Forget the hype, “thinking machines” can’t replace humans
&gt; [Video @ 21:50] I don&#x27;t know about you, I put my car in reverse and if it gets too close to something it beeps because it&#x27;s aware of its surroundings, right? Does that make my car self-aware?<p>I wouldn&#x27;t call sensing the external environment &quot;self-awareness&quot; even for humans - it&#x27;s more about ability to inwardly inspect our own thoughts and have an internal model of ourself. If you take some entity with a train of thought and then you give it that ability, I&#x27;d probably say you gave it self-awareness.<p>&gt; [Video @ 25:18] Computers are constrained by something called the Church-Turing thesis, which says anything you can do on a computer of today or a computer of the future could be done on Alan Turing&#x27;s original 1938 Turing machine<p>&gt; Now today&#x27;s computers can do things millions billions of times as fast.<p>A Turing machine is an abstract model of computation with infinite time and memory. Maybe nitpicking, but I feel it&#x27;s being talked about here as if it were some real physical machine.<p>&gt; [Video @ 26:35] You have an algorithm on your shampoo, right? &quot;Wet, apply shampoo, lather, rinse, repeat&quot;. Unfortunately if a computer was looking at this what would happen? You would wash your hair forever wouldn&#x27;t you? Because it doesn&#x27;t say &quot;rinse once&quot;, it says &quot;rinse, repeat&quot;.<p>&gt; [Article] The computer will not do anything that departs from its programming. That’s a human specialty.<p>I think there&#x27;s conflation between instructions given to some agent and its low-level underlying programming (floating point math, or chemical interactions for us).<p>Modern AI would likely be capable of using context and understand the intended meaning of the video&#x27;s given examples, or disobey a given instruction.<p>&gt; [Video @ 28:50] The first one he did is something called the Turing halting problem.<p>You can&#x27;t correctly answer a question like &quot;What won&#x27;t you answer this question with?&quot; - halting problem is effectively this. Less a limitation specific to computation, more about showing that some tasks are sufficiently non-trivial to embed this kind of paradox so can&#x27;t be solved in all instances.<p>&gt; [Video @ 30:58] Imagine trying to explain your experience to a man who has been blind since birth. [...] but duplicating the experience that you&#x27;re having, the simple experience of seeing green, is not possible to describe to the blind man to the point where he can experience it also<p>Could probably build up the concept and associations of green in his head, but the visualization part is going to be limited by neural pathways in&#x2F;between visual cortex not being properly formed without having received signals from the eyes. Sufficiently advanced future neurosurgeon could make someone experience green without actually having seen green, I&#x27;d bet.<p>&gt; [Video @ 31:24] Now if we can&#x27;t explain it to a blind man then how are we ever going to write a computer program to have qualia? And the answer is we won&#x27;t.<p>Consider a text-based agent that can reason and introspect. How would it describe the tokens of text that it receives? I reckon similar to how we consider qualia - seemingly irreducible inputs that are hard to explain in terms of anything else.<p>&gt; [Video @ 31:48] Understanding is something that computers will never do. This was established a long time ago by [Chinese room example], but does the person inside the room understand Chinese? No, he is exercising an algorithm.<p>I think one problem with the thought experiment is that people imagine the person in the room&#x27;s procedure to be relatively tractable - like replacing English characters with a couple sets of intermediate characters, and then finally to Chinese.<p>While you can translate Chinese just with look-ups and writing symbols (with unbounded time&#x2F;memory), to do so at a human-Chinese-speaker level would currently (until machine translation improves) involve using symbols to simulate arithmetic, to simulate quantum field theory, to simulate chemical interactions, to simulate a Chinese speaker&#x27;s head.<p>I personally believe the answer of whether the system as a whole understands Chinese at that point has to be &quot;yes&quot;, but at the very least it&#x27;s not a clear &quot;no&quot;.<p>&gt; [Video @ 35:58] Rather I like the proposal made by Selmer Bringsjord called the Lovelace Test for strong AI. That is as follows: &quot;Strong (or General) AI will be demonstrated when a machine&#x27;s performance is beyond the explanation of its creator&quot;<p>This was originally proposed in 2001, and I feel since then it has been accomplished by deep learning. Leaps in performance that take theory a while to catch up and understand what it&#x27;s actually doing, agents that cheat games in unintended ways not previously thought possible, or unexpected generalization ability to novel tasks.<p>I think this definition of strong AI is generally too lenient. Although in the opposite direction, given this is a Christian conference and beliefs on God&#x27;s omniscience, it doesn&#x27;t seem like they&#x27;d consider humans to meet the bar.<p>&gt; [Video @ 37:12] All computer programs have done what they were designed to do.<p>That would make my job a lot easier!<p>&gt; [Video @ 37:30] Can AI create music? No it can&#x27;t create music, do you know what a typical scenario of creating music is? Say you want to have a computer program AI generate baroque music, what do you do? You feed it a bunch of musical scores which were written by Bach. What&#x27;s it going to generate? It&#x27;s going to generate a musical score which sounds like Bach. It&#x27;s not going to generate Wagner&#x27;s music or Schoenberg&#x27;s music or any of the more modern music, it&#x27;s only going to generate things that sound like Bach, it just does the interpolation. So again it&#x27;s this idea of interpolation, that we have. So no a computer cannot create music.<p>On sufficiently high dimensional data like music, novel examples are essentially always going to be extrapolation. If there&#x27;s acceptance that it can learn from pieces of music and produce new pieces, with ability to vary similarity to existing piece distribution, then I don&#x27;t see the objection to it not being able to do the same with musical styles.<p>&gt; [Video @ 41:16] [...] totally splits the brain. Now if this is true, shouldn&#x27;t we end up with a split personality after it was over if the mind was the same as the brain?<p>By materialism there&#x27;ll be no direct communication between the halves of a split brain, and that&#x27;s what&#x27;s observed. It doesn&#x27;t imply anything about whether both halves of the brain have the capability to develop personality traits, or that they&#x27;ll noticeably diverge even given roughly the same experiences.