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James Rickards

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“Confabulation, or hallucination, is ubiquitous in AI/GPT output already. Efforts to correct this by self-learning algos and back propagation are unlikely to solve the problem because they add to the complexity of the system as a whole, which increases the likelihood of emergent ghosts. The difficulty is that duplicity is hard to detect unless you're a subject matter expert in the topic or you conduct your own research to test its accuracy. This begs the question — if you have to be a subject matter expert to spot the flaws in AI/GPT output, what good is the system in the first place?”

“... bias in training materials is inevitable and there are better ways to deal with adverse effects than to scrub them out of existence. Efforts to eliminate bias would simply create new kinds of bias and distort the validity of the original datasets. Bias can be unjust by some standards but serves a useful purpose. ... the way to deal with it is to use other systems developed by those not involved in the original code, assisted by subject matter experts who could spot damaging bias in AI output and mitigate it in a commonsense manner. The way to deal with bias is not to eliminate it (you can't) but to identify bias and take it into account. Again, education and accountability are better than censorship and new biases.”