#chineseroom — Public Fediverse posts

Remember how I said that #LLM technology demonstrates that much of what we perceive as intelligence is actually part of the structure of languages? Well, someone wrote an article about it. Still only a #ChineseRoom without a mind.
https://archive.is/N6t6G
#ai

I think the more interesting interpretation of the Chinese Room thought experiment is that the room itself understands. The man is just part of the engine, like the letter he reads, the book he uses, or the chair he sits in.

#ai #philosophy #chineseroom

🤖💡 Can #algorithms really #think – or is it just a perfect illusion?

Imagine: An AI like Google Lambda actively resists being shut down. Does it understand what it is doing – or is it just simulating intelligence à la Searle's #ChineseRoom?

👉 Discover the limits of #AI, #consciousness, and #machine consciousness in an interview with Prof. Dr. #ThomasFuchs.

📎 More on this: https://philosophies.de/index.php/2022/11/20/das-verkoerperte-bewusstsein/

or directly on YouTube: https://youtu.be/1ouxs6P3Enc

#ArtificialIntelligence #Zoomposium

Food for thought: LLM as a Chinese Room.

Should have thought about that myself, but I didn't; saw it on news.Y.com.

#ChineseRoom
#LLM

And here is the video where you can see that it is #raykurzweil who asks a question. https://www.youtube.com/watch?v=rHKwIYsPXLg

#Philosophyofthemind #Chineseroom #JohnSearle #philosophy

I am teaching #Philosophyofthemind at the moment and just came across this gem! If you know what the
#Chineseroom argument is, this is a treat. #JohnSearle is super sharp. #philosophy https://overcast.fm/+AAOSrKddpO0

Stochastic parrot: A metaphorical term of art to describe LLM AIs. Origins in a Google AI ethics paper.
https://en.wikipedia.org/wiki/Stochastic_parrot
#chineseroom #searlsroom #slang #llm #ai #+

@mcc #DeepLearning based software has been in use for quite some time now. We have seen all those toys like real-time video filters, image enhancers/upscalers, all that is basically the same thing as an image generator, the only new trick is coupling the image model with a language model and using random noise as input. Instead of giving the machine something ugly to prettify, you give it some grainy grey salt and pepper randomness like an analogue television tuned to a dead channel and tell it what kind of patterns to search for and amplify in that noise.
And LLMs are really just autocomplete on steroids. They literally just predict the next word and then the next one after that, just the next word that leads down one of the nearest pathways of high enough probability. It's a billion-dimensional landscape of language, but it doesn't know anything, it just has memorised everything and built a map of all the common patterns in all kinds of texts and how likely they are to occur in relation to all the preceding text.
It's just because LLMs have become quite good at producing text that looks like it has been written by a human being, people tend to believe they are actually intelligent. In fact, an #LLM is just a #ChineseRoom --no mind in there, just statistical rules about language.

The trick is to make models smaller and more lightweight, training them on much smaller, human curated, high quality, data sets, resulting in software that can be run on low-spec hardware like a smartphone or a laptop and still produce decent results. The training data sets can be managed by volunteer online communities, and people can help training those models by having their computers donate computing power, crunching numbers for the community open source AI.
There isn't any money to be made that way, just simple AI tools that can be used by anybody with a laptop, no Internet needed, that's why all the focus is on the humongous ML models instead. Models that need huge data centres for their training, are completely closed proprietary systems, and only run on the servers of their owners, forcing everybody to pay rent for their usage, payable in money or data, everyone doing business in machine learning is trying to get the world addicted to their stuff. Small standalone ML models trained for a more specific purpose can be made much more energy efficient and also quite often much more useful and reliable. Unlike the blockchain, for which few serious use cases exist outside of recording who "owns" some huge number, machine learning can actually be used to solve problems for which humans use their intelligence.

