Always invest in the spades never the gold mine
That’s why Nvidia is making bank right now
And AMD won the console wars
Chinese tech leader wants west to slow down their progress on AI
AI companies specializing in spreading bullshit all across the internet have a bright future however
Yeah, but the 0.1% remaining will take over the world.
Does anyone remember the era when there were a million search engines? Google didn’t spawn alone.
Same with Amazon. You think nobody else tried to make an online store in the 90s? Lol.
People are trying to vindicate their dislike of AI, pointing to trends like this as if it were supporting evidence. But saying “AI is going to be a big flop because 99% of companies today will end up failing” is as stupid as saying “online shopping will never work because 99% of online stores will close by the year 2010”
No shit.
Like all new technologies, there is a time when bunches of companies jump on the band wagon to get in on the action. You can see it all throughout the history of the industrial revolution.
They mostly know that there will come a great weeding out of those that can’t handle the technology or just fail from poor management. But they are betting they will be among the 1% that wins the race and remain to dominate the market.
The rest will just bide their time until the next Big Thing comes along. And the process starts over again.
So, I have clients that are actively using AI on a daily basis and LOVE it. It is however a very narrow subset. Also, I’m pretty sure that a LARGE amount of Dollars are currently being spent on AI generated political articles.
The web didn’t die after the dot com bubble burst. The AI bubble will burst, but a smaller niche of companies will continue to exist.
Yeah, but thanks to the glory of corporateworld, all the people involved in making these decisions will be in a higher position at a different company by the time the consequences come knocking.
You definitely will not regret spending billions of dollars on GPUs and electricity bills.
I’ll be gone, you’ll be gone.
Maybe it’s like the dotcom bubble: there is genuinely useful tech that has recently emerged, but too many companies are trying to jump on the bandwagon.
LLMs do seem genuinely useful to me, but of course they have limitations.
We need to stop viewing it as artificial intelligence. The parts that are worth money are just more advanced versions of machine learning.
Being able to assimilate a few dozen textbooks and pass a bar exam is a neat parlor trick, but it is still just a parlor trick.
Unfortunately probably the biggest thing to come out of it will be the marketing aspect. If they spend enough money to train small models on our wants and likes it will give them tremendous amounts of return.
The key to using it in a financially successful manner is finding problems that fit the bill. Training costs are fairly high, quality content generation is also rather expensive. There are sticky problems around training it from non-free data. Whatever you’re going to use it for either needs to have a significant enough advantage to make the cost of training /data worth it.
I still think we’re eventually going to see education rise. The existing tools for small content generation adobe’s use of it to fill in small areas is leaps and bounds better than the old content aware patches. We’ve been using it for ages for speech recognition and speech generation. From there it’s relatively good at helper roles. Minor application development, copy editing, maybe some VFX generation eventually. Things where you still need a talented individual to oversee it but it can help lessen the workload.
There are lots of places where it’s being used where I think it’s a particularly poor fit. AI help desk chatbots, IVR scenarios, It says brain dead as the original phone trees and flow charts that we’ve been following for decades.
If GPT4o is still not what you would call AI, then what is? You can have conversations with it, the Turing test is completely irrelevant all of the sudden.
Hasn’t the Turing Test been irrelevant for a while now? Even before the new AI boom?
Artificial intelligence is a moving target. Every time a goal gets reached, they just move the goalposts, because “well, obviously this isn’t real intelligence”.
It’s a massive text predictor. It doesn’t solve problems, it applies patterns based on correlations it picked up during training. If someone talked about your topic online, it has been trained on those conversations. If a topic has two sides that don’t agree, chat gpt might respond in a way that is biased towards one side or the other and you can easily get it to “switch” to the other side with follow up prompts.
For what would be considered AI, think of the star trek computer or Data. The Star Trek computer could create simulations of warp core behaviour to push frontiers of knowledge or characters smart enough to defeat its own safeties (frankly, the computer was such a deus ex machina kinda thing that it was hard to suspend disbelief at times, like why did they even have humans doing the problem solving with computers that capable?). Data wouldn’t get confused about whether any counties in Africa start with K.
