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I think when the hype dies down in a few years, we’ll settle into a couple of useful applications for ML/AI, and a lot will be just thrown out.
I have no idea what will be kept and what will be tossed but I’m betting there will be more tossed than kept.
Maybe in some places, but I just found this:
A Market place, where people can generate their ideas of jewellery and order them after. Makes life of goldsmiths and customers way more easy. I do not think aI will leave this project, for example.
I recently saw a video of AI designing an engine, and then simulating all the toolpaths to be able to export the G code for a CNC machine. I don’t know how much of what I saw is smoke and mirrors, but even if that is a stretch goal it is quite significant.
An entire engine? That sounds like a marketing plot. But if you take smaller chunks let’s say the shape of a combustion chamber or the shape of a intake or exhaust manifold. It’s going to take white noise and just start pattern matching and monkeys on typewriter style start churning out horrible pieces through a simulator until it finds something that tests out as a viable component. It has a pretty good chance of turning out individual pieces that are either cheaper or more efficient than what we’ve dreamed up.
AI is like the calculator for the mathematician. A very useful tool that allows you to be more efficient but is completely useless without someone capable of handling it.
and then simulating all the toolpaths to be able to export the G code for a CNC machine. I don’t know how much of what I saw is smoke and mirrors, but even if that is a stretch goal it is quite significant.
<sarcasm> Damn, I ascended to become an AI and I didn’t realise it. </sarcasm>
Snort might actually be a good real world application that stands to benefit from ML, so for security there’s some sort of hopefulness.
AI is very useful in medical sectors, if coupled with human intervention. The very tedious works of radiologists to rule out normal imaging and its variants (which accounts for over 80% cases) can be automated with AI. Many of the common presenting symptoms can be well guided to diagnosis with some meticulous use of AI tools. Some BCI such as bioprosthosis can also be immensely benefitted with AI.
The key is its work must be monitored with clinicians. As much valuable the private information of patients is, blindly feeding everything to an AI can have disastrous consequences.
Yeah, he’s right. AI is mostly used by corps to enshittificate their products for just extra profit
Oh please. Wait until they release double-sided, double-density 128bit AI quantum blockchain that runs on premises/in the cloud edge hybrid.
It will have revolutionary rock star synergy
The blue sky, out of the box thinking will disrupt the glass ceiling.
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I feel like they snuck in a little square of reasonable terms with
Best practices Optimization Industry standard Authenticate
But now that I’ve typed it, I’m scared that optimization and authenticate have gross business-speak definitions I just don’t know about yet.
I agree with Mr. Torvalds
That’s my usual feeling with Linus takes.
Well, I agree, but he could be nicer about it.
Seems generous, might be more like 5% reality.
it is basically like how self improvement folks are using quantum
and that 10% isnt really real, just a gabbier dr.sbaitso
Idk man, my doctors seem pretty fucking impressed with AI’s capabilities to make diagnoses by analyzing images like MRI’s.
then you are a fortunate rarity. most posts about the tech complain about ai just rearranging what it is told and regurgitating it with added spice
I think that is because most people are only aware of its use as what are, effectively, chat bots. Which, while the most widely used application, is one of its least useful. Medical image analysis is one of the big places it is making strides in. I am told, by a friend in aerospace, that it is showing massive potential for a variety of engineering uses. His firm has been working on using it to design, or modify, things like hulls, air frames, etc. Industrial uses, such as these, are showing a lot of promise, it seems.
thats good. be nice if all the current ai developers would aim that way
Decided to say something popular after his snafu, I see.
Ai bad gets them every time.
He is correct. It is mostly people cashing out on stuff that isn’t there.
As a fervent AI enthusiast, I disagree.
…I’d say it’s 97% hype and marketing.
It’s crazy how much fud is flying around, and legitimately buries good open research. It’s also crazy what these giant corporations are explicitly saying what they’re going to do, and that anyone buys it. TSMC’s allegedly calling Sam Altman a ‘podcast bro’ is spot on, and I’d add “manipulative vampire” to that.
