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Imprint: https://teapawt.org/t.co/0tNML7fnEi

Grünwald, Deutschland
Joined October 2014
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RiskDataScience retweeted
🚨 AI coding agent ASCENDED in NetHack 5.0.0 🤖⚔️ 🎮 23,008 turns 🧠 242 sessions ⌨️ 46,230 inputs 🔁 32 recoveries It even built its own tools to keep playing The twist? Human guidance + authorized RNG/state edits📄👇 zenodo.org/records/23923839 #AI #AgenticAI #NetHack #CodingAgents
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RiskDataScience retweeted
This is honestly shocking. Three months ago, Hong Wang was awarded the Fields Medal for resolving the Kakeya conjecture in three dimensions. Today, OpenAI released a proposed proof of the conjecture in ALL dimensions. And that’s not all. It also announced a proposed existence proof for quantum chromodynamics and a proposed refutation of the Exponential Time Hypothesis. Experts say that, if confirmed, these would be Nobel prize and Turing award level discoveries respectively. A journalist just asked me: “What remains for humans to do?” I think it’s becoming increasingly clear that more and more problem solving will be automated. But humans will remain crucial in determining the direction of discovery. Deciding which problems are worth solving. Because the value of a discovery depends on our needs, our priorities and the kind of world we want to build. There will be a shift from problem solving to problem setting. I also think there will be huge individual differences. People with strong critical thinking skills, creativity and imagination will be supercharged by AI. People without those skills risk surrendering to it. I’m particularly concerned for children, who may never develop these skills. I think epistemic literacy - how to distinguish information from knowledge - is the single most important skill we should be teaching.
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RiskDataScience retweeted
⚽♟️🃏 260 sports & games. One shared rules API. 🚀 Building rulesatlas: an open-source Python + R library for game states, legal transitions & outcomes. Fully extensible w custom rules No simulations. Just rules🤖 pip install rulesatlas #RuleAtlas #OpenSource #Python #RStats #AI
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⚽♟️🃏 260 sports & games. One shared rules API. 🚀 Building rulesatlas: an open-source Python + R library for game states, legal transitions & outcomes. Fully extensible w custom rules No simulations. Just rules🤖 pip install rulesatlas #RuleAtlas #OpenSource #Python #RStats #AI
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RiskDataScience retweeted
🚨 Fable 5.5 appeared in Azure’s East US 2 model catalog with GenerallyAvailable status github.com/BerriAI/litellm/c…
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RiskDataScience retweeted
Replying to @ChrisGPT
Or alternatively, big models could spawn smaller self-replicating ones as scalable subagents.
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RiskDataScience retweeted
1/15 🧵🚀 The #Mathocalypse is changing mathematics. Our contribution: 14 AI-assisted research papers by RiskDataScience GmbH: 178 pages of proofs, LaTeX, and reproducible code. 179 computational test groups passed. Explore the collection 👇 #AI github.com/rds0001/agentic-m…
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RiskDataScience retweeted
15/15 🌌 P12 | Under a mean-energy budget, uniform hyperbolic volume on a ball minimizes worst-direction infinitesimal Lorentz-boost sensitivity over all probability laws. For E→∞, only 1 spatial dimension reaches zero sensitivity. github.com/rds0001/agentic-m…
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RiskDataScience retweeted
14/15 📐 P11 | Preserving order pairwise is not the same as using one shared random monotone map. For Lorentz order in dimension d≥2 on a radius-R ball, the latter has an optimal uniform mean-bias and RMS-error floor R(d−2)/d. github.com/rds0001/agentic-m…
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RiskDataScience retweeted
13/15 ⚡ P10 | Near a three-level Gibbs state, the first positive work depends on the number-theoretic properties of the energy spectrum. A fixed positive mean yield, however, always requires Θ(δ⁻²) copies. github.com/rds0001/agentic-m…
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RiskDataScience retweeted
12/15 📉 P09 | Globally close models can diverge when their conditional transitions are switched locally. For m positive path models at TV diameter ε, the sharp worst-case gap is Θ(Φ(mε)), where Φ(x)=x(1+ln(1/x)) for 0<x<1. github.com/rds0001/agentic-m…
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RiskDataScience retweeted
11/15 🎲 P08 | N exactly pairwise independent Bernoulli variables at fixed bias p need log₂N+O_p(1) bits of joint entropy. With fixed pair-correlation tolerance ε for fair signs, the leading scale drops to (1−ε)log₂log₂N. github.com/rds0001/agentic-m…
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RiskDataScience retweeted
10/15 🧮 P07 | Proof resources aren't interchangeable. In a specified conservative calculus, polynomial refutations of unary pigeonhole need Θ(n²) fresh definitions in total, yet O(n) simultaneously live names suffice. github.com/rds0001/agentic-m…
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RiskDataScience retweeted
9/15 🔋 P06 | Mean excitation E>1 permits exact correction of any prescribed finite number of phase errors. But at fixed positive dephasing noise, every finite E leaves nonzero error. Reaching error ε costs Θ_p(log(1/ε)) mean excitation. github.com/rds0001/agentic-m…
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RiskDataScience retweeted
8/15 ⚛️ P05 | Pure global quantum states can have exact product marginals on all sufficiently small subsystems. With mean excitation E≥2 for every logical input, maximum exact one-erasure code dimension grows as Θ_E(N^(⌊E⌋−1)). github.com/rds0001/agentic-m…
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RiskDataScience retweeted
7/15 🧩 P04 | For an N-party classical process, validity under N independent copies is equivalent to validity under every number of copies—and to a compatible fixed global order. Correlated extensions follow a different hierarchy. github.com/rds0001/agentic-m…
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RiskDataScience retweeted
6/15 ⏳ P03 | A time-reversible observed process need not imply reversible hidden dynamics. For finite stationary continuous-time Markov chains under ordinary reversal, σ ≥ 16 max(0,−C(t))/(r²t), with C(t) the covariance and r the output range. github.com/rds0001/agentic-m…
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