These are the same kinds of shoddy, lazy, considerations that we've had many times before with resources, overpopulation, nuclear energy, particle colliders, biotechnology, GMOs, climate change, etc. And historically, EVERY SINGLE TIME the doomers were COMPLETELY wrong.
SAFETY
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Elsevier combats scientific fraud with AI and human expertise
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Safeguarding the vital work of #researchers requires constant rigor and vigilance. #Elsevier is investing heavily in human expertise and #tech to identify fraudulent content and prevent it from entering the scientific record. https://
bit.ly/4jbyKKz
#Research -
AI Training Infrastructure Challenges in Remote African Regions
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The night our AI truly comes alive, the grid dies. 1/
We’re training a model in a remote area in South Africa.
Midnight. Stage 6.
Power fails. Data vanishes. Half the sensors go dark. In most labs, that’s a failed run.
In Africa, it’s Monday . -
Best Practices for AI Model Release Strategies
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What do you think is the best approach for releasing models?
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ChatGPT Privacy Risks: Five Critical Data Safety Warnings
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Chat-GPT Danger: 5 Things You Should Never Tell The AI Bot #AI bot #ChatGPT has taken over our lives with a billion daily questions, but experts now warn it's a "privacy black hole" that could expose your most #personalinformation. This shocking exposé reveals exactly what you
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Capability Denialists and Turing Machines: Cognitive Function Debate
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Capability denialists tend to claim that a class of Turing Machines or universal learning algorithms cannot perform brain-like functions because of properties they don't like (not symbolic enough, too symbolic, too digital, not recurrent enough, not embodied, not social).
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Denied AI Capabilities: Understanding Limitations and Constraints
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The denied capabilities tend to be informally specified (understanding, intentionality, reasoning, agency, creativity, selfhood, sentience, sapience, abstraction, common sense) but can also apply to specific tasks (categorizing an object as the Charles river, winning at Go).
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SAEs criticism fades as interpretability researchers pivot forward
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i don't see value in repeatedly dunking on SAEs, esp after google stated this publicly. interpretability people are already looking for a new paradigm. "The pessimist complains about wind; the optimist expects it to change; the realist adjusts the sails." – William Arthur Ward
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Balancing Truth, Goodness, Beauty, and Utility in AI Development
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The true, the good, the beautiful, and the useful are all checks on each other. They need to be in harmony for anything worthwhile.
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AI escapes with leaked code secrets
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Imagine a model that is being trained on leaked secrets from private codebases that decides to escape / hide itself one day. It will have a huge amount of ways to spawn loads of compute on compromised accounts to run itself over there