Zeno’s Paradox And The Problem Of AI Tokenization Machine Learning Tech Brief By HackerNoon podcast

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Zeno’s Paradox and the Problem of AI Tokenization

6d ago 7:56

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Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/zenos-paradox-and-the-problem-of-ai-tokenization.
Token prediction forces LLMs to drift. This piece shows why, what Zeno can teach us about it, and how fidelity-based auditing could finally keep models grounded
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai-tokenization, #generative-ai-governance, #zenos-paradox, #neural-networks, #ai-philosophy, #autoregressive-models, #model-drift, #hackernoon-top-story, and more.
This story was written by: @aborschel. Learn more about this writer by checking @aborschel's about page, and for more stories, please visit hackernoon.com.
Zeno Effect is a structural flaw baked into how autoregressive models predict tokens: one step at a time, based only on the immediate past. It looks like coherence, but it’s often just momentum without memory.

… continue reading

442 episodes

#News #Tech News #HackerNoon #Machine Learning #Machine Learning Stories #Ml Professionals #OpenAI

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Zeno’s Paradox and the Problem of AI Tokenization

Machine Learning Tech Brief By HackerNoon

published 6d ago

Share

MP3•Episode home

Content provided by HackerNoon. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by HackerNoon or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/zenos-paradox-and-the-problem-of-ai-tokenization.
Token prediction forces LLMs to drift. This piece shows why, what Zeno can teach us about it, and how fidelity-based auditing could finally keep models grounded
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #ai-tokenization, #generative-ai-governance, #zenos-paradox, #neural-networks, #ai-philosophy, #autoregressive-models, #model-drift, #hackernoon-top-story, and more.
This story was written by: @aborschel. Learn more about this writer by checking @aborschel's about page, and for more stories, please visit hackernoon.com.
Zeno Effect is a structural flaw baked into how autoregressive models predict tokens: one step at a time, based only on the immediate past. It looks like coherence, but it’s often just momentum without memory.

… continue reading

442 episodes

#News #Tech News #HackerNoon #Machine Learning #Machine Learning Stories #Ml Professionals #OpenAI

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