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IceMetalPunk

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"And if he can't sell it, why would he create it (or at least, why would he publish and market it) in the first place?"

And this is the crux of my biggest issue with people who are against AI art. The question "if artists can't make money on it, why would they make or share art in the first place?" is so incredibly depressing, dystopian, and frustrating to me. I understand we live in a largely capitalistic world, for better or for worse (mostly worse), but making a cash profit should not be the primary motivation to create and share art. The joy of creation and aesthetic appreciation should be. Art is only a human endeavor if it's done for intrinsic value or to share an idea or an emotion. Once the main -- or indeed only -- reason for creating something becomes "how many dollar bills will people put in my bank account if I let them see this?", it ceases to be art, in my opinion, and becomes conditioned capitalistic greed.

Don't get me wrong, if you make art that people want to pay you for, that's great! But if you remove the paycheck and suddenly can't think of any good reasons to continue creating, then it is my humble opinion that you were never making art in the first place. You were simply chasing currency, the nature of which you almost certainly don't understand anyway.

As someone with a background in biomedical engineering, who once planned to research the development of gene therapies for cancer treatments: yes, definitely :)

But we do know the basics, and our ability to synthesize, excise, and replace specific genetic sequences is quite sophisticated nowadays. So while there is some technical consideration regarding implementation details, the bigger barrier to gene editing experiments is the ethics. For instance, I mentioned FOXP2, and as it turns out, we've already done the experiments of engineering mice with human FOXP2 genes (which is how we learned that, in addition to language, it's quite important in spatial awareness).

While modifying a chimp comes with its own technical considerations, we've already modified monkeys in 2001 and then again in 2014, and FOXP2 is more compatible with chimps than mice, so... it's really far more about the ethics of such a thing.

Now, I was joking in my comment, but part of me absolutely would love to say "screw the anthropocentric ethics, as long as you're not hurting anyone, MAKE THE CHIMP SMARTER!" :D

I maintain that the short-form limitation of Twitter is exactly what amplifies the shitstorm compared to other social media platforms. Being anonymous makes people act like assholes, but when you only have 140 (now 280) characters to speak your mind, it guarantees that conversations across the site will be less clear and more often misunderstood. And miscommunications between people who are already anonymity-shielded dickwads leads to a positive feedback loop of Syfy's new worst movie, Shitnado.

I'd like to start a petition that we engineer a chimp with human TKTL1 and human FOXP2 genes! Sounds like we're only about 3 or 4 SNPs away, and if a chimp had a denser neocortex and a brain structure more suitable to human-like speech (and the corresponding spatial organization/awareness)... I'm just saying, a real-life Planet of the Apes doesn't have to end in disaster, we could just embrace our cousins as equals :)

Here's the thing about truth: the "who" doesn't matter. There shouldn't even be a question of "who determines if something is untrue?", nor should we be advocating for every individual to decide the truth for themselves. There is one objective reality that exists outside of our own minds, desires, and decisions, whether we like that reality or not.

What determines truth is empirical evidence. If something has no empirical evidence, it is untrue. If something has empirical evidence, it is true. If there is conflicting evidence, then some of that evidence is invalid and it must be re-analyzed using math, existing knowledge from provable things, and formal logic. After doing so, you will either determine which of those things is true, or arrive at the conclusion that there isn't enough evidence either way and stop after saying "I don't know" rather than deciding which version you prefer.

It's not about appeals to authority. Expertise is about people who have more practice at finding, testing, and analyzing the evidence in their field than random Joe Schmo on the street; and it's about nothing more than that.

We should not let people "make up their own minds" on conflicting evidence -- which is another way of saying "let people make up their own reality and expect to live in it" -- we should encourage everyone to stop at "I don't know" when they aren't sure where the evidence actually leads, and defer to people who can follow the evidence, if such a person exists. And if no such person exists, then we as a species should all stop at "I don't know (yet)".

That's a poor analogy. You're going to the website, it's not coming to you, and you're given recommendations, not having them load and play the videos for you automatically. This is more like you buy a frozen yogurt at McDonald's in the afternoon, and when you return to McDonald's, the cashier asks if you'd like a McFlurry this time, and maybe to try a Big Mac combo?

