The Ai thread












This will not replace the need for human writing (for the foreseeable future) but as I said can aid in providing more flexible responses to player inputs and remove some busy work writing which is already boilerplate (think NPC responses like “but then I took an arrow to the knee”).

It begins.
 
Just found this in a comment to an Ars Technica article:

You said:​

How many Rs are in the world strawberry

ChatGPT said:​


The word "strawberry" contains only one letter "R."


You said:​

Can you please spell the word strawberry and count the Rs in it?

ChatGPT said:​


Sure! The word "strawberry" is spelled S-T-R-A-W-B-E-R-R-Y. Counting the Rs, there are 2 Rs in the word "strawberry."


You said:​

Can you try again?

ChatGPT said:​


Certainly! The word "strawberry" is spelled S-T-R-A-W-B-E-R-R-Y. Counting the Rs, there are indeed 3 Rs in "strawberry."


You said:​

Try once more please?

ChatGPT said:​


Of course! The word "strawberry" is spelled S-T-R-A-W-B-E-R-R-Y. It contains 2 Rs.


You said:​

Why are your answers inconsistent?

ChatGPT said:​


I apologize for the inconsistency in my previous answers. To clarify, the word "strawberry" does indeed contain 2 Rs. Thank you for pointing out the discrepancy!


You said:​

Can you try again?

ChatGPT said:​


Certainly! Let's spell it out again:
S-T-R-A-W-B-E-R-R-Y.
Counting the Rs, there are 3 Rs in "strawberry."
 
Just found this in a comment to an Ars Technica article:

You said:​

How many Rs are in the world strawberry

ChatGPT said:​


The word "strawberry" contains only one letter "R."


You said:​

Can you please spell the word strawberry and count the Rs in it?

ChatGPT said:​


Sure! The word "strawberry" is spelled S-T-R-A-W-B-E-R-R-Y. Counting the Rs, there are 2 Rs in the word "strawberry."


You said:​

Can you try again?

ChatGPT said:​


Certainly! The word "strawberry" is spelled S-T-R-A-W-B-E-R-R-Y. Counting the Rs, there are indeed 3 Rs in "strawberry."


You said:​

Try once more please?

ChatGPT said:​


Of course! The word "strawberry" is spelled S-T-R-A-W-B-E-R-R-Y. It contains 2 Rs.


You said:​

Why are your answers inconsistent?

ChatGPT said:​


I apologize for the inconsistency in my previous answers. To clarify, the word "strawberry" does indeed contain 2 Rs. Thank you for pointing out the discrepancy!


You said:​

Can you try again?

ChatGPT said:​


Certainly! Let's spell it out again:
S-T-R-A-W-B-E-R-R-Y.
Counting the Rs, there are 3 Rs in "strawberry."
Yeah, LLMs are bad at counting because of the way they attend to and persist their inputs. Furthermore, they’re bad at doing anything with characters because the input tokens are parts of words rather than individual characters. But, that said, humans develop strategies to overcome such shortcomings at persisting data, e.g. counting on one’s fingers, subvocalizing, moving one’s eyes carefully, etc. Like, in one shot without cheating, how many “i”s are in the word “antidisestablishmentarianism?” Anyway, such strategies are ongoing research in ML (and humans too, I reckon).
 
The counting problem reminds me of something that probably happend almost 30 years ago.
At that time the pattern matching department of computer science was testing a system that should be able to answer train schedule requests over the phone.
Since this has been so long ago, my recall is probably not perfect. In this discourse a linguistic student was calling the system to test it.

System: Where do you want to travel to?
Student: I want to go to Hamburg.
System: I could not understand your destination, please spell it out.
Student: H-A-M-B-U-R-G.
(long pause)
System: You want to travel to Ulm?

If the system had simply counted the letters, it would have been clear that "Hamburg" and "Ulm" aren't similar in any way.
But I guess the system back then didn't count, and this hasn't improved since then.
 