I don’t think the Turing test is an effective means of determining intelligence anyways. It came from a time when a conversational computer was barely thinkable. But I wouldn’t even say chat gpt is there yet, since you can tell if you ask it the right things. It is very useful, don’t get me wrong, like a very powerful search engine. But it’s not intelligent.
What of what you say does not apply to humans? They apply patterns of behavior in response to some input. Picked up by learning them. Including people talking online. They are always biased on some way. Some will acknowledge their bias and change it if you give them context.
GPT can literally create simulations. I have used it to do exactly that, specifically for 2D heat conducting with coupled mass transport and reaction kinetics.
Yeah, it does do some very human-like things, but it’s still missing some important parts.
It’s kinda like using a textbook for problem solving. It’s great at helping you solve instances of problems that have already been solved, but you won’t likely find the next big advancement in that field in a textbook.
Newton realized masses attracted each other, and through experimentation, came up with his laws of classical physics.
Einstein took the idea that the speed of light always seems to be the same despite relative motion to come up with special relativity, then realized that space-time itself was a physical thing that could be interacted with rather than just a medium, plus came up with field equations that were used to predict things like black holes before anyone had any kind of notion that they were real things.
Chat gpt is incapable of things like that. And sure, many humans never do anything like that, some might not even be capable even if they were motivated and had the right supports to try. But many humans do solve problems that they’ve never seen before. There’s big names in academia but so many more that don’t get famous but still push the boundaries of human knowledge, creatively solving problems and answering questions every day.
I wouldn’t be surprised if an LLM is a piece of general AI if or when it comes, but there will be other parts that are currently missing. We don’t even know what consciousness is, let alone if any of our hardware is capable of creating/hosting one.
I listened to a podcast (This American Life, IIRC), where some researchers were talking about their efforts to determine whether or not AI could reason. One test they did was asking it to stack a random set of items (one it wouldn’t have come across in any data set, plank of wood, 12 eggs, a book, a bottle, and a nail. . .probably some other things too) in a stable way. With chat gpt 3, it basically just (as you would expect from a pure text predictor) said to put one object on top of another, no way would it be stable.
However, with gpt 4, it basically said to put the wood down, and place the eggs in a 3 x 4 grid with the book on top (to stop them from rolling away), and then with the bottle on top of that, with the nail (even noting you have to put the head side down because you couldn’t make it stable with the point down). It was certainly something that could work, and it was a novel solution.
Now I’m not saying this proves it can think, but I think this “well it’s just a text predictor” kind of hand-waves away the question. It also begs the question, and based on how often I hear people parroting the same exact arguments against AI thinking, I wonder how much we are simply just “text predictors.”
The sheer size of it and it’s training data makes it hard to really say what it’s doing. Like for an object that it wouldn’t have come across in it’s training data, a) how could they tell it was truly a new thing that had never been discussed anywhere on the internet where the training could have consumed it, and b) that any description provided for it didn’t map it to another object that would behave similarly when stacking.
Stacking things isn’t a novel problem. The internet will have many examples of people talking about stacking (including this one here, eventually). The put the flat part down for the nail could have been a direct quote, even. Putting a plank of wood at the bottom would be pretty common, and even the eggs and book thing has probably been discussed before.
I mean, I can’t dismiss that it isn’t doing something more complex, but examples like that don’t convince me that it is. It is capable of very impressive things, and even if it needs to regurgitate every answer it gives, few problems we want to solve day to day are truly novel, so regurgitating previous discussions plus a massive set of associations means that it can map a pretty large problem space to a large solution space with high accuracy.
I’m having trouble thinking of ways to even determine if it can really problem solve that won’t accidentally map to some similar discussion among nerds that like to go into incredible detail and are willing to speculate in any direction just for the sake of enjoying a thought experiment.
Like even known or suspected unsolvable problems have been discussed to greater levels of detail than I’ve likely considered them, so even asking it to do its best trying to solve the traveling salesman problem in polynomial time would likely impress me because computer science students and alums much smarter than I am have discussed it at length.