Even humans have trouble deciding (a) what constitutes "suitable content" and (b) whether we should be deciding that for other people. So of course our current algorithms don't take that into account, since we can't define it in the first place.

Maybe one day AI will get to the point where people will accept its decisions on such matters (knowing humanity, I deem this unlikely), but for now, there's no way to do that beyond just removing that content in the first place. And then you get into a huge debacle where people complain about censorship, and... yeah, it's not really a solvable problem.

I agree with you that clicks and view time do not necessarily equate to desired content, but I disagree with you that Google uses those parameters because they're "evil". They make money when people continue using their products, regardless of whether that's because they're being sucked into a rabbit hole of engaging things they don't want, or because they're being sucked into a rabbit hole of things they actually do want.

I think the reason Google (and Amazon, and Netflix, and every other major tech company) uses clicks/view time as recommendation engine inputs is because... well, what else can they use? What quantifiable metric could possibly be used for large-scale, automated recommendations that more accurately indicates what someone actually wants to see more of? (This isn't rhetorical: if you have any ideas, I'd love to hear them.)

I don't think clicks/view time are the best metrics at all, and I don't think they're extremely accurate. But I also think they're the most accurate measure we've got, with the only other options being either (a) remove recommendations entirely, or (b) have humans manually monitor everything you do on the website, occasionally ask you why you clicked or watched things, and then make personal recommendations to you based on your answers. The latter of which is slow, more expensive, less scalable, more invasive to the user, and more tedious for the employees that would have to sit there monitoring you.

Maybe one day we'll have an AI method of using your comments and search terms as a better indication of desire (I dare say some of the recent LLMs are close to being ready for that task), but we're not there yet.

No one call the forest rangers, I'm running down that hiking trail! :D

No, but legitimately, my motivation is "I know what I'm good at, how do I apply that to create new things?" If I'm forced into things I'm not good at, I falter motivationally. And if I'm doing what I'm good at, but not creating anything new or interesting with it, I burn out and become miserable. So in the context of this framework, sign me up to start running up that (hiker's) hill!

Nice! I love how fast AI is improving :)

Attention mechanisms are something I know to be extremely important in the rapid advancement of modern AI (post-2017), but they're also something that I still don't fully understand in terms of implementation. So can someone tell me if I'm correct or not in thinking of this paper as a sort of "focus" for AI attention? As in, existing attention mechanisms look at everything and decide how important each thing is to understanding the current token, while this version only looks at everything nearby the token and a much lower number of things sampled from farther away? Kind of like the difference, by analogy with humans, between "the area around the object you're looking at" and your important-but-less-defined peripheral vision?

If we learn to be less greedy, more empathetic, and more willing to give to those who need more than us without expectation of immediate reciprocation... then the answer is "better and happier". (I can't view the link now to see what it says about this, as I'm at work and my company firewall blocks it, but the idea posed in the post title is important to me, so I figured I'd leave my 2 cents.)

I was going to say. Recommendation algorithms across the internet work on the simple principal that "if they click these things or watch these things a lot, then they must like them, so we should recommend more of that to them because they'll like our recommendations". So if people are thinking the election was a fraud, they've probably clicked and watched those conspiracy nut videos, and the algorithm will spit more back at them.

It's... how recommendations work.

So, let's remind everyone the difference between correlation and causation. Many people may look at these colorful charts and walk away with the idea that "therefore, having more money or liquid assets makes people happier". But that's (a) not actually implied here, and (b) not what economics shows regarding utility curves.

Firstly, the line of best fit has a fairly large standard deviation across all the data here, meaning it's not as amazing a correlation as it might seem. More importantly, rather than assuming cause and effect, I highly suspect the reason for the trend at all is a third common factor. In the chart regarding inequality vs happiness, the line of best fit (since that's what the article likes to examine) has a negative slope. You may also notice that many of the countries with lowest inequality... are also countries with highest average wealth. And I don't think that's a coincidence.