Talk to any long-time resident of localllama and similar “local” AI communities who actually dig into this stuff, and you’ll find immense skepticism, not the crypto-like AI bros like you find on linkedin, twitter and such and blot everything out.
It’s selling the future, but nobody knows if we can actually get there
The first part is true … no one cares about the second part of your statement.
It’s selling an anticompetitive dystopia. It’s selling a Facebook monopoly vs selling the Fediverse.
We dont need 7 trillion dollars of datacenters burning the Earth, we need collaborative, open source innovation.
Agreed that’s why it’s so dangerous. These tech bros are going to do damage with their shitty products. It seems like it’s Altman’s goal, honestly.
He wants money/power, and he is getting it. The rest of the AI field will forever be haunted by his greed.
Seriously, I’d love to be enthusiastic about it because it’s genuinely cool what you can do with math.
But the lies that are shoved in our faces are just so fucking much and so fucking egregious that it’s pretty much impossible.
And on top of that LLMs are hugely overshadowing actual interesting approaches for funding.
TSMC are probably making more money than anyone in this goldrush by selling the shovels and picks, so if that’s their opinion, I feel people should listen…
There’s little in the AI business plan other than hurling money at it and hoping job losses ensue.
TSMC doesn’t really have official opinions, they take silicon orders for money and shrug happily. Being neutral is good for business.
Altman’s scheme is just a whole other level of crazy though.
Yep the current iteration is. But should we cross the threshold to full AGI… that’s either gonna be awesome or world ending. Not sure which.
What makes you think there’s a threshold?
Based on what I’ve witnessed so far, people will play with their AGI units for a bit and then put them down to continue scrolling memes.
Which means it is neither awesome, nor world-ending, but just boring/business as usual.
There are people way smarter than me that claim it will be a threshold and would likely grow exponentially after it’s crossed. I guess we won’t know for sure until it happens. I do agree most people get bored easily but if this thing is possible to think for itself without interaction it won’t matter if the humans get bored.
Current LLMs cannot be AGI, no matter how big they are. The fundamental architecture just isn’t right.
You’re absolutely right. LLMs are good at faking language and sometimes not even great at that. Not sure why I got downvoted but oh well. But AGI will be game changing if it happens.
I know nothing about anything, but I unfoundedly believe we’re still very far away from the computing power required for that. I think we still underestimate the power of biological brains.
Very likely. But 4 years ago I would have said we weren’t close to what these LLMs can do now so who knows.
For real. Being a software engineer with basic knowledge in ML, I’m just sick of companies from every industry being so desperate to cling onto the hype train they’re willing to label anything with AI, even if it has little or nothing to do with it, just to boost their stock value. I would be so uncomfortable being an employee having to do this.
For sure, it seems like 90% of ai startups are nothing more than front end wrappers for a gpt instance.
They’re all built on top of OpenAI which is very unprofitable at the moment. Feels like the whole industry is built on a shaky foundation.
Putting the entire fate of your company in a different company (OpenAI) is not a great business move. I guess the successful AI startups will eventually transition to self-hosted models like Llama, if they survive that long.
Most projects I’ve been in contact with are very aware of that fact. That’s why telemetry is so big right now. Everybody is building datasets in the hopes of fine tuning smaller, cheaper models once they have enough good quality data.
My company is realizing that hosting a model which will be private, cost-effective, and performing better than traditional algorithms is like finding a unicorn. Few months back, the top execs were jumping around GenAI like a bunch of kids. Fortunately, the Sr. research head beat some sense into them.
You’re lucky there’s a higher up that could talk down the even higher ups. Though, sometimes it’s not even about the r&d teams.
I saw company wide HR educational emails or courses telling you how to improve you work quality/efficiency, and one of them tells us to “research AI” and learn how to utilize it, talking about how great it is and improved the work efficiency by 30%. Sure, it has its uses, but I won’t go touting how great it is. And with how ChatGPT works, you have to be the biggest idiot in the world to upload all your sensitive stuff to ChatGPT just for it to make a spreadsheet faster. But without these disclaimers in the email, I doubt regular clerical staff knows about this, and it’s extremely dangerous.