I can write a program that just replies “yes” to everything you say and you can have a conversation with that. Is that program AI?
“AI isn’t really AI and no one ever thought that AI was actually AI so it doesn’t matter if we call it AI” is the funniest level of tech bro cope these days.
AI has been the name of the field for 70 years at this point, it isn’t something Sam Altman came up with as a marketing wheeze.
Magic Eightball
We’re hitting logarithmic scaling with the model trainings. GPT-5 is going to cost 10x more than GPT-4 to train, but are people going to pay $200 / month for the gpt-5 subscription?
Is it necessary to pay more, or is it enough to just pay for more time? If the product is good, it will be used.
But it would use less energy afterwards? At least that was claimed with the 4o model for example.
4o is also not really much better than 4, they likely just optimized it among others by reducing the model size. IME the “intelligence” has somewhat degraded over time. Also bigger Model (which in tha past was the deciding factor for better intelligence) needs more energy, and GPT5 will likely be much bigger than 4 unless they somehow make a breakthrough with the training/optimization of the model…
4o is optimization of the model evaluation phase. The loss of intelligence is due to the addition of more and more safeguards and constraints by the use of adjunct models doing fine turning, or just rules that limit whole classes of responses.
Businesses might pay big money for LLMs to do specific tasks. And if chip makers invest more in NPUs then maybe LLMs will become cheaper to train. But I am just speculating because I don’t have any special knowledge of this area whatsoever.
I am old enough to remember when the CEO of Nortel Networks got crucified by Wall Street for saying in a press conference that the telecom/internet/carrier boom was a bubble, and the fundamentals weren’t there (who is going to pay for long distance anymore when calls are free over the internet? where are the carriers-- Nortel’s customers-- going to get their income from?). And 4 years later Nortel ceased to exist. Cisco crashed too, though had enough TCP/IP router biz and enterprise sales to keep them alive even until today.
This all reminds me of the late 1990s internet bubble rather than the more recent crypto bubble. We’ll all still be using ML models for all kinds of things more or less forever from now on, but it won’t be this idiotic hype cycle and overvaluation anymore after the crash.
Shit, crypto isn’t going anywhere either, it’s a permanent fixture now, Wall Street bought into it and you can buy crypto ETFs from your stockbroker. We just don’t have to listen to hype about it anymore.
Crypto is still just as awful as it ever was IMO. Still plenty of assholes
gamblinginvesting in crypto.Well put.
Soon, it won’t be this idiotic hype cycle, but it’ll be some other idiotic hype cycle. Short term investors love hype cycles.
We just don’t have to listen to the hype about it anymore.
True, it’s now in most circles just been mixed in as a commodity to trade on. Though I wish everyone would get that. There’s still plenty of idiots with .eth usernames who think there’s some new boon to be made. The only “apps” built on crypto networks were and are purely for trading crypto, I’ve never seen any real tangible benefit to society come out of it. It’s still used plenty for money laundering, but regulators are (slowly) catching up. And it’s still by far the easiest way to demonstrate what happens to unregulated markets.
Crypto has been turned into gold by wallstreet, they bought up enough of it to jot be completely exposed, it’s supply is extremely limited and will run out. Putting your money into it is no different than putting it into gold, you might catch a good moment and buy in low and get some return, but most wont.
Putting your money into it is no different than putting it into gold
Sorry kiddo, putting your money into crypto is very, very different to putting it into gold.
Once the apocalypse comes, you can at least use a gold brick to brain the zombies, whereas your crypto will vanish along with the Internet and electrical grid.
Well yeah, it’s easier to steal it if you click on a link you shouldnt.
The supply is absolutely more like unlimited lol.
Not enough btc? Make lite coin! Etc etc etc
No one cares about lite coin though which defeats the purpose.
Etherium bla bla bla there are tons of them thousands. There us no shortage lol.
Silver, bronze etc, are you being dense on purpose? Though your seeming affinity for crypto implies you are simply dense.
