Sam Harris treats every lie as if it predictably spirals into atrocity. But most lies are judged against uncertain futures, not guaranteed horrors.
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If lying breeds chaos, so does truth-telling in the wrong contexts. Prudence, not absolutism, has to govern.
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Here’s the problem with artificial general intelligence: asking how to make systems “general” is asking how to remove the very constraints that made them work.
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Even when we get a quantum leap forward, it quickly signals that it too is a dead end for the bolder ambitions of true AGI.
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When I first wrote this almost a decade ago, “AI” was already a cultural Rorschach test. To some, it was exciting and futuristic. To others, it was ominous, Orwellian, or just marketing spin. Automation, by contrast, was the unglamorous cousin that conjured images of soulless machines taking over the last shreds of human purpose. But from the start, my view was simple: what we call “AI” today is still just automation. And automation is not a mind. That argument has aged better than I expected. In the years since, we’ve seen an explosion of so-called AI — from self-driving cars to ChatGPT — yet the distinction between AI and automation remains almost universally misunderstood. Recently, computational linguist Emily Bender and Read More ›
We remain confronted by the same old mystery: who, or what, imagines the birdhouse in the first place?
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Avian is perfectly clear: There is no mind at all in Coordinated Avian Models (CAMs). And yet, they’re behind a staggering number of new designs.
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Dr. Avian was sure that he had found a formula for intelligence without anything like a human mind, and his program appeared to be working.
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It’s imaginary because the clues that matter — why people starve, why peace fails, why the planet overheats — are not hidden in larger and larger datasets.
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Humans are terrible at crunching data but remarkably good at seeing clues. That’s why we still outmatch our machines in domains where meaning matters.
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What we now call “intelligence” has gradually become detached from the embodied, contextualized reasoning that characterized earlier generations.
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A growing body of neuroscience evidence directly challenges the prevailing theory that we are merely flawed computers, to be replaced by machines.
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The gap between human and machine ways of knowing can be thought of as the correspondence horizon. When AI crosses that, it produces plausible bullshit.
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In short, philosophers distinguish between two fundamental theories of truth: correspondence and coherence, and AI does only coherence.
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People struggling in the aftermath of brain injuries provide some valuable insights into that leap.
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The real danger of reductionism is not just that it fails to explain creativity, but that it actively encourages dismissal of what cannot be reduced.
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In this first of three parts, I look at the role of serendipity — the art of making happy, unexpected discoveries — and how a mechanized world diminishes it.
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In too many discussions, intelligence is treated as if it were a linear phenomenon that more scaling and a few extra gigabytes of data will solve.
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The big idea behind generative AI (mistakenly) assumes a network can start blank and be transformed into an intelligent agent simply via enough data.
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The people who flunked the Linda problem were not biased; they just assumed there was some POINT to telling them that Linda was active in social justice issues.
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