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> On 14 Sep 2023, at 10:06, Alberto Cammozzo via nexa 
> <nexa@server-nexa.polito.it> wrote:
> 
> Segnalo questo pezzo, a mio giudizio molto equilibrato e competente, di Brian 
> Hayes contra Welch, con un gustoso confronto tra Knuth e ChatGpt.
> 
> Il punto cruciale per me è questo: "we should blame the [] unavoidable 
> weakness of a protocol in which the most frequent answer is, by default, the 
> right answer".
> <http://bit-player.org/2023/ai-and-the-end-of-programming>
> 
> 
> Earlier this year Matt Welsh announced the end of programming. He wrote, in 
> Communications of the ACM:
> 
> I believe the conventional idea of “writing a program” is headed for 
> extinction, and indeed, for all but very specialized applications, most 
> software, as we know it, will be replaced by AI systems that are trained 
> rather than programmed. In situations where one needs a “simple” program 
> (after all, not everything should require a model of hundreds of billions of 
> parameters running on a cluster of GPUs), those programs will, themselves, be 
> generated by an AI rather than coded by hand.
> 
> A few weeks later, in an online talk, Welsh broadened his deathwatch. It’s 
> not only the art of programming that’s doddering toward the grave; all of 
> computer science is “doomed.” (The image below is a screen capture from the 
> talk.)
> 
> [...]
> 
> I wanted a problem that can be solved by computational means, but that 
> doesn’t call for doing much arithmetic, which is known to be one of the weak 
> points of LLMs. I settled on a word puzzle invented 150 years ago by Lewis 
> Carroll and analyzed in depth by Donald E. Knuth in the 1990s. 
> [...]
> 
> At this point I must concede that ChatGPT, with a little outside help, has 
> finally done what I asked of it. It has written a program that can construct 
> a valid word ladder. But I still have reservations. Although the programs 
> written by GPT-4 and by Knuth produce the same output, the programs 
> themselves are not equivalent, or even similar.
> 
> [...]
> 
> Why do the chatbots favor the inferior algorithm? You can get a clue just by 
> googling “word-ladder program.” Almost all the results at the top of the list 
> come from websites such as Leetcode, GeeksForGeeks and RosettaCode. These 
> sites, which apparently cater to job applicants and competitors in 
> programming contests, feature solutions that call for generating all 125 
> single-letter variants of each word, as in the GPT programs. Because sites 
> like these are numerous—there seem to be hundreds of them—they outweigh other 
> sources, such as Knuth’s book (if, indeed, such texts even appear in the 
> training set). Does that mean we should blame the poor choice of algorithm 
> not on GPT but on Leetcode? I would point instead to the unavoidable weakness 
> of a protocol in which the most frequent answer is, by default, the right 
> answer.
> 
> [...]
> 
> For decades, architects of AI believed that true intelligence (whether 
> natural or artificial) requires a mental model of the world. To make sense of 
> what’s going on around you (and inside you), you need intuition about how 
> things work, how they fit together, what happens next, cause and effect. 
> Lenat insisted that the most important kinds of knowledge are those you 
> acquire long before you start reading books. You learn about gravity by 
> falling down. You learn about entropy when you find that a tower of blocks is 
> easy to knock over but harder to rebuild. You learn about pain and fear and 
> hunger and love—all this in infancy, before language begins to take root. 
> Experiences of this kind are unavailable to a brain in a box, with no direct 
> access to the physical or the social universe.
> 
> LLMs appear to be the refutation of these ideas. After all, they are models 
> of language, not models of the world. They have no embodiment, no physical 
> presence that would allow them to learn via the school of hard knocks. 
> Ignorant of everything but mere words, how do they manage to sound so smart, 
> so worldly?
> 
> [...]
> 
> 
> 
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