The White Nile | by Jeffrey

Take and share photos at the KWARXS booth and great things will happen! @kwarxs @T4ISummit

Singapore is about to feel the full force of @Cardano! Less than one week until @token2049 and the huge Cardano Community presence inside and outside of the conference. Ecosystem Booths, Side-Events, and more!

🚨 BAD news for Medical AI models. MASSIVE revelations from this @Microsoft paper. 🤯 Current medical AI models may look good on standard medical benchmarks but those scores do not mean the models can handle real medical reasoning. The key point is that many models pass tests by exploiting patterns in the data, not by actually combining medical text with images in a reliable way. The key findings are that models overuse shortcuts, break under small changes, and produce unfaithful reasoning. This makes the medical AI model's benchmark results misleading if someone assumes a high score means the model is ready for real medical use. --- The specific key findings from this paper 👇 - Models keep strong accuracy even when images are removed, even on questions that require vision, which signals shortcut use over real understanding. - Scores stay above the 20% guess rate without images, so text patterns alone often drive the answers. - Shuffling answer order changes predictions a lot, which exposes position and format bias rather than robust reasoning. - Replacing a distractor with “Unknown” does not stop many models from guessing, instead of abstaining when evidence is missing. - Swapping in a lookalike image that matches a wrong option makes accuracy collapse, which shows vision is not integrated with text. - Chain of thought often sounds confident while citing features that are not present, which means the explanations are unfaithful. - Audits reveal 3 failure modes, incorrect logic with correct answers, hallucinated perception, and visual reasoning with faulty grounding. - Gains on popular visual question answering do not transfer to report generation, which is closer to real clinical work. - Clinician reviews show benchmarks measure very different skills, so a single leaderboard number misleads on readiness. - Once shortcut strategies are disrupted, true comprehension is far weaker than the headline scores suggest. - Most models refuse to abstain without the image, which is unsafe behavior for medical use. - The authors push for a robustness score and explicit reasoning audits, which signals current evaluations are not enough. 🧵 Read on 👇

Hey @IntersectMBO Members, There is just one day left for the Intersect Board of Directors election! I've translated my Candidate Letter into Vietnamese, Tamil, Sanhala, Spanish, French, and Japanese for broader outreach. Thanks for your consideration! Tiếng Việt - docs.google.com/document/d/1TT… 🔥 தமிழ் - docs.google.com/document/d/1ps… 🔥 සිංහල - docs.google.com/document/d/1zY… 🔥 Español - docs.google.com/document/d/1x6… 🔥 Français - docs.google.com/document/d/1NR… 🔥 日本語 - #heading=h.hvxt3cd7isr4" target="_blank" rel="nofollow noopener">docs.google.com/document/d/1g6… 🔥

✨ Good things don’t last forever… but this one just got longer. Phase 1 of Glacier Drop is extending → new close date: Oct 20, 2025. Because building together means no ecosystem is left behind. 🕛 More time to: ✔️ Resolve wallet & hardware compatibility issues ✔️ Engage with new integrations ✔️ Keep cross-chain participation thriving Now you’ve got more time. Don’t miss the biggest token distribution event of the year. Full announcement: midnight.gd/news/more-time…

Who's got two thumbs, a few cases of Labubu, and is ready to yap about Hydra all over Singapore? This guy! 👍🤠👍✈️👹🇸🇬 Come hang with @therealdisasm and I in the @InputOutputHK booth at @token2049 next week! (Note: no Labubu will be harmed in the making of this podcast, probably)