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   Isaac Ji Kuo (isaackuo@spacey.space)'s status on Sunday, 01-Feb-2026 00:30:39 JST Isaac Ji Kuo

   @Infoseepage @cstross @nyrath Oh please. You are blinded by recency bias. Just because something is in the news right now does not mean this is how it has been for a long time, and it does not mean it's going to continue for a long time.

   In fact, the fact that it's currently in the news should be a red flag for you to consider that it is NEWS-worthy. And therefore NOT the typical pattern.

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   William Lindsey :toad: (wdlindsy@toad.social)'s status on Friday, 16-Jan-2026 02:06:07 JST William Lindsey :toad:

   in reply to

   "Trump’s ability to bend reality to his will is the foundation of his political success. It’s how he has survived so many moments that would have ended other politicians’ careers.

   But it is not working this time.

   In what may be the most high-profile failure of the Trump media machine, the American people are not falling for Trump’s lies [about Renee Good's murder].'

   Dan Pfeiffer

   #Trump #ReneeGood #ICE #MaskedThugs #violence #Minnesota #resistance
   /22

   https://www.messageboxnews.com/p/how-trump-is-losing-the-fight-on

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   Curmudgeon (jetton@mastodon.online)'s status on Tuesday, 06-Jan-2026 17:09:17 JST Curmudgeon

   Woo-hoo first post. #introduction. Old guy trying to figure out what the heck this is. All too many interests, so little time.

   In no particular order #Art #Books #BookHistory #EvolutionaryBiology #Science #Cytogenetics #Dogs #History #HistoryOfScience #Reading #DataVisualization #Genetics #Neurobiology #Comics #Videogames #SciFi #Fantasy #Neurodivergent #ActuallyAutistic #Statistics #Epidemiology #Medicine #Quaker #Humor #Astronomy

   Leaving some extra # here so I don’t lose them: ############

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   Futurist Jim Carroll (jimcarroll@mastodon.futurist.info)'s status on Monday, 08-Dec-2025 20:49:58 JST Futurist Jim Carroll

   "Know when to get your age out of the way." - Futurist Jim Carroll

   ----

   Futurist Jim Carroll is writing his end-of-2025 / introduction-to-2026 series, 26 Principles for 2026. You can follow along at 2026.jimcarroll.com. He welcomes your comments.

   ---

   We are on Day 13. We just spent Day 12 dismantling your personal hubris - getting your ego out of the way. One of the most important aspects of that?
   Showing your wisdom the door!

   We need to dismantle the collective, generational delusion of your organizational hierarchy!

   We need to talk about age.

   Yup.

   Sorry.

   Depending on who you are while reading this, there might be a major reality you need to consider - it might very well be the case that your grey hair is now a strategic liability.

   The unique nature of our times? I call it "The Wisdom Inversion!"

   Think about where we are at this moment in time.

   In a slow-moving, linear world, wisdom was cumulative. Grey hair was a proxy for foresight. The people at the top of the pyramid had seen the most, so they knew the most. You paid your dues, waited your turn, and eventually, you got to hold the steering wheel.

   In an exponential world, that model is completely broken.

   When technology, culture, and consumer behaviour shift radically every 36 months, your 30 years of experience isn't just irrelevant; it’s often a dangerous anchor to an obsolete past. You might have earned your way to the top, but by the time you get there, your experience, insight, and wisdom are probably wildly out of date.

   The result? Right now, in boardrooms across the world, rooms full of 55-year-olds are making massive strategic bets on a future built by, and for, 25-year-olds.

   They are trying to interpret TikTok dynamics through a PowerPoint lens.

   Need an example? They are analyzing decentralized finance business models - weird things involved crypto and blockchain and stuff like that - using banking models from 1995.

   And your younger employees? They are rolling their eyes. They are quietly laughing. They are sitting in the back of the room, biting their tongues, watching leadership steer the ship toward an iceberg they spotted five miles back. They are frustrated because they are native to the future that senior leadership is only visiting as tourists.

   If your strategy is being dictated solely by the oldest people in the building, you are driving forward while staring into the rearview mirror.

   That's why a discipline you must master in 2026, and beyond, is Wisdom Inversion.

   Keep on reading - because you need to deal with this reality!

   ----

   **#2026** **#Change** **#Navigate** **#Future** **#Inspiration** **#Principles** **#Speed** **#Growth** **#Guidance** **#Exponential**

   Jim Carroll's 1997 book, Surviving the Information Age, continues to be a powerful indictment of the change barriers that come with slow-moving minds in an era of fast change.