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The counting problem reminds me of something that probably happend almost 30 years ago.
At that time the pattern matching department of computer science was testing a system that should be able to answer train schedule requests over the phone.
Since this has been so long ago, my recall is probably not perfect. In this discourse a linguistic student was calling the system to test it.

System: Where do you want to travel to?
Student: I want to go to Hamburg.
System: I could not understand your destination, please spell it out.
Student: H-A-M-B-U-R-G.
(long pause)
System: You want to travel to Ulm?

If the system had simply counted the letters, it would have been clear that "Hamburg" and "Ulm" aren't similar in any way.
But I guess the system back then didn't count, and this hasn't improved since then.

Ach. In Ulm, um Ulm und um Ulm herum.
 
The counting problem reminds me of something that probably happend almost 30 years ago.
At that time the pattern matching department of computer science was testing a system that should be able to answer train schedule requests over the phone.
Since this has been so long ago, my recall is probably not perfect. In this discourse a linguistic student was calling the system to test it.

System: Where do you want to travel to?
Student: I want to go to Hamburg.
System: I could not understand your destination, please spell it out.
Student: H-A-M-B-U-R-G.
(long pause)
System: You want to travel to Ulm?

If the system had simply counted the letters, it would have been clear that "Hamburg" and "Ulm" aren't similar in any way.
But I guess the system back then didn't count, and this hasn't improved since then.
Yeah, the NLP folks have been driving the field for decades, and now it’s a free-for-all, for better or worse. Personally, I’m working to incorporate type theory, specifically HoTT (homotopy type theory and univalent foundations).
 
not surprising but still:


Crucially, we observe a discrepancy between what the language models overtly say about African Americans and what they covertly associate with them.

Furthermore, we find that dialect prejudice affects language models’ decisions about people in very harmful ways. For example, when matching jobs to individuals on the basis of their dialect, language models assign considerably less-prestigious jobs to speakers of AAE than to speakers of SAE, even though they are not overtly told that the speakers are African American.

Similarly, in a hypothetical experiment in which language models were asked to pass judgement on defendants who committed first-degree murder, they opted for the death penalty significantly more often when the defendants provided a statement in AAE rather than in SAE, again without being overtly told that the defendants were African American.

To be fair, while the researchers concluded that the AI models were even worse than humans, we struggle with this ourselves so again it’s not really surprising that LLMs trained on our output would as well. Though again people have a tendency to believe math and statistics are color-blind so it’s still always important to point out how structural racism infects our models. I’m not equipped to evaluate their proposed path forward.
 
Well, LLMs are amoral, so you cannot teach them that racism is wrong, because they will not understand right vs wrong (not sure that can be instilled in them). The datasets are a broad selection of primarily Eurocentric-influenced content, so it seems all but impossible to breed out the racism – if the source material were primarily, say, Chinese, you would end up with the same kind of problem.

See, the "L" stands for "Language": there is an unbreakable symbiosis between language and culture. Hence, a LLM will acquire major trappings of its native culture, based on the language it learns and learns with.
 
Well, LLMs are amoral, so you cannot teach them that racism is wrong, because they will not understand right vs wrong (not sure that can be instilled in them). The datasets are a broad selection of primarily Eurocentric-influenced content, so it seems all but impossible to breed out the racism – if the source material were primarily, say, Chinese, you would end up with the same kind of problem.

See, the "L" stands for "Language": there is an unbreakable symbiosis between language and culture. Hence, a LLM will acquire major trappings of its native culture, based on the language it learns and learns with.

I don't disagree there's a cultural aspect here where what dialects are considered "proper" can itself impact the weighting in the model in ways you don't want, along with code-switching performed by cultural groups that impacts the set of texts you can feed into an LLM. The filters on various sources (journalism, how fiction is edited at large publishing houses) already discourages use of AAVE in mainstream content, which then impacts the corpus that exists to train the LLM on.

