Maybe the poem has a message
A digital pet squid that also teaches how neural networks and hebbian learning work. Behaviours are driven by the neural network according to his needs:
https://github.com/ViciousSquid/Dosidicus
I spent AGES on this and would love feedback. I think it's just the right balance of educational and fun. I did all the graphics myself and am currently working on multiplayer - squids will be able top enter other tanks and steal things, bring them home
This might also see a long shelf life, say, as familiars of fantasy rpgs, as pets from a fictionalized world-building narrative online; I guess it could be so for any LLM in principle, but the basic Sims-like gamification behind a tamagotchi seems like a solid foundation for those usecases.
Yes, Paul Newman did experience significant struggles with alcohol. In his posthumously published memoir, The Extraordinary Life of an Ordinary Man, Newman candidly discusses his drinking habits and acknowledges his long-term battle with alcoholism. He describes himself as a "functioning alcoholic," a trait he noted was shared with his father. At one point, Newman was reported to consume a case of beer daily, followed by spirits, until he eventually gave up hard liquor.
Why should print be so specifically necessary if a book's content is what defines it? That I might read, say, Umberto Eco, in digital makes it no less intellectually valuable than if I bought a paperback version, or if you want to get really fancy about things, a hard cover, if those are still even released...
If anything, being able to carry hundreds of books of all kinds around with me nearly anywhere on my Kindle, or even on my cell phone, makes it all the easier to read more voraciously. With this it requires no extra effort beyond that of having with you a device that you'd in any case carry, and thus taking advantage of many more spare moments between daily activities..
God I miss old Scientific American. Today's SA isn't especially terrible, but old SA, like old BYTE, was reliably enlightening.
I don't know about the work's true impact on AI or tech languages, but it's a masterpiece of criticism, analysis and penmanship.
By this I mean that to make confident predictions, you need some serious statistics, but psych is one of the least math heavy sciences (thankfully they recently learned about Bayes and there's a revolution going on). Unlike physics or chemistry, you have so little control over your experiments.
There's also the problem of measurements. We stress in experimental physics that you can only measure things by proxy. This is like you measure distance by using a ruler, and you're not really measuring "a meter" but the ruler's approximation of a meter. This is why we care so much about calibration and uncertainty, making multiple measurements with different measuring devices (gets stats on that class of device) and from different measuring techniques (e.g. ruler, laser range finder, etc). But psych? What the fuck does it even mean "to measure attention"?! It's hard enough dealing with the fact that "a meter" is "a construct" but in psych your concepts are much less well defined (i.e. higher uncertainty). And then everything is just empirical?! No causal system even (barely) attempted?! (In case you've ever wondered, this is a glimpse of why physicists struggle in ML. Not because the work, but accepting the results. See also Dyson and von Neumann's Elephant)
I've jokingly likened psych to alchemy, meaning proto-chemistry -- chemistry prior to the atomic model (chemistry is "the study of electrons") -- or to astrology (astronomy pre-Kepler, not astrology we see today). I do think that's where the field is at, because there is no fundamental laws. That doesn't mean it isn't useful. Copernicus, Brahe, Galileo (same time as Kepler; they fought), and many others did amazing work and are essential figures to astronomy and astrophysics today. But psych is in an interesting boat. There are many tools at their disposal that could really help them make major strides towards determining these "laws". But it'll take a serious revolution and some major push to have some extremely tough math chops to get there. It likely won't come from ML (who suffers similar issues of rigor), but maybe from neuroscience or plain old stats (econ surprisingly contributes, more to sociology though). My worry is that the slop has too much momentum and that criticism will be dismissed because it is viewed as saying that the researchers are lazy, dumb, or incompetent rather than the monumental difficulties that are natural to the field (though both may be true, and one can cause the other). But I do hope to see it. Especially as someone in ML. We can really see the need to pin down these concepts such as cognition, consciousness, intelligence, reasoning, emotions, desire, thinking, will, and so on. These are not remotely easy problems to solve. But it is easy to convince yourself that you do understand, as long as you stop asking why after a certain point.
And I do hope these conversations continue. Light is the best disinfectant. Science is about seeking truth, not answers. That often requires a lot of nuance, unfortunately. I know it will cause some to distrust science more, but I have the feeling they were already looking for reasons to.