   Original post: https://jimcarroll.com/2025/12/decoding-tomorrow-mastering-2026-the-wisdom-inversion-know-when-to-get-your-age-out-of-the-way

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   CelloMom On Cars (cellomomoncars@mastodon.social)'s status on Friday, 14-Nov-2025 18:23:17 JST CelloMom On Cars

   in reply to

   "The evidence of nearly three decades of climate diplomacy is that when we set ourselves an objective, more often than not we will hit it.

   Consider the European Union’s first pledge under the 2015 Paris Agreement to cut emissions in 2030 to 40% below levels in 1990.

   Plenty scoffed at the time.

   In fact, greenhouse pollution last year was already 37% below 1990, and on current trends the EU may achieve a 54% cut, almost enough to hit a stricter target passed in 2020."

   https://www.bloomberg.com/opinion/articles/2025-11-13/how-the-world-is-quietly-winning-on-climate

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   David Chisnall (*Now with 50% more sarcasm!*) (david_chisnall@infosec.exchange)'s status on Saturday, 27-Sep-2025 00:13:31 JST David Chisnall (*Now with 50% more sarcasm!*)

   I think this needs to be repeated, since I tend to be quite negative about all of the 'AI' hype:

   I am not opposed to machine learning. I used machine learning in my PhD and it was great. I built a system for predicting the next elements you'd want to fetch from disk or a remote server that didn't require knowledge of the algorithm that you were using for traversal and would learn patterns. This performed as well as a prefetcher that did have detailed knowledge of the algorithm that defined the access path. Modern branch predictors use neural networks. Machine learning is amazing if:

   • The problem is too hard to write a rule-based system for or the requirements change sufficiently quickly that it isn't worth writing such a thing and,
   • The value of a correct answer is much higher than the cost of an incorrect answer.

   The second of these is really important. Most machine-learning systems will have errors (the exceptions are those where ML is really used for compression[1]). For prefetching, branch prediction, and so on, the cost of a wrong answer is very low, you just do a small amount of wasted work, but the benefit of a correct answer is huge: you don't sit idle for a long period. These are basically perfect use cases.

   Similarly, face detection in a camera is great. If you can find faces and adjust the focal depth automatically to keep them in focus, you improve photos, and if you do it wrong then the person can tap on the bit of the photo they want to be in focus to adjust it, so even if you're right only 50% of the time, you're better than the baseline of right 0% of the time.

   In some cases, you can bias the results. Maybe a false positive is very bad, but a false negative is fine. Spam filters (which have used machine learning for decades) fit here. Marking a real message as spam can be problematic because the recipient may miss something important, letting the occasional spam message through wastes a few seconds. Blocking a hundred spam messages a day is a huge productivity win. You can tune the probabilities to hit this kind of threshold. And you can't easily write a rule-based algorithm for spotting spam because spammers will adapt their behaviour.

   Translating a menu is probably fine, the worst that can happen is that you get to eat something unexpected. Unless you have a specific food allergy, in which case you might die from a translation error.

   And that's where I start to get really annoyed by a lot of the LLM hype. It's pushing machine-learning approaches into places where there are significant harms for sometimes giving the wrong answer. And it's doing so while trying to outsource the liability to the customers who are using these machines in ways in which they are advertised as working. It's great for translation! Unless a mistranslated word could kill a business deal or start a war. It's great for summarisation! Unless missing a key point could cost you a load of money. It's great for writing code! Unless a security vulnerability would cost you lost revenue or a copyright infringement lawsuit from having accidentally put something from the training set directly in your codebase in contravention of its license would kill your business. And so on. Lots of risks that are outsourced and liabilities that are passed directly to the user.

   And that's ignoring all of the societal harms.

   [1] My favourite of these is actually very old. The hyphenation algorithm in TeX trains short Markov chains on a corpus of words with ground truth for correct hyphenation. The result is a Markov chain that is correct on most words in the corpus and is much smaller than the corpus. The next step uses it to predict the correct breaking points in all of the words in the corpus and records the outliers. This gives you a generic algorithm that works across a load of languages and is guaranteed to be correct for all words in the training corpus and is mostly correct for others. English and American have completely different hyphenation rules for mostly the same set of words, and both end up with around 70 outliers that need to be in the special-case list in this approach. Writing a rule-based system for American is moderately easy, but for English is very hard. American breaks on syllable boundaries, which are fairly well defined, but English breaks on root words and some of those depend on which language we stole the word from.