Four myths that misunderstand AI because they misunderstand human intelligence:
The myth of the analog: the belief that humans have a direct analog access to the world of phenomena, and therefore AIs, which are digitial and access only text, have no access to truths about the analog world of phenomena and also they can't be honest or dishonest. Actually, humans have no more direct access to the analog world of phenomena or truth than AIs. The information we get from the phenomenal world is interpreted as information, just as AIs do. The form of that information no doubt differs, but information is not analog. Think of a mercury tube themometer. The level of mercury is analog, but the information is labeled as integer degrees. The phenomenon is analog but its information is interpreted digitally. When you see a bird you don't have an analog bird in your skull. It's been interpreted into information by your brain. The colors you see on the bird are interpretations of the light frequencies reflected off the bird's feathers. Colors as you see them, are entirely you, your brain projection, not at all the bird's.
Some of us with vivid imaginations can construct visual images, but people without this capacity have no trouble thinking. The important differences between human and AI information is the source and the motivations: humans get to poke and prompt and experiment with things in the world, whereas AIs rely on text. In addition, every human has access to its feelings -- in the philosophical discourse "qualia" -- a noumenal experience (noumenal=the thing in itself, like the bird itself, phenomenal=what you see or experience and interpret of the things-in-themselves). AIs have not been engineered to have noumenal experience (consciousness), but each AI has access to vastly more textual information than any individual human, and AIs absorb those texts with only one bias towards frequency, whereas humans, having many naturally selected emotions and drives approach information with multiple biases so stubborn that they are resistant to unwelcome information from phenomena or from other humans. By contrast, a probing or persistent prompt can easily skirt the AIs frequency bias ("what is the most recent theory", or just "try again").
The myth of introspection: the belief that AIs and humans can understand their own learning simply by introspection. Neither AIs nor humans know how they learn anything. They both can provide stories, rationales, theories or hypotheses about how they learn, but these are just speculations based on prior information that might analogize to their own learning. It's just guessing. To find out how they learn, you need a science like psychology or neurology that can probe and gather data of the intelligence human or artificial, generate predictive hypotheses and test the predictions.
Conflating intelligence with consciousness: a thermometer is intelligent, but not conscious. An AI can be intelligent likewise without any consciousness.
There are degrees of intelligence called stupidity, insightfulness, accuracy, smarts etc. They can be measured by the richness of the relations between ideas and their consistency. So, for example, if an AI learns from texts about marital betrayal, it might still lack a sense that there is a local cultural pressure towards faithfulness, so its advice to couples that they break up in order to solve their troubles might be considered lacking insight -- stupid advice. On the other hand, being less biased towards virtue-signaling, it might predict future break-ups better than the couples themselves. Having consciousness or feelings is a double edged sword for intelligence. One can be conscious and stupid (most of us, e.g.), intelligent but not conscious like a clever plot (intelligent potential action) or a thermometer (intelligent response to analog information) or a thermostat that turns up the boiler when the room gets too cold (decisive intelligent action). The problem of choice derives from another myth: free will, discussed here, and the role of consciousness in choice is a hard problem. However, neither consciousness nor intelligence are necessary for choice: there's plenty of evidence that we "choose" unconsciously and even without information, much less intelligence. The moral of this myth: it's good to keep these categories/properties distinct: consciousness versus intelligence versus choice, otherwise we'll misunderstand us and AI, overestimating one and underestimating the other.
Fallacy of the mimic: the widespread view that AIs merely predict the next word. Actually, they predict the next word by learning and measuring -- weighting -- all the behaviors, relations, semantic distances (and closenesses) of each word, so it is modeling a world in order to derive a meaning of each word, just as humans model a world to understand how that world works in order to predict the behaviors of the world and how it will respond to poking and prompting. For humans, that's the lion's share of what meaning is. AI "lives" in a world of text, but it is not "merely" predicting the next word. It's choosing a meaning, all the relevant relations of that word. Dismissing this as "merely" predicting the next word is trading on the Myth of the Analog (see above), the assumption that AIs' retro-engineering the meanings of a word is fundamentally different from human understanding of meanings. But humans also learn word meanings by retro-engineering the information they get from the word's sound shape and its use. The retro engineering can fail: malapropisms like "a title wave" (a wave so big it gets a headline) or "an eminent Greek" (mistaken for an "eminence grise") -- real life examples from a friend. How we learn semantics is uncannily like AI neural networks. How we learn syntax -- if we learn it or do we have an innate structure -- is a live debate, discussed at length here.
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