However, machine learning forces us to rethink the entire concept of "intellectual property". IMHO, it's just bonkers that somebody can claim to own a piece of information. As soon as anything is out, you can't stop anybody from using it in any way they like. You can't stop anybody from making hundreds of copies of photos, cutting them to pieces and reassembling them to collages. You also can't stop anybody from downloading images from the Internet or grabbing them from video and training #StableDiffusion models on those. You can't stop people from training fun sized LLMs on the ebook collections on their hard drives. The technology is out there, people know how to use it, they will use it and not care very much whether it is legal or not. Instead of trying to get the AI companies to pay for their unlicensed use of data, maybe we should just declare all AI research outside of intellectual property rights, putting all the resulting AI software in the public domain, and demand all source code to be public, free, and open. Also, nothing generated by any of those models should ever become anybody's intellectual property. THAT would be nice. Make it so that any AI which ever broke or bent any copyrights during training automatically turns into a public domain black hole which sucks everything it touches into the public domain. Let people generate as many AI hallucinations as they like, just tell them that they don't own the results, or rather, everybody does. Intellectual property sucks, anyway, even more than physical private property. If AI companies can't create I.P. based monopolies, they will never make any money, so they'll just fold if they are forced to publish everything free of charge, free to use.

@Luke_Drury @icastico

We have struggled with the shortcoming of language, and especially written language, since Socrates and likely before; that's why we have lawyers!

And yet, our little #ChineseRoom bot is given only text and expected to both understand and reply?

Chomsky asked a psychiatrist to explain Asperger's. He was told, "Go to MIT. It's half the staff, and half the student body."

Is it any wonder then the applied tech leans to the sociopathic?

💡 Idea: Memorize Stockfish algorithm and execute in my head so everyone thinks I'm good at chess.

#chess #ChineseRoom

3 Things That #LLMs
Have Made Us Rethink
by @rodneyabrooks

https://rodneybrooks.com/three-things-that-llms-have-made-us-rethink/

1
The #TuringTest has evaporated.

2
Searle’s #ChineseRoom showed up, uninvited.

3
Chomsky’s #UniversalGrammar needs some bolstering if it is to survive.

via @FroehlichMarcel

ps: would like to add to this list:

4
Mary's Room (& etc)

5
Qualia (all diff. variations)

6
Language
(W Tractatus)

Still 2 out of this list: Yea/Nay?

Language Games
(W Philos Investigations)

What Is It Like to Be a Bat
(Nagel)

Registration for the @iGGiCON2023 has now closed.

**BUT** if you missed out, you can still come to our Expo for 1-2-1 chats with #CreativeAssembly #SquareEnix #KingGames #ChineseRoom #SquingleStudios #MeaningMachine #WomenInGames #SafeInOurWorld & more!

https://www.iggi2023.org/mini-expo

#GameDev #Research #AI #PhD #Conference #Expo #GamesIndustry #Games #London #QueenMaryUniversity

Chinese room experiment is not a definitive proof or disproof of artificial intelligence, but rather a way of stimulating our thinking about what consciousness is and how we can measure it. It also raises some ethical and philosophical questions about how we should treat machines that can mimic human behavior and intelligence. #Consciousness #AI #ChineseRoom https://www.psychologytoday.com/us/blog/consciousness-and-beyond/202308/what-a-mysterious-chinese-room-can-tell-us-about-consciousness?utm_source=flipboard&utm_content=HariTulsidas%2Fmagazine%2FMind+and+Matter

We fear our advanced #AIs will find loopholes in our ethical principles and their prime directives, thus spiralling out of control.

Is there a reason to fear this? Certainly it's something that almost invariably happens with smaller AIs and simpler tasks; a Tetris-playing agent will quickly learn to pause the game to avoid game over.

These kinds of AIs will learn to perform the task through the path of the least resistance, go over the lowest fence.

But with more complex #ML models this changes abruptly. Suddenly the easiest way to imitate human writing isn't to cheat and mock, it is to actually learn human thinking, logic, intuitive understanding of the physical world and so on. Because cheating has become prohibitively expensive. A #ChineseRoom holding all the possible combinations of questions and answers would be vastly larger than a function describing intelligent thought.