Lol touch grass cryptobro.
I think you are the cryptobro, how the fuck is likening crypto to a known stupid investment like gold make me a crypto bro you absolute muffin.
That’s like saying US Dollars are Unlimited because you can always buy Zimbabwe Dollars…
Good metaphor.
Wow, a CEO who doesn’t buy into the hype? That’s astonishing.
I, for one, cannot wait for the bubble to burst so we can get back to some sense of sanity.
Edit>> Though if Baidu is investing in AI like all the rest, then maybe they just think they’ll be immune — in which case I’m sad again that I haven’t yet come across a CEO who calls bullshit on this nonsense.
AI will have its uses, and it has practical use cases such as helping people to walk or to speak or to translate in real time, etc. But we’re decades away from what all these CEOs seem to think they’re going to cash in on now. And it’ll be fun on some level watching them all be wrong.
Edit>> Though if Baidu is investing in AI like all the rest, then maybe they just think they’ll be immune — in which case I’m sad again that I haven’t yet come across a CEO who calls bullshit on this nonsense.
They may just have kept their AI investments responsible—that is, not put more money into it than they can afford to lose. Keep in mind, Baidu is the Chinese equivalent of Google. They have a large, diversified business with many income streams. I expect they’ll still be around after the bubble bursts.
10 to 30? Yeah I think it might be a lot longer than that.
Somehow everyone keeps glossing over the fact that you have to have enormous amounts of highly curated data to feed the trainer in order to develop a model.
Curating data for general purposes is incredibly difficult. The big medical research universities have been working on it for at least a decade, and the tools they have developed, while cool, are only useful as tools too a doctor that has learned how to use them. They can speed diagnostics up, they can improve patient outcome. But they cannot replace anything in the medical setting.
The AI we have is like fancy signal processing at best
AI in health and medtech has been around and in the field for ages. However, two persistent challenges make roll out slow-- and they’re not going anywhere because of the stakes at hand.
The first is just straight regulatory. Regulators don’t have a very good or very consistent working framework to apply to to these technologies, but that’s in part due to how vast the field is in terms of application. The second is somewhat related to the first but really is also very market driven, and that is the issue of explainability of outputs. Regulators generally want it of course, but also customers (i.e., doctors) don’t just want predictions/detections, but want and need to understand why a model “thinks” what it does. Doing that in a way that does not itself require significant training in the data and computer science underlying the particular model and architecture is often pretty damned hard.
I think it’s an enormous oversimplification to say modern AI is just “fancy signal processing” unless all inference, including that done by humans, is also just signal processing. Modern AI applies rules it is given, explicitly or by virtue of complex pattern identification, to inputs to produce outputs according to those “given” rules. Now, what no current AI can really do is synthesize new rules uncoupled from the act of pattern matching. Effectively, a priori reasoning is still out of scope for the most part, but the reality is that that simply is not necessary for an enormous portion of the value proposition of “AI” to be realized.
Not an expert so I might be wrong, but as far as I understand it, those specialised tools you describe are not even AI. It is all machine learning. Maybe to the end user it doesn’t matter, but people have this idea of an intelligent machine when its more like brute force information feeding into a model system.
Don’t say AI when you mean AGI.
By definition AI (artificial intelligence) is any algorithm by which a computer system automatically adapts to and learns from its input. That definition also covers conventional algorithms that aren’t even based on neural nets. Machine learning is a subset of that.
AGI (artifical general intelligence) is the thing you see in movies, people project into their LLM responses and what’s driving this bubble. It is the final goal, and means a system being able to perform everything a human can on at least human level. Pretty much all the actual experts agree we’re a far shot from such a system.
It may be too late on this front, but don’t say AI when there isn’t any I to it.
Of course it could be successfully argued that humans (or at least a large amount of them) are also missing the I, and are just spitting out the words that are expected of them based on the words that have been ingrained in them.
AI as a field of computer science is mostly about pushing computers to do things they weren’t good at before. Recognizing colored blocks in an image was AI until someone figured out a good way to do it. Playing chess at grandmaster levels was AI until someone figured out how to do it.
