@holger_moller@bildung.social"Most transformation assumes you throw out what's not working and start anew. That's not transformation. That's replacement. Real transformation requires presencing. Deep listening to what is trying to emerge from within the system."
#OttoScharmer
#LernenImWandel
@brichapman@mastodon.socialWhy Your Problem-Solving Approach Keeps Failing (Complicated vs Complex)
https://www.youtube.com/watch?v=w3Keu9Gcl0g
@paul@social.van.buu.re“Not every problem is a coding problem. Access to code generation pulls us toward solving policy challenges with new lines of code, and to focus on problems that are legible to machines. This capture of the imagination of policy, industry, academia, and media habituates us to dehumanization.”
https://www.
@frankel@mastodon.top#ClaudeCode Performance: Unlock Deep #Thinking for Better Results
https://claudefa.st/blog/guide/perform
@Techmeme@techhub.socialGoogle rolls out Gemini 3.1 Pro, which it says is "a step forward in core reasoning", for AI Pro and Ultra subscribers; the .1 increment is a first for Google (Abner Li/9to5Google)
https://9to5google.com/2026/02/19/google-announces-gem…
@arXiv_econTH_bot@mastoxiv.pageSolving Problems of Unknown Difficulty
Nicholas Wu
https://arxiv.org/abs/2604.00156 https://arxiv.org/pdf/2604.00156 https://arxiv.org/html/2604.00156
arXiv:2604.00156v1 Announce Type: new
Abstract: This paper studies how uncertainty about problem difficulty shapes problem-solving strategies. I develop a dynamic model where an agent solves a problem by brainstorming approaches of unknown quality and allocating a fixed effort budget among them. Success arrives from spending effort pursuing good approaches, at a rate determined by the unknown problem difficulty. The agent balances costly exploration (expanding the set of approaches) with exploitation (pursuing existing approaches). Failures could signal either a bad idea or a hard problem, and this uncertainty generates novel dynamics: optimal search alternates between trying new approaches and revisiting previously abandoned ones. I then examine a principal-agent environment, where moral hazard arises on the intensive margin: how the agent explores. Dynamic commitment leads contracts to frontload incentives, which can be counteracted by the presence of learning. The framework reflects scientific discovery, product development, and other creative work, providing insights into innovation and organizational design.
toXiv_bot_toot
@Mediagazer@mstdn.socialMemo: Business Insider says it will give a new "quarterly AI Award" for the best staff AI use, like solving a frustrating problem, with the winner getting $400 (Max Tani/@maxwelltani)
https://x.com/maxwelltani/status/2039345510683709715
@aredridel@kolektiva.socialRE: https://social.coop/@eloquence/116297123919323315
Absolutely this.
MiniMax M2.7 and GLM-5.1 are incredibly capable.
And I think _tooling_ (that is: traditional, deterministic as can be software) to guide and give structure to the probabilistic problem-solving is actually incredibly effective. And better, in terms of energy, very very cheap. You can throw 500 billion more parameters and 2 TB of RAM and many megawatts at things to make them better... or you can throw tools to shape things at them.
@niklaskorz@rheinneckar.socialI'll be in the #fosdem translations dev room this afternoon, speaking about something completely unrelated to my usual topics.
Don't expect a ready to use project though, it's more about sharing a story of creative problem solving. :blobcatartist:
@Techmeme@techhub.socialUtah launches a one-year pilot program allowing Legion Health's AI chatbot to renew prescriptions for 15 low-risk psychiatric maintenance medications (Robert Hart/The Verge)
https://www.theverge.com/ai-artificial-intelligence/90652…
@cdamian@rls.socialFriday Links 26-04
It is a bit sad that he is leaving In Our Time, but I enjoyed the interview with Melvyn Bragg.
The blog post about curiosity as a leader is short and great.
https://christof.damian.net…
@UP8@mastodon.social🦝 Raccoons break into liquor stores, scale skyscrapers and pick locks – studying their clever brains can clarify human intelligence, too
https://theconversation.com/raccoons-b
@datascience@genomic.socialPrimer to get you started with Optimization and Mathematical Programming in R #rstats
@qurlyjoe@mstdn.socialAre plants intelligent? Define #intelligence. One definition is, problem solving. The root tip of a plant will grow towards nutrients, avoiding obstacles before they reach them. Build the equivalent of a rat maze but for plants, in dirt. They will grow through a maze, exploring much like a rat does, until a root tip finds the ammonium nitrate “cheese”. Takes a while, but they do it, avoiding p…
@hiimmrdave@hachyderm.iosoftware that people make for themselves may look like spaghetti to a professional, but it's solving the right problem.
@Techmeme@techhub.socialUK National Education Union poll: 66% of secondary school teachers in England say pupils using AI are losing their capacity for core skills like writing (Sally Weale/The Guardian)
https://www.theguardian.com/technology/2026/apr/02…
@Cognessence@social.linux.pizza“Bees can learn a surprising amount of information from observing peers, including which flowers to visit, but also how to solve complex object-manipulation tasks. Accordingly, many complex social behaviors are much more driven by individual problem solving than by a diffuse swarm intelligence, as was traditionally thought.”
- Lars Chittka, ‘The Mind of a Bee’
@arXiv_csLG_bot@mastoxiv.pageFuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
Xu Wan, Chao Yang, Cheng Yang, Jie Song, Mingyang Sun
https://arxiv.org/abs/2602.20729 https://arxiv.org/pdf/2602.20729 https://arxiv.org/html/2602.20729
arXiv:2602.20729v1 Announce Type: new
Abstract: Safe Reinforcement Learning (RL) is crucial for achieving high performance while ensuring safety in real-world applications. However, the complex interplay of multiple uncertainty sources in real environments poses significant challenges for interpretable risk assessment and robust decision-making. To address these challenges, we propose Fuz-RL, a fuzzy measure-guided robust framework for safe RL. Specifically, our framework develops a novel fuzzy Bellman operator for estimating robust value functions using Choquet integrals. Theoretically, we prove that solving the Fuz-RL problem (in Constrained Markov Decision Process (CMDP) form) is equivalent to solving distributionally robust safe RL problems (in robust CMDP form), effectively avoiding min-max optimization. Empirical analyses on safe-control-gym and safety-gymnasium scenarios demonstrate that Fuz-RL effectively integrates with existing safe RL baselines in a model-free manner, significantly improving both safety and control performance under various types of uncertainties in observation, action, and dynamics.
toXiv_bot_toot
@al3x@hachyderm.ioHonest curiosity: are current interviews for software development positions asking candidates to use AI to solve the problems?
If not familiar with tools, then the candidate will now face three challenges: solving the problem, explaining the line of thinking, and using the tools to display the work.
@Techmeme@techhub.socialFlapping Airplanes, an AI research lab "devoted to solving the data efficiency problem", raised $180M at a $1.5B valuation from GV, Sequoia, Index, and others (Kate Clark/Wall Street Journal)
https://www.
@frankel@mastodon.topWhy Senior Engineers Let Bad Projects Fail
https://lalitm.com/post/why-senior-engineers-let-bad-projects-fail/
@tomkalei@machteburch.socialDonald Knuth discovers vibe-mathing:
"What a joy it is to learn not only that my conjecture has a nice solution but also to celebrate this dramatic advance in automatic deduction and creative problem solving. I’ll try to tell the story briefly in this note."
https://www-cs-faculty.stanford.edu/~knuth/papers/claude-cycles.pdf
