"What is your religious affiliation". Seems perfectly innocuous, but turned out to be retroactively fatal if your answer could be attributed to you by a certain foreign occupier in the 1940s .

B) Say you have a slow optimizer in a fast world: a lot of the time the optimal solution is going to be some form of computational generalization. Now you have meta-optimization. Life seems to enjoy doing this recursively.

C) Crow intelligence is clearly highly evolved, so you're technically correct, best kind of correct. Though here I'd argue that a very parsimonious answer is single-lifespan learned behavior. You're applying an existing learning system, no new mechanisms needed. (As opposed to positing some new evolved fixed action pattern).

D) There's not even anything stopping it from being planned behavior. Searle is struck out because it is biological; and no one can accuse us of anthropomorphism HERE!

E) Actually, for sparse events, planning using a world model can be more parsimonious. Apply existing model to new problem, again no extra mechanism needed. Which one works better for a particular entity in a particular situation depends on tradeoffs. (For a human example: see eg Memory items vs checklists vs airmanship in eg aviation)

F) That said, I'd even count evolution as a form of intelligence (well... it's an optimizer at least). I will literally die on this hill, and so will you O:-) (unless you represent optimums as valleys) ---> Plot evolution as a dynamic system in phase space, or with your typical hill-climber/gradient descent representations. How much does the trajectory differ from other optimizers? What happens if the 'terrain' is very bumpy with many local optimums? What if it deforms as you cross it?

Edit:

> If you’re wondering what the story is, [...] I expect it to be out in the next two weeks [...] I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.

Ok, this takes clickbait to new lows. The headline is trying to sell the teaser here, with very limited meat in the middle of the sandwich.

Oh dear. Funny story.

So the other month, I made a quick and dirty Eliza implementation; bolted on the crappiest numeric sentiment classifier I could get away with (regex), and integrated the output of the classifier over time in a 'functional affect vector' (aka. emotion vector)

Anyone's intuition will tell you that this cannot POSSIBLY have 'Real Feelings (TM)'; and that's the whole point.

A) It was still capable of quite a bit of functional affect though; to wit I got it to trigger fireworks when happy, and rain when unhappy. This was the actual point of the exercise. Functional Affect Does The Thing, QED, yay me.

After that it gets annoying though.

B) Am I allowed to say it's happy or sad? Well... I mean emotion.happy=0.995 and emotion.sad=0.001. "It's really happy" is a prosaic description of a real numeric value representing a real functional state. What else am I supposed to call it? I swear I never meant to go there, and now I'm stuck with it.

C) So, we all know that it's a crappy demo, not the real thing. So I ducked into the psychology literature to try and find a protocol to disprove. For Science! And this is where the psychology literature really let me down.

So now I'm stuck with the crappiest thing that can plausibly still chat, and where I can't actually disprove it has emotions. Not properly, at least. And I'm not saying it's because it has emotions, because that would be really funny, but no.

I'm saying that -despite lots of people having fun debates at the local pub- it doesn't seem like anyone actually scientific has done anything about it in the last century or so. I might be searching in the wrong places. Some Help Here?

I'd argue the qualia question is a red herring. Functional Affect is a thing, regardless of ontological status. It's all fun and games until someone gets hurt.

To paraphrase Dijkstra: "The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.". If you're building a navy: you care about displacement, propulsion, navigation and whether it can fire torpedoes. Whether your submarine has some "biological essence" of swimming is not really relevant to the fact that it is currently moving through the water and can collide with things. Turing also rejected the question "Can Machines Think" as posed, and replaced it with an operationalization (something else that we can actually usefully measure and work with).

To reiterate, functional affect is a concrete phenomenon. Whether or not there is a what-it-is-to-be is interesting in the abstract, but engineering a system means looking at how the inputs influence the outputs. A next token predictor working on a language that communicates affect needs to be able to predict affect or it is simply not going to be accurate. Given an 'angry' version of an input and a 'friendly' version of the same input, LLMs are likely to provide a different output, especially if there's a non-objective element. You can diff this.

Searle argues "A simulation is not the real thing", which is great and all... but if you hook up say an autopilot to the real world (as llms increasingly are) , you'd best hope the simulation was accurate in the first place (utterly regardless of where you stand on Searle).

Right now we're seeing situations where LLMs can be helpful or a real nuisance. Ignoring functional affect out of sheer ideology means you can't properly predict what they'll do, and that causes trouble, as we've already seen stories about.

This gets especially interesting when you start feeding the output back into the input (autoregression) , because now you have a highly non-linear dynamic system and you've introduced some amount of sensitivity to initial state. There's some interesting mathematical intuitions to be had there.

That may or may not be true, people are justifiably (or not justifiably) angry about a lot of things all the time.

But this was very obviously a brigading attempt. It's a form of online bullying. If it had been about whether the maintainer liked striped socks, nothing else about this would have changed.

