The post's recommendations and analogies kind of go against two shortcut approaches that have helped a lot of people in the pre-AI real world:
1) perfect is the enemy of good
2) fake it till you make it
The analogies imagine difficult scenarios where the habit of taking shortcuts doesn't help. But most people most of the time don't run into those scenarios at all.
I find the coordination between nations suspicious.
But what you said - "It's also because social media is part of the USA's soft power projection, and many of us now consider this to be a threat." - strikes me as the most plausible driver behind it, given how chummy Trump and the techbros have become.
I agree with your other observations about SM. But they've all been true from many years. That's why this sudden urge by culturally diverse societies to act now feels suspicious, to me at least.
> I find the coordination between nations suspicious.
You shouldn't. I mean, they talk to each other continuously. Them coordinating things is normal. The EU nations will be doing even more coordination, because the EU is a body for the coordination of those nations.
> That's why this sudden urge by culturally diverse societies to act now feels suspicious, to me at least.
We're not all that diverse, really. Ironically, social media may have brought us all together against social media. And it's not really all that sudden, this has been building for many years now.
Similar things due to Trump trying to bully everyone, but specifically NATO, the EU, and the Americas (and all the international stuff DOGE cut) will have a lot more stuff like this, some of which will be coordinated, some of which will be everyone spontaneously making similar decisions. That too will take years… well, unless Trump actually picks a kinetic fight with a NATO country, then political years pass in a few weeks.
Well, I happen to use it everyday. I honestly don't know what exactly is "terrible/horrible/awful" about it. I'm neutral about its UX - neither memorable nor despicable.
It may be missed if the new app's UX turns out to be worse on whatever metrics you're using.
My understanding is that the synthetic training data helps capture abstract time-series patterns that are common in all domains.
As they say in appendix 8:
> We create the synthetic data to reflect common time-series patterns using traditional statistical models. We start with four simple times series patterns:
> • Piece-wise linear trends (I), where the number of the piece-wise linear components is randomly chosen between 2 and 8.
> • ARMA(p, q) (II), where 1 ≤ p, q ≤ 8 and the corresponding coefficients are generated from either a multivariate Gaussian or a uniform, then normalized.
> • Seasonal patterns. In particular we create the sine (III) and the cosine (IV) waves of different random periods between 4 and max context length / 2 time-points and time delays.
If there were no such underlying patterns in the class of all time-series data, then even the idea of traditional time-series models would be fundamentally misplaced.
And since this is a transformer model, it also looks for patterns in the problem-specific input data at inference time, just like how the input context to an LLM influences its output's relevance.
I thought the title meant the training data used was ethics content and ethical reasoning. Turns out "ethically trained" means the training data used doesn't violate copyright laws.
Given the extent to which the copyright system has benefited corporations and publishing companies to the detriment of individual authors and the general public, I'm constantly surprised that it still has many apologists.
As we don't live in a world where the rich patronize the arts some sort of copyright system is the only way authors and artists are gonna make a living doing their thing. ...though I suppose proponents of Universal Basic Income (UBI) would disagree, but between the abolishment of copyright, the institution of UBI, or a 7 year old child being hit by 7 lightning strikes and 7 meteor impacts and surviving; the latter seems the most likely.
People imagine poor author having their thing stolen rather than poor author that corporate takes IP from by contract agreement (and if you don't do that, you don't get the job), then abuses for 70+ years
It would be interesting to talk with a victorian-era chatbot, including victorian-era ethics. would be interesting to see how much divergence from modern era ethics it would have.
I believe the works are no longer under copyright. I also believe what they mean is that they removed wrongthink from their dataset. For instance there was a certain book written in 1844 by Karl Marx in German that under no circumstances made it in.
This ofc means that the LLM is completely pointless.
If training data of any kind violated copyright, every creator alive would be in breach of by virtue of any influence their “training data” (lifelong exposure to the work of others) has on their output.
The creators crying foul of AI are painting themselves into a corner, both literally and figuratively.
This is a truly awful argument that keeps coming up. It relies on the false equivalence between training an AI (a technical process that involves copying a work into computer storage), and a human being experiencing a work, which doesn't involve any kind of copying (and usually involves the human legally purchasing the work, which AI companies did not do).
There is a legal difference as well as a technical difference. AIs don't learn the same way human brains do. The law does not treat these things the same. You may want to draw an analogy between the two and say they're "basically the same", but they are not basically the same. They aren't the same at all, outside of a very weak analogy. Is training kind of sort of like human learning? Yes. That doesn't mean anything. Dogs are kind of sort of like children, but if you try to treat your child the way you treat your dog, you end up in prison. Because children aren't dogs, either in reality, or in the eyes of the legal system.