Along the way, it created a lot of really important tools. Things like optimizing compilers, virtual memory, and runtime environments. The way computers work today was built off of a lot of things out of the old MIT CSAIL labs. Saying “there’s no I to this AI” is an insult to their work.
Recognizing colored blocks in an image was AI until someone figured out a good way to do it. Playing chess at grandmaster levels was AI until someone figured out how to do it.
You make it sound like these systems stopped being AI the moment they actually succeeded at what they were designed to do. When you play chess against a computer it’s AI you’re playing against.
That’s exactly what I’m getting at. AI is about pushing the boundary. Once the boundary is crossed, it’s not AI anymore.
Those chess engines don’t play like human players. If you were to look at how they determine things, you might conclude they’re not intelligent at all by the same metrics that you’re dismissing ChatGPT. But at this point, they are almost impossible for humans to beat.
I’m not the person you originally replied to. At no point have I dismissed ChatGPT.
I disagree with your logic about the definition of AI. Intelligence is the ability to acquire, understand, and use knowledge. A chess-playing AI can see the board, understand the ramifications of each move, and respond to how the pieces are moved. That makes it intelligent - narrowly so, but intelligent nonetheless. And since it’s artificial too, it fits the definition of AI.
This is not up to you or me : AI is an area of expertise / a scientific field with a precise definition. Large, but well defined.
Intelligence: The ability to acquire, understand, and use knowledge.
A self-driving car is able to observe its surroundings, identify objects and change its behaviour accordingly. Thus a self-driving car is intelligent. What’s driving such car? AI.
You’re free to disagree with how other people define words but then don’t take part in their discussions expecting everyone to agree with your definiton.
LLM’s are not the only type of AI out there. ChatGPT appeared seemingly out of nowhere. Whose to say the next AI system wont do that as well?
ChatGPT did not appear out of nowhere.
ChatGPT is an LLM that is a generative pre-trained model using a nueral network.
Aka: it’s a chat bot that creates it’s responses based on an insane amount of text data. LLMs trace back to the 90s, and I learned about them in college in the late 2000s-2010s. Natural Language Processing was a big contributor, and Google introduced some powerful nueral network tech in 2014-2017.
The reason they “appeared out of nowhere” to the common man is merely marketing.
You’re misquoting me. I haven’t claimed LLMs appeared out of nowhere.
You said ChatGPT appeared out of nowhere. ChatGPT is basically Eliza with an LLM.
Those are not my words and you know it. You’re misquoting me.
LLM’s are not the only type of AI out there. ChatGPT appeared seemingly out of nowhere. Whose to say the next AI system wont do that as well?
I’m not sure what I’m misquoting. A large language model is not AI, a large language model is a non-human readable function used by a generative AI algorithm.
Simply put, ChatGPT did not appear out of nowhere.
ChatGPT did not appear out of nowhere
I agree.
The key word there is seemingly. The technology itself had existed for a long time, but it wasn’t until the massive leap OpenAI made with it that it actually became popular. Before ChatGPT, 99% of people had never heard of LLMs, and now everyone has. That’s what I mean when I say it appeared seemingly out of nowhere - it took the masses by surprise. There’s no reason to assume another company working on a different approach to AI won’t make a similar massive breakthrough, giving us AI far more powerful than LLMs and taking everyone by surprise, despite the base technology having existed for a long time.
A large language model is not AI
It is AI though - a subset of generative AI to be specific, but it still falls under the AI category.
Anything can happen. We can discover time travel tomorrow. The economy cannot run on wishful thinking.
It can! For a while. Isn’t that the nature of speculation and speculative bubbles? Sure, they may pop some day, because we don’t know for sure what’s a bubble and what is a promising market disruption. But a bunch of people make a bunch of money until then, and that’s all that matters.
The uncertainty of it is exactly why it shouldn’t suck up as much capital and resources as it is doing.
Shouldn’t, definitely. But for a while, it will keep running, because that’s how a lot of speculative investment works.
