1. Find some nicher but interesting topic (e.g. some historical event like Lepanto's battle)
2. Have AI generate the content of the 20 minutes video by collecting information about it online
3. Have AI generate the video
4. Have AI generate a realistic voice to comment on the video
5. Upload it without mentioning it's all AI generated
6. Have me get mad 4 minutes into the video because footage/paintings referring to that battle...do not exist at all...slowly realize it was all AI generated
The YouTube algorithm got unbearable to me even before the mudslide of AI content.
I highly recommend using an extension like Unhook and disabling all algorithmic recommendations such as the Home feed, sidebar/endscreen recommendations etc. The only way I interface with YouTube now is through the subscriptions page which shows me videos from creators I follow in chronological order.
I started "do not recommending" and removing channels that had any stink of AI. I also removed some subscriptions from major YouTube channels. My subscription and homepages are much quieter now and easier to parse.
This is good information, but a bit superficial - before AI, what percentage of online articles were generated from templates? What was written by content generation farms? Fiverrr and co pay-per-word writers?
I suspect that market has been more affected than anything.
I imagine something like 98% of articles also get less than 100 views. So the question is more about the articles you're reading rather than articles in general.
If one cant remember what they generated, whats the point in generating? Half of those who write articles do not remember what the AI put in it... Reviewing has become a slop work by humans!
> We build on our prior research by using three different AI detectors (Pangram, GPTZero, Copyleaks). We independently evaluate each to show that the false positive rates and average false negative rates are consistently below 2%. Each AI detector shows a similar trend.
This is all bullshit, none of those actually work, and the false-positives rates are sky-high. I'm not sure how any serious person have tried out any of those services and came away with the impression of "Well, better than nothing" because literally, it seems the opposite.
The detectors aren’t great but they aren’t really the issue. The fact that LLMs make it so easy to impersonate human communication is precisely the problem here. There cannot be a reliable way to identify if something is from a human or not. And the ease of access and low price makes using LLM generated content a no brainer, you have to actively go out of your way to produce human generated content.
We are building a future where human contact will be scarce
> We are building a future where human contact will be scarce
Yes, until you remember there is a world outside of the screen, where people build things with their hands, use their physically to play instruments for others, paint beautiful things for others to see physically and so much more.
"Humanness" online been dead for decades already, if you want humanness you need to step outside, or at least invite other humans home.
There is a meaningful difference between “humans online are tribalistic” and “content consumed by humans is generated by machines”. The IRL world isn’t safe either, books, newspapers, advertising, speeches are/will be heavily LLM made. Political parties are using LLMs. The IRL humans are relying on what their LLMs summarized or searched for them.
The same way the online world has never actually been that distinct from the offline world, one is merged with the other and they influence each others.
There has been of humanness online of you do not look for it on social medias. But that’s now breaking down, because we developed a technology designed to impersonate human communication
Right, what I was talking about things that generally aren't done by AI. People aren't building sculptures with AI, no graffiti is made with AI, the oil paintings you can see in galleries aren't AI, the DJ that fucks up during a performance isn't AI.
There is so much humanity in the world outside of the screen, and it's really easy to see what is authentically made, ignore the rest. Find live events with real other humans, there are a ton of them out there, doesn't really matter how people find the events, as long as we put our bodies in the same physical space.
I hope you’re right. Over the past month or so I personally started to feel really pessimistic about AI development. I really don’t know how much of those human spaces are safe from AI.
Yes you can go to a drawing course or music festival and see human performances. But how do you then stay in contact with those people? The answer is very likely via software, meaning there is still this question of “am I interacting with a human? Or are they copy-pasting from ChatGPT?”. A friend you met shares a new song, is it really them playing or did they generate that track?
Just the fact that we have some level of doubt means we already lost something.
That being said, sure, live in the physical world and build social contacts. I’m all for it.
