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UK Taxpayers Lose £340 Million on Starlink Rival British taxpayers are reportedly sitting on an estimated £340 million paper loss from the government-backed Eutelsat OneWeb project, built as a rival to SpaceX’s Starlink. The UK’s combined £517 million investment is now worth only around £175 million, as per @outerspacetoday British taxpayers are paying the price because the government would rather compete with Elon Musk than use the better product he already built. They deserve the best technology, not an inferior government-backed alternative they are forced to subsidize.

[📹] 𝙉𝙤 𝙏𝙚𝙖𝙧𝙨 𝙊𝙣 𝙏𝙝𝙚 𝘿𝙖𝙣𝙘𝙚𝙛𝙡𝙤𝙤𝙧🪩 With #ONEWE# 🔗 🔗 #이채연# #LEECHAEYEON# #원위# #TILLIDIE# #NTOTD_Challenge# #노티온플_챌린지# @official_ONEWE

[#MCOUNTDOWN#] EP.930 Lineup LE SSERAFIM/박현규/비비(BIBI)/윤산하/IDID (아이딧)/아이오아이/아일릿(ILLIT)/ALPHA DRIVE ONE/AND2BLE (앤더블)/UNCHILD/XLOV/원위(ONEWE)/YOUNITE/YUHZ(유어즈)/ITZY/ZEROBASEONE/CORTIS/Queenz Eye/태용(TAEYONG)/FLARE U (플레어 유)/HEART OF WOMAN

老师们我发现一个Bug，@ohyishi，@okxchinese Onekey的默认兑换的时候，会有很大的差价，我看是走的OKX的Swap，同样的价格在 Jumper上是正常的。 这是OKX Swap的问题吗，我看他下面只有1USDT=0.991USDC

we hacked ledger. the @OneKey_Anzen team has successfully reproduced a transaction replacement attack against ledger ethereum app 1.22.1 in our lab. the bug is a race condition between the transaction display logic and the underlying transaction buffer. an attacker can overwrite the transaction waiting to be signed while the user is still reviewing a legitimate one. in simple terms: - you see transaction a on your ledger. - you approve transaction a. - your ledger can end up signing transaction b. - and you never see transaction b. to reproduce this, we built the 1.22.1 ELF ourselves, fixed the speculos reset 502 issue, and got the full attack flow working end to end. ledger fixed this in ethereum app 1.22.3. if you’re still on an older version, update it.

We Are All Human Beings - Speaking of a life that has not been easy, His Holiness says that living truthfully has kept him optimistic — and that holding oneself apart as special only deceives oneself, while meeting others as a fellow human being brings people close. Video originally recorded on January 2, 2015.

7/ 🧭 Dyna-2 in one line It's not "robots should watch videos." It's this: physical intelligence has its own scaling variable, and human experience is the most scalable one we've got. 1M hours is just the start. Full technical report 👉

Claude's watermark probably doesn't work how you think. As the CTO of GPTZero, I'll explain how Anthropic, Google and OpenAI are building text watermarking in this brief explainer and whether it can be defeated. Almost all forms of watermarking that are fast and cheap enough for a frontier lab have the same formula, following the KGW method: In generation: 1. Let's say you've generated n tokens so far. Take those n tokens + a secret key to generate a random hash 2. Use that hash to randomly reweight the probabilities for the n+1 token, and then sample from that new distribution. In the simple case, you could split 50% of all English words into a green or red set based on your hash, and boost the probability of words in the green set. For watermark detection: 1. For each token, see if it was in the green or red set. 2. To do this, recreate the hash based on the secret key and the text preceding the current token. Then, recreate the green and red set of words. 3. Once you've checked all the words in the text, if the next token is selected disproportionally from the green set more than 50% of the time, you claim the text has the watermark. I can tell you want to ask the following: 1) Isn't it easy to mess up the hash if you paraphrase the text? The answer is mostly yes, however, you can use a statistical model to get your hash instead of a deterministic function (SIR, Adaptive Watermark). Since the entire watermark is probabilistic, this is fine. 2) Doesn't this make the text much worse? The answer is yes, it does - Yes, it does – but for most people, it's imperceptible (Google claims in human feedback study with 20,000 texts), since there are exponentially many ways to write the same paragraph. DiPmark does something more sophisticated to avoid shifting the text distribution on average. Of course, watermarks fail on short text or highly predictable texts like "2+2=4". 3) Shouldn't it be easy to figure out the green and red sets? The answer is no. You would need an exponentially large number of samples from the watermarker to reconstruct those sets exactly, but it's a risk if the detector is open to the wild (Watermark Stealing) Still, there are couple challenges that a frontier lab needs to overcome: 1. Their watermark needs to work token-by-token because they are streaming their text to users. Many watermark methods plan sentences or paragraphs at a time, or change the text after its entirely written, in order to make their watermark robust to paraphrasers, and a frontier lab cannot afford to do this yet (SemStamp, PostMark) 2. If the secret key leaks, the watermark is busted. To avoid a large blast damage from this, you need to have a couple secret keys in rotation. 3. There are some texts, like code, that cannot be arbitrarily changed, otherwise the code will break. In those cases, the watermark needs to selectively change words in parts of the text that can tolerate synonyms (i.e. like variable naming) - see SWEET, EWD, Invisible Entropy. 4. They will need to educate their users on how to deal with false positives and false negatives of a detector, which is a big challenge (one we put a lot of effort into) So, how do I see this playing out in the next 6 months? 1. If Anthropic releases the watermark detector publically, I think they defeat their own watermark. People find reliable watermark removal strategies by testing against Anthropic (AI detectors like GPTZero have an advantage here because they can train against these adversaries once they become popular). 2. If they keep the detector private to the government, like Google has done, it's "safer". However, there are some papers showing trained approaches that work robustly to zero-shot break watermarks without any data, simply because they try to write the text just like a human (Zhang et al. 2024, Watermarks in the Sand). Also, making your detector makes it battle-tested and stronger long-term (my experience). 3. In my testing, the watermarks don't survive intense paraphrasing (especially if you combine word choice and syntax attacks), or human text substitution (rewrite your AI text by plagiarizing human authors). The free paraphrasers I've tried have quickly bypassed Google Deepmind's SynthId for what it's worth. 4. All-in-all, frontier labs are likely okay with this because they expect most users to not attack the watermark, and also because they + European regulators likely don't care past a certain point - its good enough. 5. Overall, I think users of frontier LLMs will not really care about this, because 1) they don't realize watermarks are there, 2) EU will force everyone to conform, 3) this seems more like regulatory hoop-jumping than an earnest effort from frontier labs to expose LLM use Lastly, people's first concern shouldn't be watermarking, it should be AI detectors! If you're posting, "its not X, its Y!!", I don't think the watermark is going to make a difference :)