What kind of use-cases was it, where you didn’t find suitable local models to work with ? I’ve found that general “chatbot” things are hit and miss but more domain-constrained tasks (such as extracting structured entities from unstructured text) are pretty reliable even on smaller models. I’m not counting my chickens yet as my dataset is still somewhat small but preliminary testing has been very promising in that regard.
What kind of use-cases was it, where you didn’t find suitable local models to work with ?
Any time you ask very domain specific questions; eg “i have collected some soil samples from the mesolithic age near the Amazon basin which have high sulfur and phosphorus content compared to my other samples. What factors could contribute to this distribution?”, both of-the-shelf local models & OpenAI fail.
The main reason is because these models are not trained on highly-specialized domains of text. Sometimes the models start hallucinating and which reduces our trust upon them.
As someone who was working really hard trying to get my company to be able use some classical ML (with very limited amounts of data), with some knowledge on how AI works, and just generally want to do some cool math stuff at work, being asked incessantly to shove AI into any problem that our execs think are “good sells” and be pressured to think about how we can “use AI” was a terrible feel. They now think my work is insufficient and has been tightening the noose on my team.
This. Exactly.
The saddest part is, this is going to cause yet another AI winter. The first few ones were caused by genuine over-enthusiasm but this one is purely fuelled by greed.
The AI ecosystem is flooded, we need a good bubble pop to slow down the massive waste of resources that our current info-remix-based-on-what-you-will-likely-react-positively-to shit-tier AI represents.
I think we should indict Sam Altman on two sets of charges:
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A set of securities fraud charges.
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8 billion counts of criminal reckless endangerment.
He’s out on podcasts constantly saying the OpenAI is near superintelligent AGI and that there’s a good chance that they won’t be able to control it, and that human survival is at risk. How is gambling with human extinction not a massive act of planetary-scale criminal reckless endangerment?
So either he is putting the entire planet at risk, or he is lying through his teeth about how far along OpenAI is. If he’s telling the truth, he’s endangering us all. If he’s lying, then he’s committing securities fraud in an attempt to defraud shareholders. Either way, he should be in prison. I say we indict him for both simultaneously and let the courts sort it out.
“When you’re rich, they let you do it.”
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TSMC’s allegedly calling Sam Altman a ‘podcast bro’ is spot on, and I’d add “manipulative vampire” to that.
What’s the source for that? It sounds hilarious
When Mr. Altman visited TSMC’s headquarters in Taiwan shortly after he started his fund-raising effort, he told its executives that it would take $7 trillion and many years to build 36 semiconductor plants and additional data centers to fulfill his vision, two people briefed on the conversation said. It was his first visit to one of the multibillion-dollar plants.
TSMC’s executives found the idea so absurd that they took to calling Mr. Altman a “podcasting bro,” one of these people said. Adding just a few more chip-making plants, much less 36, was incredibly risky because of the money involved.
I really want to like AI, I’d love to have an intelligent AI assistant or something, but I just struggle to find any uses for it outside of some really niche cases or for basic brainstorming tasks. Otherwise, it just feels like alot of work for very little benefit or results that I can’t even trust or use.
It’s useful.
I keep Qwen 32B loaded on my desktop pretty much whenever its on, as an (unreliable) assistant to analyze or parse big texts, to do quick chores or write scripts, to bounce ideas off of or even as a offline replacement for google translate (though I specifically use aya 32B for that).
It does “feel” different when the LLM is local, as you can manipulate the prompt syntax so easily, hammer it with multiple requests that come back really fast when it seems to get something wrong, not worry about refusals or data leakage and such.
Attractive. You got some pretty solid specs?
Rue the day I cheaped out on RAM. soldered RAMmmm
Soldered is better! It’s sometimes faster, definitely faster if it happens to be lpddr.
But TBH the only thing that really matters his “how much VRAM do you have,” and Qwen 32B slots in at 24GB, or maybe 16GB if the GPU is totally empty and you tune your quantization carefully. And the cheapest way to that (until 2025) is a used MI60, P40 or 3090.