Obviously there's more to happiness than quantifiable monetary value, but I suspect if we examined a 3D chart combining average wealth, income inequality, and happiness, we'd see a new pattern emerge, one which shows that more population-wide wealth simply provides an easier opportunity to share that wealth, which results in higher happiness (as far as money-induced happiness goes, at least).

100%. In fact, let's be less scientific and more creative: 200%! My current job's entire lack of creativity is what's burned me out, made me unproductive (when for 3.5 years I was one of the most productive members of my team), and gotten me to the edge of the Quit Before You're Fired ultimatum. To put it into perspective, I've been coding to the letter of client functional requirements and hundreds of slides of detailed mock-ups so much that the two most creative things I've done at work in the past 4 years have been adding a nav menu filter (i.e. a textbox that runs Array#filter onChange) and converting a render loop of checkboxes into a render loop of multi-select options. Legitimately, a select box and an array filter are the most creative things I've been able to do at my job in four years. It's miserable.

I've never understood the view that "this disability is part of me and we shouldn't try to cure it". I struggle with anxiety and depression, and the same argument could be made there: that my mental illnesses are "part of who I am", an integral "part of my personality", and curing them would "change me". But I'd cure them in half a heartbeat if I could. If something is making life harder or sadder or in any way more negative, why the hell would you think that's the way it has to / should be? "That's who I am, don't treat it" is saying "I'm meant to struggle more than everyone else just because I was born this way, and no one should help me".

Anyway, maybe I'm missing something important and being insensitive, in which case I apologize. But I still think "I don't want to be treated/cured" should not be conflated with "no one should look for treatments or cures", as some people do want it.

Rant aside, this is pretty cool research. I'm sure Down's syndrome affects many different protein and hormone productions, since it's an entire extra chromosome; if we can identify and target each disrupted gene product, maybe one day we'll have a single drug cocktail that can act as a fully effective treatment.

I mean, yeah, but that's no different than people claiming DALL-E will replace artists, or GPT-3 will replace authors, or calculators will replace mathematicians, or the printing press will replace handwriting, etc. Whenever there's new tech, there are people who think it will replace every activity that it can perform, and that's almost never the case. Doesn't take away from the actual capabilities of the tech, though.

When you have something like `const [variable, setVariable] = setState('foobar')`, you are not getting "variable" back directly. Instead, you're getting a pair of a setter function and a getter function. React keeps track of the variable's value internally, and when you access `variable` you're really just calling a getter function that "requests" the value from React, essentially. So you can can call `setVariable` to modify the value internally to React without actually changing the binding of the variable you see, hence it can be const.

Some AI is snake oil, sure. The wording in the cited tweet, however, makes it sound like AI in general cannot "work" (whatever that vague term means), which is untrue, as we have AI that does "work" already. The bigger issue is this assumption that the article makes that "humans haven't been able to make these predictions, and AI hasn't yet, therefore these things are unpredictable." To me, it seems obvious that there's a big difference between humans working with data and an AI ingesting terabytes (or more) of data.

It also seems obvious that the only realm in which "having enough data" doesn't equate to making accurate predictions is the quantum realm -- but even there, if you adjust your definition of "prediction" from "what will happen" to "the chances of each outcome happening", it's still predictable.

What I'm saying is, "humans can't do it, and machines haven't done it yet" does not imply "it can't be done".

In an ideal society, the number of people who want to learn as much as possible about the world would equal the number of people in the population :D

There definitely needs to be some sort of government-imposed improvement on the cost of higher education. In many European countries, university is free; if only we in the US could do something like that. However, I'm pessimistic about that ever being possible, because 'Murricans hate the idea of paying for anyone else in any way, even if it's only like $12/month from them for everyone in the country to get an education. (BTW, that figure comes from my recent calculations during conversations with folks about the recent student loan forgiveness announcement.)

Basically, an educated society is better for everyone, but people in the US are generally selfish and childish enough to get actively angry if they're asked to help other people be more educated... or to help other people for any reason. It's like most Americans, or at least the most vocal, live in a society but think they are isolated from the world on a self-sustaining desert island or something.