AI belongs to this world of necessity and it is bound by the inputs we give it: code, training data, prompts. It has no imagination.
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First, we must remind ourselves that “data is not the plural of anecdote.”
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The Stanford Lab for Mental Health Innovation examined the unique risks of chatbots harming children. In many ways, this study confirms the obvious.
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Lazy lawyers have no doubt filed hallucinated content orders of magnitude more often.
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Russian philosopher Nikolai Berdyaev long ago argued that human creativity springs from freedom — just what machines do not have.
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Because he says the “genie” (or maybe the GenAI) “is out of the bottle,” Dr. Dembski wants me to submit to the myth of progress by sharing even one use of AI in education that I support.
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His paper, “Artificial General Intelligence Is Nowhere Near, Artificial Specific Stupidity Is Already Here,” challenges both hype and groundless fear.
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Dr. Dembski wants us to use technology with wisdom. I share his broader goal, but not the use of generative AI chatbots to get there.
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When you prompt an LLM, the entire conversation acts as a prompt driving the reply. Thus your prompt can push the LLM in unforeseen, unintended directions.
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In Homann’s view, AI is best seen as “autocomplete with ambition.” But that doesn’t mean it can’t be quite useful.
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Claims that an LLM is intelligent don’t square with the fact that it may answer a question one way and, a few seconds later, simply contradict itself.
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Ultimately, the AI knowledge system will be enriched or impoverished by human decisions, not by discovering a superintelligence.
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The authors really believe in AI doom. Soares has stopped contributing to a retirement plan. I show that the serious problem is NOT superintelligence.
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Even if we adopt a materialist view of the mind, I believe I can show why it is not possible for a machine to develop a mind.
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Bottom line: Text generative AIs do not capture meaning. They capture relationships which approximate meaning well enough to be useful.
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By 2020, authors Yudlowsky and Soares were already Doomers but the rapid success of ChatGPT and similar models heightened their worries.
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If intelligence essentially involves agency, artificial general intelligence (AGI) in the human sense is not attainable by computation alone.
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Mappouras doesn’t rule out creativity in machines; he wants to develop a test that could recognize it.
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Human trainers can nudge LLMs to give good answers for specific questions but this training doesn’t prepare them for subtle variations.
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As prominent neuroscientist Christof Koch’s recent difficulties with Cancel Culture show, the cracks in materialist neuroscience are getting harder to paper over.
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