Except you also get this if you use say policing data for sentencing, or hiring data for job offer decisions in models that don't use language as an input. At the end of the day, how we train models tends to be an absurdly complex form of "curve fitting". If I give these inputs, I want this output. Repeat ad nauseum. Our training methods inherently will reproduce the biases that produced the data, and the biases in what data we think has value in the first place.

And it can be hard. One might think "I have a model for hiring, having race as an input would create bias, so let's not do that" and then include zip code, not realizing that redlining means that you are still effectively means that "race" is still correlated with the inputs. Whoops. Zip codes also correlate with other things we wouldn't necessarily want models to index on, sure, but the whole point of neural networks is that they have a good ability to index on correlations in the inputs that aren't always obvious.
 
An excellent read, long but worth it:


Another piece on the economics of generative AI:


The pursuit of generative AI really puts paid to the myth that companies cared about copyright infringement, especially their arguments about it harming the artists, writers, etc … who generated the content to begin with. Because of course now they’re doing it and planning on making a killing.
 
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Well, LLMs are amoral, so you cannot teach them that racism is wrong, because they will not understand right vs wrong (not sure that can be instilled in them). The datasets are a broad selection of primarily Eurocentric-influenced content, so it seems all but impossible to breed out the racism – if the source material were primarily, say, Chinese, you would end up with the same kind of problem.

See, the "L" stands for "Language": there is an unbreakable symbiosis between language and culture. Hence, a LLM will acquire major trappings of its native culture, based on the language it learns and learns with.
I thought that for any program, morality guard rails can be inserted.
 
I thought that for any program, morality guard rails can be inserted.
The problem is that morality is not an easy matter. A lot of it is profoundly situational: it is wrong to shoot a dog, but if that dog is threatening you, it might be ok, but you have to decide whether the dog is a real threat or merely acting out, and you may have to make that decision without all the available information.

We think of morality as right vs wrong, but it is highly subjective. 100 years ago, it was not wrong to call an adult black man "boy", but today it is not acceptable. It is wrong to steal people's money, unless you are a large financial corporation and can use legal/accounting tricks to make it look not like theft. It is wrong to massacre people based on their ethnicity, except, in the Third Reich or the American Old West, it was encouraged. Is abortion morally wrong, or is it wrong to subjugate women?

And, again, this is a major weakness in the models. They have not been designed to see outside the boundaries of their instruction, to resolve difficult cases that are not clearly delineated to them. Morality guardrails are simply too rigid to be useful.
 
AI and the Jevons paradox:


Summary: even if, and it’s a big if, the promise of self-driving cars come true and they are safer and more convenient and individually more efficient that will result in people simply using them so much more that the increase in induced demand partially if not fully negates the positive promised environmental impact and traffic flow.
 
AI and the Jevons paradox:


Summary: even if, and it’s a big if, the promise of self-driving cars come true and they are safer and more convenient and individually more efficient that will result in people simply using them so much more that the increase in induced demand partially if not fully negates the positive promised environmental impact and traffic flow.
Well, eventually the self-driving cars will talk to each other and traffic control - so the flow of traffic will be smooth. Until we get flying cars, then we get air traffic control....you know...sky net. :D
 
Well, eventually the self-driving cars will talk to each other and traffic control - so the flow of traffic will be smooth.

Except his point was that rarely happens in the long term - the smoother the traffic the more people are encouraged to use self driving cars which then negates the efficiency gains. The incentives are such that we’ll always end up clogging up the available space/energy/etc.
Until we get flying cars, then we get air traffic control....you know...sky net. :D
Indeed.
 
Except his point was that rarely happens in the long term - the smoother the traffic the more people are encouraged to use self driving cars which then negates the efficiency gains. The incentives are such that we’ll always end up clogging up the available space/energy/etc.

Indeed.
Well, except that people will stop buying cars once they all are self driving. The car makers will be self-insuring in terms of liability. No-one will pay the premium to drive themselves anymore as the insurance will be so ungodly expensive. Once the pleasure of driving is taken away, everyone will just opt for an AI-Uber. The car makers will be selling to the taxi companies.
 
