-D objects from 2-D images. Proc. 4th Intl. Conf. Computer Vision, Berlin, pp. 121-128. A 2-dimensional CNN whose downsampling layers use Max-Pooling (which has become very popular) instead of Fukushima's Spatial Averaging [1].
[9] In 2011, the fast and deep GPU-based CNN called DanNet (7+ layers) achieved the first superhuman performance in a computer vision contest. See overview: "2011: DanNet triggers deep CNN revolution."
[10] How 3 Turing awardees republished key methods and ideas whose creators they failed to credit. Technical Report IDSIA-23-23, Swiss AI Lab IDSIA, 14 Dec 2023. See also the YouTube video for the Bower Award Ceremony 2021: J. Schmidhuber lauds Kunihiko Fukushima.
ALGOTRIX retweeted
No one did it like Jim Simons.
Not a single losing year in terms of gross returns.
152.10% return in a single year on 5.2B is insane.
ALGOTRIX retweeted
There is now a path for China to surpass the U.S. in AI. Even though the U.S. is still ahead, China has tremendous momentum with its vibrant open-weights model ecosystem and aggressive moves in semiconductor design and manufacturing. In the startup world, we know momentum matters: Even if a company is small today, a high rate of growth compounded for a few years quickly becomes an unstoppable force. This is why a small, scrappy team with high growth can threaten even behemoths. While both the U.S. and China are behemoths, China’s hypercompetitive business landscape and rapid diffusion of knowledge give it tremendous momentum. The White House’s AI Action Plan released last week, which explicitly champions open source (among other things), is a very positive step for the U.S., but by itself it won’t be sufficient to sustain the U.S. lead.
Now, AI isn’t a single, monolithic technology, and different countries are ahead in different areas. For example, even before Generative AI, the U.S. had long been ahead in scaled cloud AI implementations, while China has long been ahead in surveillance technology. These translate to different advantages in economic growth as well as both soft and hard power. Even though nontechnical pundits talk about “the race to AGI” as if AGI were a discrete technology to be invented, the reality is that AI technology will progress continuously, and there is no single finish line. If a company or nation declares that it has achieved AGI, I expect that declaration to be less a technology milestone than a marketing milestone. A slight speed advantage in the Olympic 100m dash translates to a dramatic difference between winning a gold medal versus a silver medal. An advantage in AI prowess translates into a proportionate advantage in economic growth and national power; while the impact won’t be a binary one of either winning or losing everything, these advantages nonetheless matter.
Looking at Artificial Analysis and LMArena leaderboards, the top proprietary models were developed in the U.S., but the top open models come from China. Google’s Gemini 2.5 Pro, OpenAI’s o4, Anthropic’s Claude 4 Opus, and Grok 4 are all strong models. But open alternatives from China such as DeepSeek R1-0528, Kimi K2 (designed for agentic reasoning), Qwen3 variations (including Qwen3-Coder, which is strong at coding) and Zhipu’s GLM 4.5 (whose post-training software was released as open source) are close behind, and many are ahead of Meta’s Llama 4 and Google’s Gemma 3 — the U.S.’ best open-weights offerings.
Because many U.S. companies have taken a secretive approach to developing foundation models — a reasonable business strategy — the leading companies spend huge numbers of dollars to recruit key team members from each other who might know the “secret sauce“ that enabled a competitor to develop certain capabilities. So knowledge does circulate, but at high cost and slowly. In contrast, in China’s open AI ecosystem, many advanced foundation model companies undercut each other on pricing, make bold PR announcements, and poach each others’ employees and customers. This Darwinian life-or-death struggle will lead to the demise of many of the existing players, but the intense competition breeds strong companies.
In semiconductors, too, China is making progress. Huawei’s CloudMatrix 384 aims to compete with Nvidia’s GB200 high-performance computing system. While China has struggled to develop GPUs with a similar capability as Nvidia’s top-of-the-line B200, Huawei is trying to build a competitive system by combining a larger number (384 instead of 72) of lower-capability chips. China’s automotive sector once struggled to compete with U.S. and European internal combustion engine vehicles, but leapfrogged ahead by betting on electric vehicles. It remains to be seen how effective Huawei’s alternative architectures prove to be, but the U.S. export restrictions have given Huawei and other Chinese businesses a strong incentive to invest heavily in developing their own technology. Further, if China were to develop its domestic semiconductor manufacturing capabilities while the U.S. remained reliant on TSMC in Taiwan, then the U.S.’ AI roadmap would be much more vulnerable to a disruption of the Taiwan supply chain (perhaps due to a blockade or, worse, a hot war).
With the rise of electricity, the internet, and other general-purpose technologies, there was room for many nations to benefit, and the benefit to one nation hasn’t come at the expense of another. I know of businesses that, many months back, planned for a future in which China dominates open models (indeed, we are there at this moment, although the future depends on our actions). Given the transformative impact of AI, I hope all nations — especially democracies with a strong respect for human rights and the rule of law — will clear roadblocks from AI progress and invest in open science and technology to increase the odds that this technology will support democracy and benefit the greatest possible number of people.
[Full text: deeplearning.ai/the-batch/is… ]
