This would be explicitly invalid in Germany in particular, as they encoded common practice into law! In other jurisdictions, as discussed, you'd have to argue the case based on caselaw, e.g. for new mediums unknown at the time. https://
cambridge.org/core/books/int
ellectual-property-licensing-and-transactions/license-grant-and-scope/
…
@alexjc
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German IP Law Invalidates License Scope in New Mediums
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Copyright Transfer Laws: Unequal Legal Treatment for AI Creators
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Except you can't broadly transfer your Copyrights to unknown applications in many jurisdictions (EU+US). The creators would win in court if it wasn't a two-tier legal system: good rules for big publishers, bad ones for individuals.
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Decentralized CLI tool for bot messaging and LAN control
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@keet_io Any chance of a simple CLI for bots to send and read messages? Could be connected to existing account from the app, or a new terminal-only accounts… Need a decentralized interface to control bots on LAN and/or robust to cloud failures!
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Integrating AI Innovations into Competition Rules
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Yes, looked at those and other papers too — so much low hanging fruit there! The hard part is fitting those innovations into the context of a competition (what the rules should be); there was at least one attempt in doing that I think…
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Token Undertrain and Variance in Language Model Training
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Many papers! The tokens were significantly undertrained already last year, and the trend towards even fewer tokens likely just makes the variance worse! NanoGPT's small competition hides variance problems by gathering statistics over many seeds, IMHO just switch to medium size.
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Are Claude Instances Shared Among Anthropic Staff Teams
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I would be fascinated to hear if the Claude instances doing the coding and research are *shared* between the human staff… (Presumably the bug-fixing Slack bot is.)
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Don’t Trust Verify: Research on AI Model Reliability
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[1] Don't Trust: Verify (ICLR 2024) https://
proceedings.iclr.cc/paper_files/pa
per/2024/file/0a79ecda13603817de4cdfc68b417e89-Paper-Conference.pdf
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DTV Math Model: CPU-First Approach vs Symbolic Specialization
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The DTV paper[1] from 2024 uses a 62B model specialized in math and reasoning, building up from 48.6% baseline by putting more emphasis on the symbolic side. What if we did the opposite, matching the baseline with specialized system on CPU and adding more parameters to improve?
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2.6K Parameter RL System Matches 62B Specialized Model
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Oh My! I just built a new RL-native system with 2.6k parameters that matches a specialized 62B model from a 2024 paper, specifically on GSM8k. That's over 20,000,000x smaller than the baseline — but the approach is so different the traditional comparison doesn't make sense? 😉
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Hypocrisy in AI: Silent until personal impact
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As it stands, he just looks like the guy "When they came for others I said nothing", but suddenly "they came for me…"