Currently the way I detect AI when I read something is that I start to notice a fuzzy confused behind my eyeballs, or The words and concepts seem unusually disconnected and meaningless in a hard-to-describe way. Then I squint a little harder and realize that I was got tricked into reading slop. Weirdly, I also notice this quality in code, even really good AI code that apparently works.
Humans writing more like LLMs, just like new LLMs write more like humans, it's all coalescing into one.
I've copied-pasted comments I made on HN from like 2020 and had it tell me it's "100% AI". I've seen examples where the services claim "100% AI" because there was no normal dashes, only em-dashes. Even have a recent example from HN itself: https://news.ycombinator.com/item?id=48165690
Your evidence seems very anecdata. The graphite.io study does make an effort to quantify the false positive and false negative rates of the three detectors, rather than just saying “they work”. They generate 2000 ai articles and ask the detectors to evaluate them, measuring the false negatives (articles falsely IDd as human written); and they use a separate pre-AI dataset (years 2000-2022) to determine false positives.
Yeah, I suppose it is, I haven't finished my dissertation on it yet, I'll get right on that :)
Throughout them being available I've tried them every now and then, both with AI generated trash and my own pre-LLM writings, and had about 0% success in getting them to accurately report what it actually is. Maybe my writing style and what specific LLM you use matters a lot, I'm sure these platform's training data is mostly from the mainstream models so as soon as you use anything else, they'll get trivially lost. But again, I don't have any evidence and proof behind this, based only on when I've tried to evaluate them myself in the past.
If you need an AI detector to figure out if something is AI or not, surely that means the AI is so good that there is no need to detect whether it is AI or not, because it is indistinguishable from writings by a human when read by humans?
I mean this is an article coming from an SEO company that's really just trying to advertise its services in the end. Their methodology seems very loose.
Just a boring old organic human tired of other organic beings falling for obvious bullshit most likely made up by machines convincing humans with something like "you really have a neat idea here, the world will appreciate you making this into a product".
The Gnome desktop that shipped with Solaris over two decades ago is just as useful, possibly more useful, as the tablet-oriented hamburger menu UI of today.
I do. It's great that the UI is stagnated, but unfortunately the UX is too. Things like bluetooth not being integrated with the DE, and various details that we take for granted not working correctly
GTK is still alive. It seems like Cosmic desktop with GTK apps will be a reasonable path forward. Of course there's KDE and QT, but I mean as an alternative to those.
Accutrons and tuning fork watches are amazing. They have an incredibly unique sound/hum due to the tuning fork oscillating at 360 hz and the most smooth glide you'll ever see in a watch. Recommend a ESA 9162 or ESA 9164 over a pure Accutron for beginners though, a bit more resilient and far more affordable, though they don't have the exposed dial.
I believe this is why all modern digital watches use a 32768.0Hz crystal resonator, it's a power-of-2 frequency above the 20Khz top end of the range of human audio perception, to avoid the whole 'tinnitus on your wrist' thing.
I have an Accutron 214 and I swear it sounds higher pitched than 360Hz (sounds to my ear higher than A440, which I'm very familiar with). Maybe I'm hearing an overtone?
While recycling is last in that mantra, it is overemphasized more than the other two. It shifts the onus of stewarding our environment to the individual rather than the corporations and militaries, which wreck our planet more than any individual can. They'd rather you not look at what they're doing to the environment, and instead look at the individual.
Moreover, companies don't want you to reduce your consumption, they want you to keep buying their products. Reuse? Nah, here are products that are obsolete, buy the new model.
Repair! We should fight for that. I want to be able to repair not just my electronics (or pay someone to do it for me), but also my tools and machines.
Anything that doesn't imply infinite growth is taboo... Which is weird because it will for sure happen, the question is whether you plan it or suffer from it
Yes. I don't think a broadcaster would accept a billion dollars for a 30 second "ad" during the Super Bowl with a message that said "buying this junk will not make you happy".
Making products that are hard to repair and which don't last long are the huge culprits. Also, when it comes to clothing, it is all fast fashion. Wear a few times, then dump.
Also labor costs to repair in the developed world is another factor.