Later on the brigade can claim "oh we had a justifiable grievance" to sooth their souls, but what actually happened is what actually happened.

It's all a bit silly and childish.

(To be sure: the balance of fao_'s statement is well reasoned. It's the brigade who are being childish, and I don't think they should be rewarded for that. )

I don't know what sets this kind of thing off, maybe it's not predictable, but it's never ok.

I'd like to hope in a few years those people will look back on their participation in this particular brigade sheepishly; but sadly it's more likely they'll have forgotten about it by morning.

So instead of just sampling in a close 'circle' around your current point looking for a 'down', how about we spread that out a bit? You could use a 'circle' in a regular pattern, but what does that even look like in high dimensional space? Seems it's best to use some random distribution centered on your current position.

(LLMs actually have a 'temperature' setting which introduces noise for this exact reason.)

Some of GA's claims to fame are A) it uses purely just this distribution to descend. B) It can find multiple optima.

The way I think of it is that the simplest GA is basically greedy optimization with spread.

Greedy is like shooting a rifle , which is great for sniping, but you'll miss if the target is moving fast or doing things you can't quite keep up with.

A GA -like a shotgun- introduces spread: multiple chances to hit, multiple chances to escape local optima and rough patches in the landscape.

(A really good -if slightly morbid- modern example in the wild is COVID; which managed to outwit human civilization rather handily. "Not bad for a bit of encapsulated RNA" you'd think - until you realize it was running trillions of attempts in parallel. Really, the poor governments had no chance. )

Technically birdshot from a shotgun is also randomly distributed (passing through a cone). This actually improves the chance of hitting the clay pigeon, because the birdshot spreads out and each individual ball has a chance to hit.

Genetic algo is similar. it's an optimizer that - in order to avoid local optima - will 'shotgun' an area around its current best guess.

   if "needle" in haystack:
      print ('haystack.__contains__("needle")')

So obvious that -to be fair- I blanked for a moment too. But 'in' is an operator, not a method (even though it calls __contains__ under the hood) . The question might have been slightly malformed?

https://meta.wikimedia.org/wiki/Talk:Community_Wishlist#Upda...

may 24:

https://meta.wikimedia.org/wiki/Talk:Community_Wishlist#Resp...

Even early on, there were graphical browsers, text mode browsers, tools like wget and curl (not everyone had access to a graphical desktop!). Services like AltaVista allowed one to approach the web in yet another way, spidering the web and surfacing information through full-text-search that couldn't be found through just surfing.

And this is before we get to people with visual impairments using screen-readers or braille terminals. Or folks connecting to the internet over expensive satellite or infrequent wifi who might want to batch their web-fetches and read in their own time.

As far as I'm concerned you're just continuing that tradition with more modern tools. Semantic tools. Carry on. Put your tools on a git site someplace if you'd like to share!

ps. see also the exploits of https://en.wikipedia.org/wiki/James_%22Kibo%22_Parry (he grepped the entire usenet feed for mentions of his name, and allegedly always answered) . Use your powers for good! (see eg altairprime's comment for a definition of 'good' https://news.ycombinator.com/item?id=48260183 )

pps. if I want to telnet info.cern.ch 80 I should still darn well be able to! Uphill, both ways, in driving snow!

ppps. https://line-mode.cern.ch/www/hypertext/WWW/TheProject.html <- eg using a line mode browser.

So long as only a few countries are doing this, it might seems doable. If everyone starts doing it, international travel becomes rather annoying to say the least. Realistically I think at some point a detente might want to be reached, with everyone agreeing not to search everyone else's electronics.

But then .. stuff like this happens, and now of course people's priors update that all LLMs in all configurations can't be trusted with sourcing.

It's like the 1980's with 8-bit machines, or the 1990's with the introduction of the internet. One half of the world thinks it's a fad. And to be fair! They keep getting confirmation because of course the new tech fails in weird and interesting ways.

The other half meanwhile quietly gets on, quietly patches the bugs as they are discovered, and do get meaningful work out of it.

Neither the hype nor the skeptical version are entirely correct.

Couldn't possibly happen with railroads!

https://libertystreeteconomics.newyorkfed.org/2016/02/crisis...

That does seem rather more polite and respectful of people's time.

I do have this intuition though: some variant of Gresham's law or Akerlof's Market For Lemons may apply [1][2].

It takes more effort to put together a well compressed post or message; it'll drown in the sea of content which is easier to to type or generate; and it is hard to distinguish.

(Also: how many people think to eg. drop a post into an LLM to find the components expanded back out? )

[1] https://en.wikipedia.org/wiki/Gresham%27s_law [2] https://en.wikipedia.org/wiki/The_Market_for_Lemons

But why would this automatically cause the teacher to be the one to retire?

Are there documents or books on this? This system seems so alien to me. And yet it does seem to produce some amount of competent graduates who can -eg- launch a spacecraft into lunar orbit.