I receive alerts when people are outside my house, using security cameras, Blue Iris, CodeProject AI, Node-RED and Home Assistant, using a Google Coral for local AI. Entirely local - no cloud services apart from Google’s notification system to get notifications to my phone while I’m not home (which most Android apps use). That’s a good use case for AI since it avoids false positives that occur with regular motion detection.
I’ve been curious about google coral, but their memory is so tiny I’m not sure what kinds of models you can run on them
A lot of people use them for the use case I described (object detection for security cameras), using either Blue Iris or Frigate. They work pretty well for that use case.
Wake word detection is a good use case too (eg if you’re making your own smart assistant).
The Coral site lists a few use cases.
Ya, it’s like machine learning but better. That’s about it IMO.
Edit: As I have to spell it out: as opposed to (machine learning with) neural networks.
I mean… it is machine learning.
It’s also neural networks, and probably some other CS structures.
AI is a category, and even specific implementations tend to use multiple techniques.
Well there is a very specific architecture “rut” the LLMs people use have fallen into, and even small attempts to break out (like with Jamba) don’t seem to get much interest, unfortunately.
Sure, but LLMs aren’t the only AI being used, nor will they eliminate the other forms of AI. As people see issues with the big LLMs, development focus will change to adopt other approaches.
There is real risk that the hype cycle around LLMs will smother other research in the cradle when the bubble pops.
The hyperscalers are dumping tens of billions of dollars into infrastructure investment every single quarter right now on the promise of LLMs. If LLMs don’t turn into something with a tangible ROI, the term AI will become every bit as radioactive to investors in the future as it is lucrative right now.
Viable paths of research will become much harder to fund if investors get burned because the business model they’re funding right now doesn’t solidify beyond “trust us bro.”
the term AI will become every bit as radioactive to investors in the future as it is lucrative right now.
Well you say that, but somehow crypto is still around despite most schemes being (IMO) a much more explicit scam. We have politicans supporting it.
Sure, but those are largely the big tech companies you’re talking about, and research tends to come from universities and private orgs. That funding hasn’t stopped, it just doesn’t get the headlines like massive investments into LLMs currently do. The market goes in cycles, and once it finds something new and promising, it’ll dump money into it until the next hot thing comes along.
There will be massive market consequences if AI fails to deliver on its promises (and I think it will, because the promises are ridiculous), and we get those every so often. If we look back about 25 years, we saw the same thing w/ the dotcom craze, where anything with a website got obscene amounts of funding, even if they didn’t have a viable business model, and we had a massive crash. But important websites survived that bubble bursting, and the market recovered pretty quickly and within a decade we had yet another massive market correction due to another bubble (the housing market, mostly due to corruption in the financial sector).
That’s how the market goes. I think AI will crash, and I think it’ll likely crash in the next 5 years or so, but the underlying technologies will absolutely be a core part of our day-to-day life in the same way the Internet is after the dotcom burst. It’ll also look quite a bit different IMO than what we’re seeing today, and within 10 years of that crash, we’ll likely be beyond where we were just before the crash, at least in terms of overall market capitalization.
It’s a messy cycle, but it seems to work pretty well in aggregate.
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It is. It’s that plus an important process for living organisms rather than just burning something.
After getting my head around the basics of the way LLMs work I thought “people rely on this for information?”, the model seems ok for tasks like summarisation though
I don’t love it for summarization. If I read a summary, my takeaway may be inaccurate.
Brainstorming is incredible. And revision suggestions. And drafting tedious responses, reformatting, parsing.
In all cases, nothing gets attributed to me unless I read every word and am in a position to verify the output. And I internalize nothing directly, besides philosophy or something. Sure can be an amazing starting point especially compared to a blank page.
It’s good for coding if you train it on your own code base. Not great for writing very complex code since the models tend to hallucinate, but it’s great for common patterns, and straightforward questions specific to your code base that can be answered based on existing code (eg “how do I load a user’s most recent order given their email address?”)
It’s wild when you only know how to use SELECT in SQL, but after a dollar worth of prompting and 10 minutes of your time, you can have a significantly complex query you end up using multiple times a week.
the model seems ok for tasks like summarisation though
That and retrieval and the business use cases so far, but even then only if the results can be wrong somewhat frequently.