For what it's worth, the mantra I was taught in the U.S. in the 1990s was ordered "recycle, reduce, reuse," but there was no indication that the ordering mattered. We were just taught about all three things.
> It seems you have to decide, do you want to learn Moroccan Arabic, Egyptian Arabic, Levantine Arabic, Iraqi Arabic, Quranic Arabic, Modern Arabic...
Why do you want to learn Arabic? Answering this question will point you to which dialect to learn..
Do you want to read Arabic? Learn Modern Standard. Do you want to watch television and movies? Learn Egyptian. Do you want to study Islam and the Quran? Learn Quranic. Do you have a particular interest in a region and speaking with the people of that region? Learn the dialect that is predominately spoken there.
> The other group (increasingly large) just wants to `git push` and be done with it, and they're willing to spend a lot of (usually their employer's) money to have that experience. They don't want to have to understand DNS, linux, or anything else beyond whatever framework they are using.
lol, even understanding git is hard for them. Increasingly, software engineers don't want to learn their craft.
I think the root of it is most people coming into the software engineering industry just want a good paying job. They don’t have any real interest in computers or networks or anything else. Whatever keeps the direct deposits coming is what they’ll do. And in their defense, the web dev industry is so large in breadth and depth and the pay/benefits are so generous it’s an attractive career path no matter what your passion is.
The ChatGPT, LLMs, generative AI, and other hyped usecases have been the driving force for Nvidia: it injected huge sums of money into their R&D, which also stimulated the economy as developers ran to build build build in order to keep up with the demand for datacenters, which in turn required more infrastructure building to satiate the thirst and power needs of datacenters, etc. Before, ChatGPT, I recall the hype was blockchain, crypto, and NFTs; and maybe before that, it was "big data."
As the LLM, generative AI, etc. bubble begins to deflate due to investors and companies finding it hard to make profits from those AI usecases, Nvidia needs to pivot. This article indicates that Nvidia is hedging on robotics as the next driving force that will continue to sustain the massive interest in their products. Personally, I don't see how robotics can maintain that same driving force for their products, and investors will find it hard to squeeze profit out of it, and they'll be back to searching for another hype. It's like Nvidia is trying to create a market to justify their products and continued development, similar to what Meta has tried, to spectacular failure, with the Metaverse for their virtual products.
After the frenzy that sustained these compute products transitioned from big data, to crypto, and now, to AI, I'm curious what the next jump will be; I don't think the "physical AI" space of robotics can sustain Nvidia in the way that they're hoping.
The part that is hard for me to parse is there is hype but there is also a significant amount of value being extracted by using LLMs and other products coming from this new wave. Everytime I read opinions like yours it’s hard to make sense of it because there is value in the tooling that exists. It cannot be applied to everything and anything but it does exist.
Comparing AI to crypto doesn't really work due to the utility of AI. If you believe that there haven't been meaningful use cases from the recent generative AI surge, then you might be out of touch.
On the investment side, it's hard to say that since ROIC is still generally up and to the right. As long as that continues, so will investment.
Then biggest gap I see is expected if you look at past trends like mobile and the internet: In the first wave of new tech there's a lot of trying to do the old things in the new way, which often fails or gives incremental improvements at best.
This is why the 'new' companies seem to be doing the best. I've been shocked at so many new AI startups generating millions in revenue so quickly (billions with OpenAI, but that's a special case). It's because they're not shackled to past products, business models, etc.
However, there are plenty of enterprise companies trying to integrate AI into existing workflows and failing miserably. Just like when they tried to retrofit factories with electricity. It's not just plug and play in most cases, you need new workflows, etc. That will take years and there will be plenty more failures.
The level of investment is staggering though, and might we see a crash at some point? Maybe, but likely not for a while since there's still so much white space. The hardest thing with new technologies like this is not to confuse the limits of our imagination with the limits of reality (and that goes both ways).
[0]: https://graphite.io/five-percent/ai-now-writes-as-many-onlin...