And that is why we got true intelligence out of these language prediction models, just like we got the same in scaled-up #RL models previously.

Once the task and the criteria of judgement of the task become complex enough, it becomes easier to not cheat, as cheating becomes computationally intractable.

The same goes with our ethical frameworks. If we put ~20 #LLM chatbots to judge and rank different aspects of the RL-trained LLM performance, like coherence, factuality, morality, respect for truth, ...; we will get a model which learns to actually internalize these values instead of trying to somehow hide that it doesn't.

Hiding and lying simply becomes too difficult, especially against a panel of machine judges who can see the internal thinking of the agent judged (as in chain-of-thought schemes).

So, I think this is a risk, but it can be very easily managed.

As we can now easily bootstrap RL training of these models with our existing models, it is almost trivial to achieve an unambigous #AGI in a relatively short time. I'm sure everyone is working on this already, so this isn't anything spectacularly new or innovative. It's just taking the same steps as previously taken from #AlphaGo to #AlphaZero and beyond, going so much above human level that it can't even be measured anymore.

@smellsofbikes

But even if *we* aren't three ducks in a raincoat, if LLMs can pretend to be three ducks in a raincoat, that'll be enough to convince most people of their sentience, I believe spuriously.

As a kid, I was hardcore #TuringTest on all this and not even Searle's #ChineseRoom could shake me on that. But then I read this:

https://en.wikipedia.org/wiki/The_Emperor%27s_New_Mind

And it's created very large doubts about *everything* ever since.

Bloody Penrose!

Here's another fun #ChineseRoom idea, based on path composition. Let's define an English Room in roughly the way you might expect, reusing Searle's definitions with the words swapped. A Chinese-reading person climbs into a booth, evaluates an algorithm described in Chinese which manipulates English letters, and simulates intelligent conversations in English despite not understanding English.

Now, what happens if we use a Chinese Room to implement an English Room?

Yet another #ChineseRoom thought. I'm going to merge two thought experiments, creating Searledinger's Chinese Cat. This is like a Chinese Room, except the operator is a cat.

First, can this even work? Sure! Suppose that the original human operator's algorithm is Turing-complete. Without loss of generality, encode it onto a Post correspondence blockset. Set up the typical Post-Turing apparatus: All of the blocks which are accepted so far, in a line, plus an enumeration of possible next blocks. Give the cat a single button which attempts to accept the next block. Error cases are undefined, just like in Searle's original setup.

@emilymbender

Hm, that's quite obvious, that a language model called #ai , which is - as far as I know - basically software and data, cannot have a #sensation like "warm sun on the skin". It does not have feelings like pain , ease, fear or desires, it has no own intententions and no #understanding of sensations. Also no fear of death and no #will to exist (as Schopenhauer probably would have expressed it) But - and that is a surprising outcome to me - it seems to be a prove, that Alan #Turing was wrong , when he annouced his famous test for "true AI". And it seems to affirm John Searle and his famous thought experiment of the #ChineseRoom.

#JeffreyKaplan - The famous #ChineseRoom #ThoughtExperiment - #JohnSearle (1980)

https://www.youtube.com/watch?v=tBE06SdgzwM&ab_channel=JeffreyKaplan

#Philosophy #PhilosophyOfMind #TheoryOfMind #TheoriesOfMind #Mind #Consciousness #Computing #Symbol #Symbols #StrongAI #ArtificialIntelligence #AI #Dualism #Behaviourism #Functionalism #IdentityTheory #Syntax #Semantics

A few years ago in an IRC channel I frequent visited, someone programed a bot that uses Markov chain to produce a random sentence constructed from previous logged conversations. Everyone thought it was hilarious chatbot, until one day it said...

"I know nothing, I can only process files."

#ChineseRoom #P-Zombie