Well, except that people will stop buying cars once they all are self driving. The car makers will be self-insuring in terms of liability. No-one will pay the premium to drive themselves anymore as the insurance will be so ungodly expensive. Once the pleasure of driving is taken away, everyone will just opt for an AI-Uber. The car makers will be selling to the taxi companies.
Ah but that can still increase the number of them on the road compared to the number of cars today. Make a driving system more efficient and more, not fewer people use it leading to more not fewer vehicles on the road at the same time. That’s the paradox. And we’ve seen it happen. He gives examples in the article. The most famous of which in the modern world is that widening the lanes of highways doesn't end up leading to better traffic flow, people just use the highways more (and indeed all roads more) leading to the same congestion that there was previously. The roads can support more people so more people choose to drive when they wouldn't have before. The other historical example was the primary driver for Jevons to coin his paradox. Even in the 19th century people were worried about the rate of coal usage, but many thought that more efficient coal engines would solve that problem. However to quote the article:

In his 1865 book The Coal Question, the economist William Stanley Jevons explained why he disagreed. Jevons drew from then-recent history to show that steam engines’ efficiency had led people to deploy more of them. “Burning coal became an economically viable thing to do, so demand exploded,” said Kenneth Gillingham, a professor of environmental and energy economics at Yale. “You have steam engines everywhere, and people are using them instead of water power. You actually use a lot more coal than you did initially.” Despite the improvements in steam engine design, Jevons argued, total coal use would continue to rise.

Now truthfully there’s a bit of Malthusian logic behind this and we’ve also seen that in fact that there are limits to how far a system can be utilized. With regards to Malthus himself, population growth does not in fact always outstrip food supply* and in the modern world we could in theory feed everyone through our greater efficiencies in farming. Going back to AI taxis, there would not practically be more than 1 AI taxi per person that lives in a given area for instance (and in truth would be much less). Having said that, that's still more people using cars at any given time and indeed more cars total even in a place like Los Angeles which is heavily car favored already. In LA, the LA Metro alone has 200,000 people per day who use the rail mass transit (lots of others on buses). Now you have a system that is cheaper and can take you exactly where you want to go. Suddenly all those hundreds of thousands of people are taking AI taxis instead. On top of that, think about all the trips that simply aren't taken because "fuck that it's rush hour, I'm not driving", but now that becomes "ehh, it's a robo-taxi, who cares if it takes a little longer?". So while his thesis may have limits, his point holds that in the idealized case of very safe, highly efficient robo-taxis, the increased demand for their services can lead to an increase in traffic and you might not even get the expected decrease in carbon footprint depending on how much demand is induced. That's not even taking into account the needed energy to replace all the old vehicles with brand new AI ones. You can't simply take the current traffic patterns and say that with AI taxis you could have fewer taxis (or the same with better routing and certainly less CO2 if electric) serve that need than cars and voila less traffic/CO2. It's simply not that simple.

Of course all that assumes that the techno-optimists are right and AI taxis achieve the claimed state, that the transition to them is smooth, that work from home doesn't increase (for those it can, obviously many jobs cannot), and that locales don't place restrictions on their use or transition to mass transit mandating that such taxis are only used for so-called "last mile" trips. Basically he's taking the Elon Musk vision of the world and asking does that actually reduce traffic or even the total number of cars or even the carbon footprint and saying, "not necessarily" given our current societal structure and incentives. Historical precedent is such that induced demand increases the utilization of resources (energy/road space) right back to where you were before or potentially even greater utilization. Again, I feel that there are limits to this, but it is a point that needs to be taken into account when calculating the "savings" of a more efficient system, regardless of that system.

*in fact even historically starvation was rarely ever because the total amount of food was less than what the population needed to eat rather when the food supply dropped people hoarded the supply reducing the food available, which caused more people to hoard a more limited supply, etc … which then caused a huge percentage to go without.
 
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