I agree, and the problem is finance capitalism itself. But then it becomes an ideological argument.
The argument could be made economically rather than ideologically.
Capitalism has a failure mode where too much capital gets concentrated into too few hands, depressing the flow of money moving through the economy.
But Capitalists start crying “Socialism!” as soon as you start talking about anti-trust.
Tulips all the way down…
No bubble has deserved to pop as much as AI deserves to
Maybe real estate?
Blockchain and crypto were worse. „AI” has some actual use even if it’s way overblown.
I mean, block chain does have some actual uses, definitely more niche than LLMs though.
I’m glad you didn’t say NFTs because my Bored Ape will regain and triple its value any day now!
Bro the GME short squeeze is going to hit any day now. We’re going to be millionaires bro, you just wait
Die almond hands, bro! We’re all gonna make it, bro!!! Trust the code, bro!!!
Creating a specialized neural net to perform a specific function is cool. Slapping GPT into customer support because you like money is horse shit and I hope your company collapses. But yeah you’re right. Blockchain was a solution with basically no problems to fix. Neural nets are a tool that can do a ton of things, but everyone is content to use them as a hammer.
Yes! “AI” defined as only LLMs and the party trick applications is a bubble. AI in general has been around for decades and will only continue to grow.
Crypto has a legitimate value, you can buy drugs with it.
Honestly kinda miss when the drugs I did were illegal. I used to buy weed from this online seller that was really into designer drugs. The amount of time I used to spend on Erowid just to figure out wtf I was about to take.
I’m not even understanding what AI is at this point because there’s no delineation between moderately sophisticated algorithms and things that are orders of magnitude more complex.
I mean, if something like multisampling came out today we’d all know how it’d be marketed
AI is a ridiculous broad term these days. Everybody had been slapping the label on anything. It’s kinda like saying “transportation” and it means anything between babies crawling up to wrap drive and teleportation.
Because most of them are AI
It’s just once AI becomes useful (and not magical), we tend to stop calling it AI unless AI gets more VC money.
It’s called the AI effect
Technically speaking how I differentiate it is:
- clever algorithm is a good heuristic
- statistics on steroids is machine learning
- using a transformer model is AI (for now)
The AI buzzword means machine learning. You give it a massive dataset and it identifies correlations.
Regular hand-coded AI is mostly simple state machines.
Yes. But companies bought into AI way more than they bought into crypto though, in many outlandish and stupid ways. And many AI companies sell it in ways they shouldn’t.
As a counterpoint: https://en.wikipedia.org/wiki/Long_Blockchain_Corp.
Truly the hardest and most weird buy-in of crypto that happened.
Oh yeah. Kodak too. Gamestop, right? There were a bunch of others.
Blockchain has many valuable uses. A distributed zero trust ledger is useful. Sadly the finance scammers and the digital beanie baby collectors attracted all the marketing money.
And yet, every single company that has ever tried to implement a distributed zero trust ledger into their products and processes has inevitably ditched the idea after releasing that it does not, in fact, provide any useful benefit.
It is exceptionally useful for the auditing of damn near everything in digital space, as long as shared resources and 3rd parties have access to the blockchain … which is probably the major reason corporations and politicians don’t want anything to do with it.
It’d be a lot harder to hide crimes, fraud, grey business dealings, bribery and illegal donations, sanction violations, secret police slush funds, etc, etc if every event in the entire financial system and supply chain was logged and cryptographically verifiable.
EDIT: NOTE I’m not talking about everyones transactions being in a public ledger (bad). Only enhancing the current system between businesses and orgs so it’s exceptionally difficult for any of them to falsify data without the others knowing, as well as having near instant visibility and analytics of the entire market (great for regulators, academics, etc).
A supply-chain wide blockchain could enable individuals to view every raw material that went into every product they consume, down to the location, date — even the exact time in many cases — each was mined, refined, harvested, transported, picked, traded, etc. in a way that no individual corp could hide or falsify dramatically. Each corp and individuals true (embodied energy consumption would be visible to every buyer; developed world politicians and corporations couldn’t simply blame China and other developing countries for their own consumption.