That’s probably true about all new technology that VCs throw billions at.
We lived more than a decade of those decisions, when borrowing money was cheap, and VC was investing in startups selling juice machines.
Just chiming in as another guy who works in AI who agrees with this assessment.
But it’s a little bit worrisome that we all seem to think we’re in the 10%.
it’s a little bit worrisome that we all seem to think we’re in the 10%.
A bit like how when you poll drivers on how good they think they are at driving, the vast majority say they’re better than average lol
That’s possible though, if there are some really bad drivers screwing the average.
Edit: it’s probably even true in this case, it just depends on how you define ‘good’. For example if you define it by getting tickets, only 36% of drivers are issued tickets. The average number of tickets issued is > 0 but the majority of drivers aren’t issued tickets, the average is skewed, because most drivers are at 0.
I don’t know how you’d measure driving “goodness”, but I expect the distribution would be something like exponential (there are billions of non-drivers, and only a few rally/stunt drivers). So the average is likely to be higher than the median.
And then people will complain about that saying it’s almost all hype and no substance.
Then that one tech bro will keep insisting that lemmy is being unfair to AI and there are so many good use cases.
No one is denying the 10% use cases, we just don’t think it’s special or needs extra attention since those use cases already had other possible algorithmic solutions.
Tech bros need to realize, even if there are some use cases for AI, there has not been any revolution, stop trying to make it happen and enjoy your new slightly better tool in silence.
Hi! It’s me, the guy you discussed this with the other day! The guy that said Lemmy is full of AI wet blankets.
I am 100% with Linus AND would say the 10% good use cases can be transformative.
Since there isn’t any room for nuance on the Internet, my comment seemed to ruffle feathers. There are definitely some folks out there that act like ALL AI is worthless and LLMs specifically have no value. I provided a list of use cases that I use pretty frequently where it can add value. (Then folks started picking it apart with strawmen).
I gotta say though this wave of AI tech feels different. It reminds me of the early days of the web/computing in the late 90s early 2000s. Where it’s fun, exciting, and people are doing all sorts of weird,quirky shit with it, and it’s not even close to perfect. It breaks a lot and has limitations but their is something there. There is a lot of promise.
Like I said else where, it ain’t replacing humans any time soon, we won’t have AGI for decades, and it’s not solving world hunger. That’s all hype bro bullshit. But there is actual value here.
Hi! It’s me, the guy you discussed this with the other day! The guy that said Lemmy is full of AI wet blankets.
Omg you found me in another post. I’m not even mad; I do like how passionate you are about things.
Since there isn’t any room for nuance on the Internet, my comment seemed to ruffle feathers. There are definitely some folks out there that act like ALL AI is worthless and LLMs specifically have no value. I provided a list of use cases that I use pretty frequently where it can add value. (Then folks started picking it apart with strawmen).
What you’re talking about is polarization and yeah, it’s a big issue.
This is a good example, I never did any strawman nor disagree with the fact that it can be useful in some shape or form. I was trying to say its value is much much lower than what people claim to be.
But that’s the issue with polarization, me saying there is much less value can be interpreted as absolute zero, and I apologize for contributing to the polarization.
There was a great article in the Journal of Irreproducible Results years ago about the development of Artificial Stupidity (AS). I always do a mental translation to AS when ever I see AI.
I make DNNs (deep neural networks), the current trend in artificial intelligence modeling, for a living.
Much of my ancillary work consists of deflating/tempering the C-suite’s hype and expectations of what “AI” solutions can solve or completely automate.
DNN algorithms can be powerful tools and muses in scientific endeavors, engineering, creativity and innovation. They aren’t full replacements for the power of the human mind.
I can safely say that many, if not most, of my peers in DNN programming and data science are humble in our approach to developing these systems for deployment.
If anything, studying this field has given me an even more profound respect for the billions of years of evolution required to display the power and subtleties of intelligence as we narrowly understand it in an anthropological, neuro-scientific, and/or historical framework(s).