Please, AI boosters, stop using this one. Human brains aren't clocks. Human brains aren't computers. Human brains aren't LLMs. AI training does not mimic human learning in any significant way.
> these tools until AI had the common property of being enhancing of human cognition, because they couldn't do the thinking for you
I have a different take, centered around this idea: Not everyone was into thinking about everything all the time even before AI. I'd say most people most of the time outsourced actual thinking to someone else.
1) Reading non-fiction books:
Not all books, even the non-fiction ones, necessarily require any thinking by the reader. A book that narrates history, for example, requires much less thinking than something like "The Road to Reality" or "Godel Escher Bach."
Most of us outsourced the thinking and historical method to the authors of the history book and just passively consumed some facts or factoids. Some of us memorize and remember these factoids well, but that's not thinking, just knowledge storage.
Philosophically, what's the difference between consuming books this way and reading an LLM's output?
2) Reading research papers:
Most people don't read any research papers at all. No thinking there.
Most people don't head to some forum to ask about latest research either.
Also, researchers in most fields don't come out and do outreach regularly.
Indeed, an LLM may actually be the only pathway for a lot of people to get at least _some_ knowledge and awareness about latest research.
Those of us in scientific, engineering, humanities, healthcare fields may read some to many papers.
But only a small subset reads very critically, looking for data errors, inconsistencies, etc.
For most of us, the knowledge and techniques may be beyond our current understanding and possibly without any interest in understanding them in future either.
Most of us are just interested in the observations or conclusions or applications. Those may involve some thinking but also may not involve any thinking, just blind acceptance of the paper's claims and possible applications.
3) Coding:
Again, deep thinking is only done by a small set of programmers. Like the ones who write kernels, compilers, distributed algorithms, complex libraries.
But most are just passive consumers who read some examples online or ask stackoverflow or reddit for direct answers.
Some even outsource all their coding entirely to gig sites. Not much thinking there except pricing and scheduling.
What's the difference between that and asking an LLM or copying an LLM's answers? At least, the LLMs patiently explain their code, unlike salty SO users!
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IMO, most people weren't doing much thinking even pre-AI.
Post-AI, it's true that some people who did do some thinking may reduce it.
But it's equally true that those people who weren't doing much thinking due to access or language barriers can actually start doing some thinking now with the help of AI.
> I'd say most people most of the time outsourced actual thinking to someone else.
Someone else being human, until now. That may change. That's the whole point!
But I concur with your general point on the upstream production of thinking and knowledge. Indeed, such elite thinkers are those in economic history referred to as the "upper-tail human capital". Terence Tao being one of them giving license to the kind of thinking that accepts AI as a simple tool that is not fundamentally breaking our relationship with technology is what exactly I am protesting.
> But it's equally true that those people who weren't doing much thinking due to access or language barriers can actually start doing some thinking now with the help of AI.
If only we keep thinking that thinking is a comparative advantage of our species, I suppose!
Your comment mistakenly assumes this is the only campaign around. But this is just one among many initiatives and websites. There are campaigns against other US big tech companies on this site itself:
To me, the assumptions in your comment about them and their views seem much more like stories of your own creation, likely without any empirical testing of the reality around you.
Couldn't this antichrist stuff be his sane/rational strategy to manipulate the powerful but religious rightwing people under his sway? Is there evidence to assume he himself is on the verge of some kind of psychosis and not fully in control of his faculties?
I'm not sure battling the Vatican over interpretations of an obscure philosopher who mentored him back when he was an undergrad is the easiest way of winning over the religious right. Most of whom will happily go along with generic arguments about Peter Thiel's portfolio being essential to defeat Communist China and the woke libs. Treating Eliezer Yudkowsky as an irrelevant nutter probably works better on people with all kinds of views on religion and politics than attempting to elevate him to the status of antichrist
OpenAI is ethically the type of company that will censor. But this single example doesn't outright support a hypothesis of malice.
There may be non-malicious reasons as well.
If the interface you're using does not include search engine results, the answer seems reasonable and non-malicious.
Or if search engines are giving conflicting information or showing only shady sources, the answer seems reasonable.
Maybe try to find some pattern in answers to diverse questions about US elections and parties.
1) perfect is the enemy of good
2) fake it till you make it
The analogies imagine difficult scenarios where the habit of taking shortcuts doesn't help. But most people most of the time don't run into those scenarios at all.
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