The reason major businesses haven’t bothered using distributed blockchains for auditing is because they fundamentally do not actually help in any way with auditing.
At the end of the day, the blockchain is just a ledger. At some point a person has to enter the information into that ledger.
Now, hear me out here, because this is going to be some totally out there craziness that is going to blow your mind… What happens if that person lies?
Like, you’ve built your huge, complicated system to track every banana you buy from the farm to the grocery store… But what happens if the shipper just sends you a different crate of bananas with the wrong label on them? How does your system solve that? What happens if the company growing your bananas claims to use only ethical practices but in reality their workers are effectively slaves? How does a blockchain help fix that?
The data in a system is only as good as your ability to verify it. Verifying the integrity of the data within systems was largely a solved problem long before distributed blockchains came along, and was rarely if ever the primary avenue for fraud. It’s the human components of these systems where fraud can most easily occur. And distributed blockchains do absolutely nothing to solve that.
Counterpoint, having a currency where every token is tied into its own transaction history might be unpopular with large businesses for other reasons. Like maybe they don’t want to be that transparent or accountable. The FBI have made public statements about how much easier it is to track criminals who used Crypto.
Your opinion seems to contradict reality.
This is a very poorly considered argument. Even if we suppose that everything you’ve said is true, the existence of a second plausible explanation doesn’t invalidate the first. You’ve not actually offered any reason why any of what I said is wrong, you just said “X is possible, therefore Y cannot be true.”
Also, I want to note that this particular digression wasn’t about cryptocurrency at all. The point I was responding to was a claim that blockchains had uses other than as currencies. So you really might want to step back a bit and consider what you think is being discussed here, and what you’re actually trying to say.
The idea has merit, in theory – but in practice, in the vast majority of cases, having a trusted regulator managing the system, who can proactively step in to block or unwind suspicious activity, turns out to be vastly preferable to the “code is law” status quo of most blockchain implementations. Not to mention most potential applications really need a mechanism for transactions to clear in seconds, rather than minutes to days, and it’d be preferable if they didn’t need to boil the oceans dry in the process of doing so.
If I was really reaching, I could maybe imagine a valid use case for say, a hypothetical, federated open source game that needed to have a trusted way for every node to validate the creation and trading of loot and items, that could serve as a layer of protection against cheating nodes duping items, for instance. But that’s insanely niche, and for nearly every other use case a database held by a trusted entity is faster, simpler, safer, more efficient, and easier to manage.
Try Venice Ai, free to use, won’t try to censor your topics. Still just a chat bot though (although I think it does image generation too).
I’m sorry, what about their comment made you think they were asking for reccomendations?
The part where they were saying they don’t like the current AIs they know about. Showing disapproval of the trend.
Censoring topics is the least of the issues with the AI bubble.
No it’s a huge one, because it’s the most likely application of AI, AI site moderation will be the start of AI digital policing a field which risks growing larger and larger until it manifests as actual legal policing.
As a major locally-hosted AI proponent, aka a kind of AI fan, absolutely. I’d wager it’s even worse than crypto, and I hate crypto.
What I’m kinda hoping happens is that bitnet takes off in the next few months/years, and that running a very smart model on a phone or desktop takes milliwatts… Who’s gonna buy into Sam Altman $7 trillion cloud scheme to burn the Earth when anyone can run models offline on their phones, instead of hitting APIs running on multi-kilowatt servers?
And ironically it may be a Chinese company like Alibaba that pops the bubble, lol.
If bitnet takes off, that’s very good news for everyone.
The problem isn’t AI, it’s AI that’s so intensive to host that only corporations with big datacenters can do it.
The fuck is bitnet
https://www.microsoft.com/en-us/research/publication/bitnet-scaling-1-bit-transformers-for-large-language-models/ use 1 bit instead of 8 or 16, yay performance gainz
So will the return of the flag conclude the adventures of ressource usage in computers?
And they will ALL deserve it.
Aw, only 99%?
